Landscape Analysis Research Articles

A Single-Cell Landscape of Spermioteleosis in Mice and Pigs.

(1) Background: Spermatozoa acquired motility and matured in epididymis after production in the testis. However, there is still limited understanding of the specific characteristics of sperm development across different species. In this study, we employed a comprehensive approach to analyze cell compositions in both testicular and epididymal tissues, providing valuable insights into the changes occurring during meiosis and spermiogenesis in mouse and pig models. Additionally, we identified distinct gene expression signatures associated with various spermatogenic cell types. (2) Methods: To investigate the differences in spermatogenesis between mice and pigs, we constructed a single-cell RNA dataset. (3) Results: Our findings revealed notable differences in testicular cell clusters between these two species. Furthermore, distinct gene expression patterns were observed among epithelial cells from different regions of the epididymis. Interestingly, regional gene expression patterns were also identified within principal cell clusters of the mouse epididymis. Moreover, through analysing differentially expressed genes related to the epididymis in both mouse and pig models, we successfully identified potential marker genes associated with sperm development and maturation for each species studied. (4) Conclusions: This research presented a comprehensive single-cell landscape analysis of both testicular and epididymal tissues, shedding light on the intricate processes involved in spermatogenesis and sperm maturation, specifically within mouse and pig models.

Abstract 6301: Mapping survivor-specific supportive care resources: A digital landscape analysis of NCI-designated cancer centers

Abstract Background: Optimal care for cancer survivors requires a comprehensive array of supportive care services to address the multitude of physical, psychological, social, financial, and health promotion needs of this population. To understand options to address their unique needs, survivors and their caregivers may seek information from online sources. Although National Cancer Institute-Designated Cancer Centers (NCIDCCs) serve as leaders in cancer treatment and care delivery, as well as credible sources for survivors to receive information on their recovery, no study has characterized the digital landscape of survivorship offerings on their website. This study aims to characterize the supportive care services offered to cancer survivors on NCIDCCs. Methods: Of the 72 NCIDCCs, 65 provide clinical care. We reviewed the webpage of each to identify supportive care offerings. Services were categorized based on the four domains of the quality-of-life model: (1) physical well-being, (2) psychological well-being, (3) social well-being, and (4) spiritual well-being. Supportive care services were classified as survivor-specific if the online description included information that specifically catered to cancer survivors. Results: Of the 65 clinical NCIDCC websites, 100% offer supportive care services and 54% offer supportive care services specific to survivors (Table). The most common supportive care service offered was nutrition (90.6%) and the least commonly offered was caregiver support (7.9%). Behavioral medicine was offered most frequently for survivors (28.3%) and caregiver support was offered the least (0%). Out of the four domains of the quality-of-life model, spiritual well-being services were offered the least at NCIDCCs. Conclusion: More than half of the NCIDCC websites provided survivor-specific supportive care services. Supportive care services on websites of NCI-Designated or Comprehensive Cancer Centers (N = 65) Supportive Care Service (n) % Supportive Care Service Specific to Cancer Survivors (n) % Physical Well Being Services Nutrition 58 90.6 14 24.1 Genetics 53 82.8 5 9.4 Pain and Palliative Care 52 81.3 8 15.4 Complementary and Alternative Medicine 49 75.4 6 12.2 Integrative Medicine 47 73.4 9 19.1 Occupational Therapy 45 70.3 6 13.3 Fertility Preservation 43 66.2 6 14 Tobacco Cessation 38 58.5 6 15.8 Speech 36 56.3 3 8.3 Exercise 29 45.3 5 17.2 Primary Care 6 9.2 3 50 Psychological Well-Being Services Behavioral Medicine 53 81.5 15 28.3 Recreational Therapy 36 58.1 5 13.9 Social Well-Being Services Oncology Social Work 56 87.5 5 8.9 Patient Navigation 33 51.6 3 9.1 Financial Counseling 32 50 4 12.5 Self-Image 27 41.5 3 11.1 Reproductive and Sexual Health 22 33.8 5 22.7 Caregiver Support 5 7.9 0 0 Spiritual Well-Being Services Pastoral Services 49 75.4 5 10.2 Citation Format: Alexis Taylor, Katherine Sterba, Evan Graboyes. Mapping survivor-specific supportive care resources: A digital landscape analysis of NCI-designated cancer centers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6301.

Modeling, landscape analysis, and solving the capacitated single-allocation hub maximal covering problem using the GARVND hybrid algorithm

In this paper, a new variation to the Hub Maximal Covering problem called the Capacitated Single-Allocation Hub Maximal Covering Problem (CSAHMCP) is addressed for the first time and a novel binary-integer mathematical model has been proposed. To analyze the features of the problem’s solution space and to determine the most effective heuristic approach to solve it, a comprehensive Fitness Landscape Analysis is performed which reveals that the problem is of ‘rugged plain’ type and can best be solved using an algorithm with good exploration and exploitation capabilities at the same time. Accordingly, we proposed a new algorithm named GARVND, which is a hybrid of the Genetic Algorithm acting as the main framework for diversification, and a variant of the Variable Neighborhood Descent algorithm called ‘Reduced VND’ acting as a means to improve individual solutions (intensification) by searching in the spaces generated by four types of neighborhood operators. We also introduced two original crossover and mutation operators that can be used for any hub location solution method. The parameters of GARVND are tuned toward less computational times and higher solution quality by using the Taguchi experiment design technique. We solved 114 CSAHMCP instances (several times each) from the Australian Post (AP) dataset using the proposed GARVND, as well as by the Genetic Algorithm, Variable Neighborhood Search, Simulated Annealing, Tabu Search algorithms, and a hybrid algorithm consisting of Grey Wolf Optimizer and Simulated Annealing, in addition to the Gurobