Landscape Analysis Research Articles

Natural Flavonoids as Primary Amoebic Meningoencephalitis Inhibitor: Virtual Screening, Molecular Docking, MD Simulation, MMPBSA, Density Functional Theory, Principal Component, and Gibbs Free Energy Landscape Analyses

ABSTRACTFlavonoids have been showing diversified bioactivities. Primary amoebic meningoencephalitis is a brain inflammation caused by Naegleria fowleri brain eating amoeba. In this manuscript, we selected 93 flavonoids by extensive literature survey and 83 flavonoids passed drug likeness parameter. Selected flavonoids were molecular docked against primary amoebic meningoencephalitis N. fowleri CYP51 receptor considering voriconazole as standard. Beta naphthoflavone, abyssinone I, and abyssinone III showed maximum docking scores of −10.9 kcal/mol, −10.7 kcal/mol, and −10.6 kcal/mol, respectively, whereas voriconazole showed docking scores of −7.6 kcal/mol. Molecular dynamic simulation data showed that RMSD values attained almost a static value during the simulation, and all nearest interacting amino acid residues were fluctuated within limit. Molecular Mechanics Poisson‐Boltzmann Surface Area (MMPBSA) data of beta naphthoflavone abyssinone I, abyssinone III, and voriconazole showed free binding energies of −82.755 kJ/mol, −1924.193 kJ/mol, −1890.335 kJ/mol, and −540.141 kJ/mol, respectively. Frontier molecular orbital (FMO) analysis showed that abyssinone III was the chemically reactive molecule and beta naphthoflavone showed maximum electrophilicity. Molecular electrostatic potential (MEP) analysis portrayed possible nucleophilic‐electrophilic attack areas of the structures. PCA and free energy landscape (FEL) analysis data confirmed the stable conformations between flavonoids and receptor. Abyssinone I and III showed nontoxic behavior. These data confirmed that if we repurpose these flavonoids against primary amoebic meningoencephalitis, it will be beneficial for mankind.

Analysis of Qatar's electricity landscape: Insights from load profiling, clustering, and policy recommendations

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. To implement appropriate DSM strategies, a greater understanding of consumer behavior is needed. To do so, load profiling and load clustering are two popular methods that can be used. This paper aims to i) summarize the most recent global load profiling and clustering works ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, weekend vs. weekday, and public holiday consumption patterns, and iii) perform load clustering to propose policies that would help manage the electricity load in Qatar for its green growth. This study provides insights into the electricity consumption trends of various sectors in Qatar, including commercial, government, hospitality, and residential sectors. It was found that among all the sectors there were only two usage periods of the same time. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Morphological Evolution of Baghdad: Analyzing Urban Growth Patterns and Transformation Processes

Urban landscape analysis is crucial for understanding the dynamics of urban transformation, especially in historic city centers. This study delves into the morphological evolution of Baghdad, focusing on its transformation from a historic nucleus to a vast metropolitan area. The primary objective is to identify mechanisms of transformation that bring about changes in the structure of the town center. Utilizing a combination of geographical-morphological methods, historical maps, and modern satellite imagery, the research identifies six distinct urban growth patterns—concentric, irregular strip, scattered, multi-nuclei, sectoral, and instant growth. The research methodology includes a detailed collection of historical maps from Western geographical travelers of the 18th and 19th centuries and modern satellite images to compare historical and contemporary urban forms. Data digitization processes were employed to prepare these maps for analysis, followed by boundary determination to track Baghdad’s expansion over time. The study integrates these digital maps to conduct temporal analyses, characterizing the main morphological phases from the historical nucleus to modern urban growth. The study is able to set forth how a complex interplay of physical, social, economic, and political forces has defined the urban form of Baghdad. Findings will make it possible to understand these growth processes for modern urban planning and design. The management of sustainable growth in a way that tackles sprawl, connectivity, and human scale in design requires a fusion of historical contexts with modern practices. This study offers the necessary information and direction for sustainable urban development in Baghdad as well as any other historic town elsewhere.

Visualization analysis and optimization strategy of thermal landscape in retrofitted elderly care buildings: A case study in Shanghai

Due to the increasing ageing population, numerous public buildings in major cities have been retrofitted into elderly care buildings. However, these retrofitted buildings often fail to meet the thermal needs of the elderly, particularly indoor thermal comfort. Unfortunately, existing measurement methods do not effectively capture the spatial and temporal distribution of the thermal environment. This study introduced a three-dimensional visualization method that combines measurements with spatial interpolation techniques to address these issues. A case study of a retrofitted nursing home in Shanghai, China, was considered, where four space interpolation methods provided by the Surfer software were used to establish visualization models of the indoor thermal landscape. The inverse distance to a power interpolation method was found to have the highest accuracy with an error of 0.27°C. Furthermore, the visualization model revealed temperature fluctuation exceeding 3°C in the activity space during the summer and identified energy waste. Consequently, thermal landscape optimization strategies were proposed, including an external shading system that can reduce cooling energy consumption by 26.8%. This study provides a visual tool for architects to accurately analyse indoor thermal environments’ distribution and variation trends whilst providing reliable data support for post-occupancy evaluation of indoor thermal environments in elderly care buildings.

Evolution of climate-smart agriculture research: A science mapping exploration and network analysis

Abstract The integrated approach of bibliometric mapping and literature review was adopted to critically examine the evolution of climate-smart agriculture (CSA) research climate from 2012 to 2023 using publications data recovered from the Elsevier Scopus database. The publication trends, stakeholder profiles, social networks, and thematic area analysis on CSA research were examined. Findings revealed that the publication trends on CSA research revealed an incremental pattern from 2012 to 2023. Stakeholder profile analysis identified numerous authors, affiliations, and countries actively engaged in CSA research. The most prolific researcher is Mangi L. Jat, whereas Wageningen University & Research is the most prolific affiliation. Finally, the United States is the most prolific nation. Stakeholder’s productivity was ascribed to a combination of factors ranging from access to research funding to collaborations. The most active funder of CSA research is CGIAR (Consortium of International Agricultural Research Centres), which has funded 52 publications, gained 1,529 citations, and an h-index of 20. The CGIAR has greatly supported the works of highly prolific authors such as Mangi L. Jat, Hanuman S. Jat, and Suresh K. Kakraliya. Social network analysis revealed strong, active, and extensive links, particularly between nations such as the US, India, Kenya, and Ethiopia. The cluster and thematic area analyses of the CSA research landscape revealed three areas, namely, Climate-Resilient Agricultural Adoption, Climate-Responsive Agricultural Practices, and Integrated Food Systems, which are the current focus of stakeholders in the field. Overall, the study showed that CSA is a broad and interdisciplinary topic with a significant impact on the social, economic, and environmental aspects of humanity and societies across the globe.

Ethnolinguistic Vitality in Minority Schoolscape

School is often said to be a representation of society because its primary aim is to promote integration into society. This study of the landscape elements of minority language schools suggests that this type of linguistic landscape may not only reflect a change in linguistic dominance but can also play a complementary role compared to the external, out-of-school world. In this paper, the authors attempt to explore a new way of interpreting the notion of the linguistic landscape of schools. The novelty of this approach lies in the application of a spatial theoretical concept, where the schoolscape is defined as an element of autopoietic space—a self-constituting spatial element. Within this autopoietic framework, markers of ethnolinguistic vitality in the linguistic landscape of minority language schools are identified, and a set of criteria is established that can be applied to other communities. The conclusions are drawn from a comparative analysis of the linguistic landscapes of Hungarian-medium schools in Romania, Slovakia, and Slovenia.

Identification of Agricultural Areas to Restore Through Nature-Based Solutions (NbS)

This study aims to present a methodological approach based on the objectives of the Nature Restoration Law and the concept of Forest Landscape Restoration to identify areas that are best suited for the implementation of Nature-based Solutions for the improvement of landscape and habitat status in the city of Campobasso (1028.64 km2). Using open data (ISPRA ecosystem services and regional land use capability), an expert based approach (questionnaire), and a multicriteria analysis (Analytical Hierarchy Process), the Total Ecosystem Services Value index was determined as a weighted additive sum of the criteria considered. The index was then classified into eight clusters, and the land use “Cropland” was extracted. Cluster 1 croplands (740.09 Ha) were identified as the areas to be allocated to Nature-based Solutions since they were those characterized by fewer ecosystem services provisioning, while Cluster 8 croplands (482.88 Ha) were identified as valuable areas to be preserved. It was then possible to compare the “Forest” areas currently present in the study area with those of a possible future scenario, represented by the areas occupied today by forest with the addition of Cluster 1 croplands. A landscape analysis was conducted; it showed greater dispersion and fragmentation of forest patches in the future scenario, but also greater connectivity and thus greater ecological functionality of the patches.

Shaping the future of AI: balancing innovation and ethics in global regulation

Abstract This article presents a comprehensive analysis of the current landscape of artificial intelligence (AI) regulation, examining the diverse approaches adopted across different global regions and the multifaceted ethical challenges presented by rapid AI advancements. The European Union’s General Data Protection Regulation (GDPR) is explored as a benchmark for data protection and privacy in AI, contrasted with the USA’s more decentralized and sector-specific approach and the varied strategies of Asian countries like China and Japan that blend State-led direction with market-driven innovation. Key ethical challenges in AI are discussed, including bias and discrimination in AI systems, the complexities of AI algorithms leading to a ‘black box’ phenomenon, and the crucial need for accountability and transparency in AI operations. The article also delves into the role of international bodies and forums in shaping AI regulations and emphasizes the importance of multi-stakeholder involvement, including academia, industry, and civil society, in developing effective AI policies. The article proposes detailed recommendations for achieving harmonized international AI regulations. It advocates for the integration of ethical considerations into AI development, the adoption of flexible and adaptive regulatory frameworks, and the promotion of multi-stakeholder engagement. Enhancing AI literacy and public engagement and facilitating international cooperation on AI are also highlighted as crucial steps. The article concludes by underscoring the critical role of policy-makers and international bodies in guiding AI development to ensure it progresses in a way that maximizes societal benefits, mitigates risks, and addresses ethical considerations comprehensively.

VEXAS Syndrome: A Comprehensive Review of Current Therapeutic Strategies and Emerging Treatments.

VEXAS syndrome is a recently identified autoinflammatory disorder resulting from somatic mutations in the UBA1 gene, leading to a complex spectrum of severe inflammatory and hematologic manifestations. The absence of established treatment guidelines and the variability in clinical presentation make its management particularly challenging. Current therapeutic approaches are often based on limited evidence, and their effectiveness remains inconsistent. This review seeks to consolidate the existing knowledge on therapeutic strategies for VEXAS syndrome, offering a critical evaluation of their efficacy and addressing the gaps in the current literature. As the clinical recognition of VEXAS grows, there is an urgent need to explore more targeted, effective treatments that can address both the inflammatory and hematologic aspects of the disease. By providing a comprehensive analysis of the current therapeutic landscape, this review aims to guide clinicians and researchers toward developing more effective, long-term management strategies for this life-threatening condition.

Using land surface phenology and information theory to assess and map complex landscape dynamics

Abstract Context Characterizing landscape ecological complexity and change requires integrated description of spatial and temporal landscape organization and dynamics, as suggested by the shifting mosaic concept. Remotely sensed land surface phenology allows the detection of even small differences among landscape patches and through time, allowing for the analysis of landscapes as shifting mosaics. Objectives We sought to quantify aspects of the complex landscape behaviors that are implied by spatiotemporal variation in land surface phenology. We adapted an information-theoretic (IT) framework from ecosystem ecology to capture landscape-level spatiotemporal complexity and organization and map these properties across large areas. Methods Phenology data were derived from remotely sensed, pixel-level time series of a vegetation greenness index, across a large portion of North America. We summarized multi-year, multi-pixel dynamics in transition matrices, calculated IT metrics from the matrices, and used matrix projection to quantify disequilibrium dynamics and long-term trajectories of the metrics. Results Mapping the IT metrics and their disequilibria revealed gradients in the spatiotemporal complexity and organization of multi-year land surface phenology dynamics at continental to local scales. These gradients suggest influences of biophysical and biogeographic setting, ecological development and disturbances, land use, and other drivers of landscape ecological dynamics. The spatiotemporal IT metrics were influenced by both year-to-year dynamics and spatial landscape heterogeneity, but correlations with spatial and temporal complexity measures varied among the IT metrics. Landscapes showing the strongest disequilibrium dynamics were mostly in the western part of the continent and appeared to be associated with large-scale disturbances including severe fire, forest pathogens, climate variability, and land use change—important subjects for further study. Conclusions This approach reveals novel features of the shifting landscape mosaic, with implications for understanding landscape resilience and sustainability. Resulting spatial data products describing long-term landscape dynamics have potential applications in broad-scale ecological modeling, monitoring, assessment, and prediction.

Molecular Insights Into β-Glucuronidase Inhibition by Alhagi Graecorum Flavonoids: A Computational and Experimental Approach.

In this study, we aimed to investigate the inhibitory mechanisms of β-glucuronidase by flavonoids derived from Alhagi graecorum through both experimental and computational approaches. The activity of β-glucuronidase was assessed using an in vitro enzyme inhibition assay, where myricetin and chrysoeriol were identified as potent inhibitors based on their low IC50 values. Kinetic studies were conducted to determine the inhibition type, revealing that both compounds exhibit noncompetitive inhibition of β-glucuronidase-catalyzed hydrolysis of PNPG. Molecular docking was employed to explore the binding affinities of the flavonoids, showing that myricetin formed the highest number of polar interactions with the enzyme. Additionally, molecular dynamics (MD) simulations were performed to evaluate the stability of the enzyme-inhibitor complexes, demonstrating consistent trajectory behavior for both compounds, with significant energy stabilization. Interaction energy analyses highlighted the dominant role of electrostatic forces in myricetin's inhibition mechanism, while Van der Waals forces were more prominent for chrysoeriol. The MM/PBSA method was used to calculate the binding free energies, with myricetin and chrysoeriol exhibiting the lowest values. Potential energy landscape analysis further revealed that β-glucuronidase adopts a more closed conformation when bound to these inhibitors, limiting substrate access. These findings suggest that flavonoids from Alhagi graecorum hold promise for clinical applications, particularly in managing drug-induced enteropathy.

Network Analysis of miRNA and Cytokine Landscape in Human Hematopoiesis.

The differentiation/maturation trajectories of different blood cell types stemming from a CD34+ common ancestor takes place in different biologically relevant multidimensional spaces. Here, we generated microRNA and cytokine profiles from highly purified populations of hematopoietic progenitors/precursors derived from cord blood hematopoietic stem/progenitor cells. MicroRNA and cytokine landscapes were then analyzed to find their mutual relationships under the hypothesis that the highly variable miRNome corresponds to the 'force field' driving the goal of a stable phenotype (here corresponding to the cytokine abundance pattern) typical of each cell kind. The high dimensionality and lack of linearity of the hematopoietic process pushed us to adopt a distance-geometry approach to compare different trajectories, while a complex network analysis was instrumental in revealing the fine structure of microRNA-cytokine relations. Importantly, the approach enabled us to identify a limited number of factors (represented either by microRNAs or cytokines) corresponding to crucial nodes responsible for connecting distinct interaction modules. Subtle changes in 'master nodes', keeping the connections between different regulatory networks, may therefore be crucial in influencing hematopoietic differentiation. These findings highlight the extremely interconnected network structures underlying hematopoiesis regulation and identify key factors in the microRNA/cytokine landscape that may be potentially crucial for influencing network stability.

Identification of novel PAD2 inhibitors using pharmacophore-based virtual screening, molecular docking, and MD simulation studies

In the realm of epigenetic regulation, Protein arginine deiminase 2 (PAD2) stands out as a therapeutic target due to its significant role in neurological disorders, rheumatoid arthritis (RA), multiple sclerosis (MS), and various cancers. To date, no in silico studies have focused on PAD2 for lead compound identification. Therefore, we conducted structure-based pharmacophore modeling, virtual screening, molecular docking, molecular dynamics (MD) simulations, and essential dynamics studies using PCA and free energy landscape analyses to identify repurposed drugs and selective inhibitors against PAD2. The best pharmacophore model, ‘Pharm_01,’ had a selectivity score of 10.485 and an excellent ROC curve quality of 0.972. Pharm1 consisted of three hydrogen bond donors (HBD) and two hydrophobic (Hy) features (DDDHH). A virtual screening of about 9.2 million compounds yielded 2575 hits using a fit value threshold of 2.5 and drug-likeness criteria. Molecular docking identified the top ten molecules, which were verified using MD simulations. Stability was verified using MM-PBSA studies, whereas conformational differences were investigated using PCA and free energy landscape analyses. Two hits (Leads 1 and 2) from the DrugBank dataset showed promise for repurposing as PAD2 inhibitors, while one hit compound (Lead 8) from the ZINC database emerged as a novel PAD2 inhibitor. These findings indicate that the discovered compounds may be potent PAD2 inhibitors, necessitating additional preclinical and clinical research to produce viable treatments for cancer and neurological disorders.

Impact of sodium dodecyl sulfate, cetyltrimethylammonium chloride and octyl glucoside surfactants on Aβ dimer conformations: A multiscale approach with MD simulations

This research investigates the effects of an anionic surfactant, sodium dodecyl sulfate (SDS), a cationic surfactant, cetyltrimethylammonium chloride (CTAC), and a nonionic surfactant, octyl glucoside (OG), on the conformation of beta-amyloid peptide dimer using molecular dynamics simulations to decipher the molecular mechanisms underlying these interactions and their effects on Aβ aggregation and toxicity in Alzheimer’s disease. Four simulation models were crafted, each housing a beta-amyloid peptide dimer, with three models incorporating 60 CTAC, 80 SDS, and 50 OG molecules to surpass the critical micelle concentration. Key findings include significant impacts of SDS, CTAC, and OG on the conformational dynamics of Aβ peptides as evidenced by RMSD and RMSF analyses. SDS notably increased the RMSF of residues 1–10 and had a pronounced effect on the C-termini, disrupting the critical salt bridge between aspartic acid 23 and lysine 28, associated with reduced peptide toxicity. Additionally, SDS disrupted more hydrogen bonds and hydrophobic interactions between the monomers of the Aβ dimer, predominantly in the C-termini region. Free Energy Landscape analysis revealed significant structural changes in the Aβ dimer in the presence of surfactants, with varying distributions of surfactant aggregates. Binding free energy calculations using MM-PBSA demonstrated that SDS exhibited the strongest binding affinity to the Aβ dimer, followed by CTAC and OG, with electrostatic forces playing a pivotal role. These outcomes align with experimental data, suggesting that SDS and CTAC, at concentrations above the CMC, diminish Aβ peptide aggregation and toxicity, whereas OG marginally increases toxicity.

Identification of Potential Inhibitors of TGFβR1 for the Treatment of Cancer through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

Globally, cancer is one of the leading causes of death. Resistance to conventional medications, such as chemotherapy and radiation, continues to be a significant challenge in the treatment of cancer despite the availability of numerous medicines. Therefore, the highest priority is to hunt for new therapeutic agents. Transforming growth factor-beta is a pivotal regulatory cytokine that exerts significant influence over cellular processes, particularly emphasizing its role in facilitating and modulating cell proliferation. TGF-β receptor 1, identified as the most promising active site of the TGF-β signaling, is a potent drug target site that has garnered wide attention for developing new anticancer agents. The present investigation investigates the potential natural products as TGFβR1 inhibitors. The SB431542 complexed TGFβR1 protein model was used to screen the natural product database to obtain a compound with high binding potential. NPC247629 has emerged as the best-scored compound among all the screened compounds, demonstrating the highest affinity towards the TGFβR1 regarding docking score -17.54 kcal/mol. The all-atoms MD simulation study indicated that all proposed hits are retained inside the receptor in dynamic states. Additionally, principal component and free energy landscape analysis were performed to explore the binding mechanism of top-hit natural products. The best-screened hits, NPC247629 and NPC60735, have excellent binding affinity and hold a massive potential for TGFβR1 inhibition, paving the way for promising future investigations in cancer treatment.

Scientific landscape of oxidative stress in sarcopenia: from bibliometric analysis to hotspots review.

Sarcopenia is a significant healthcare challenge in the aging population. Oxidative stress (OS) is acknowledged to play a pivotal role in the pathological progression of sarcopenia. Numerous studies have demonstrated that mitigating or eliminating OS can ameliorate the pathological manifestations associated with sarcopenia. However, current clinical antioxidant therapies often fall short of anticipated outcomes. This bibliometric analysis aims to delineate prevailing research trends, thematic emphases, focal points, and developmental trajectories within the domain of OS in sarcopenia, while also endeavoring to explore prospective anti-oxidative stress strategies for future clinical interventions. Relevant publications were retrieved from the Web of Science (WOS) Core Collection database for the period 2000-2024. Citespace was employed for retrieving and analyzing trends and emerging topics. In the field of OS in sarcopenia, the number of publications has significantly increased from 2000 to 2024. The United States and China are the primary contributors to global publication output. The most productive research institution is INRAE. The most prolific author is Holly Van Remmen from the United States, while the most frequently cited author is Cruz-Jentoft AJ from Spain. Experimental Gerontology is the journal with the highest volume of published articles, whereas the Journal of Gerontology Series A: Biological Sciences and Medical Sciences holds the record for the highest number of citations. The research keywords in this field can be categorized into eight domains: "Physiology and anatomy", "Physiological mechanisms", "Pathology associations", "Experimental studies", "Nutrition and metabolism", "Sports and physical activities", "Age" and "Oxidation and antioxidation". Moreover, recent years have seen the emergence of "TNF-α," "insulin resistance", "mitochondrial autophagy", "signal pathways", and "mechanisms" as focal points in the realm of OS in sarcopenia, encompassing related fundamental research and clinical translation. This bibliometric and visualization provides a comprehensive analysis of the global research landscape in the field of OS in sarcopenia, identifies priorities, summarizes the current research status and suggests possible future research priorities. In addition, in order to benefit more sarcopenia patients, strengthening cooperation and communication between institutions and research teams is the key to the future development of this field. Given the expectation that research on OS in sarcopenia will remain a prominent area of interest in the future, this article could serve as a valuable resource for scholars seeking to shape future studies through an understanding of influential scholarly contributions and key research findings. https://www.crd.york.ac.uk, identifier CRD42024528628.

Pan-cancer landscape analysis of NOP58 and its oncogenic driving role in lung adenocarcinoma

Despite improvements in treatment in recent years, patients with lung adenocarcinoma (LUAD) still face poor prognoses. In this study, we elucidated the potential role of NOP58 ribonucleoprotein in pan-cancer and validated its oncogenic significance in LUAD using bioinformatics and in vitro and in vivo functional assays. NOP58 was found to be overexpressed in various types of tumors. It had great precision for predicting 20 distinct cancer types using receiver operating characteristic curve (ROC) as well as significant connections with the prognoses in particular cancers. In LUAD, NOP58 expression was correlated substantially with the TNM stage, pathologic stage, smoking status, and effectiveness endpoints, when we analyzed its association with clinical characteristics in LUAD. Elevated NOP58 expression was shown as connected with Th2 cell infiltration while also negatively linked with infiltrating other immune cells, such as CD8 T, cytotoxic, and Th1. By inhibiting NOP58 within the LUAD cells, we found a decrease in cells’ capability to proliferate, migrate, and invade. Knockdown of NOP58 inhibited tumor growth in mouse xenograft models. Furthermore, the tissue microarray study indicated that there was a greater expression of NOP58 in the tumor tissues compared to adjacent normal tissues in LUAD. Our findings revealed that NOP58 could be an outstanding bio-index for pan-cancer diagnosis and prognosis and an independent prognostic risk factor in LUAD.

Prognostic characteristics and drug sensitivity analysis of hepatocellular carcinoma based on histone modification-related genes: a multi-omics integrated study revealing potential therapeutic targets and individualized treatment strategies.

Hepatocellular carcinoma (HCC) ranks among the most prevalent and lethal malignancies worldwide. Histone modifications (HMs) play a pivotal role in the initiation and progression of HCC. However, our understanding of HMs in HCC remains limited due to the disease's heterogeneity and the complexity of HMs. We integrated multi-omics data from multiple cohorts, including single-cell RNA sequencing, bulk RNA sequencing, and clinical information. Weighted gene co-expression network analysis (WGCNA) and consensus clustering were employed to identify histone-related genes. We developed a histone modification-related signature (HMRS) using 117 machine learning methods. Comprehensive analyses of molecular characteristics, immune landscape, and drug sensitivity associated with the HMRS were performed. Through integrative analysis, we defined 110 histone-related genes and identified 45 HCC-HM-related genes (HCC-HMRgenes). The HMRS demonstrated robust prognostic value across multiple cohorts. Patients with high HMRS scores exhibited distinct genomic alterations, including higher tumor heterogeneity and TP53 mutations. The high-risk group showed enrichment in cell cycle, DNA repair, and metabolic pathways. Immune landscape analysis revealed significant differences in immune cell infiltration and pathway activities between high- and low-risk groups. Drug sensitivity prediction suggested potential therapeutic strategies for different risk groups. Our study provides a comprehensive understanding of HMs in HCC and establishes a robust prognostic signature. The HMRS not only stratifies patients into distinct risk groups but also offers insights into underlying molecular mechanisms, immune characteristics, and potential therapeutic strategies, paving the way for personalized medicine in HCC.

Development and Usability of the OHiFamily Mobile App to Enhance Accessibility to Maternal and Infant Information for Expectant Families in Ohio: Qualitative Study.

The Infant Mortality Research Partnership in Ohio is working to help pregnant individuals and families on Medicaid who are at risk for infant mortality and preterm birth. As part of this initiative, researchers at The Ohio State University worked to develop a patient-facing mobile app, OHiFamily, targeted toward, and created for, this population. To address the social determinants of health that can affect maternal and infant health, the app provides curated information on community resources, health care services, and educational materials in a format that is easily accessible and intended to facilitate contact between families and resources. The OHiFamily app includes 3 distinct features, that is, infant care logging (eg, feeding and diaper changes), curated educational resources, and a link to the curated Ohio resource database (CORD). This paper describes the development and assessment of the OHiFamily app as well as CORD. This study aimed to describe the development of the OHiFamily mobile app and CORD and the qualitative feedback received by the app's intended audience. The researchers performed a landscape analysis and held focus groups to determine the resources and app features of interest to Ohio families on Medicaid. Participants from several focus groups were interested in an app that could offer community resources with contact information, information about medical providers and information and ways to contact them, health tips, and information about pregnancy and infant development. Feedback was provided by 9 participants through 3 focus group sessions. Using this feedback, the team created a curated resource database and mobile app to help users locate and access resources, as well as access education materials and infant tracking features. OHiFamily offers a unique combination of features and access to local resources for families on Medicaid in Ohio not seen in other smartphone apps.

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.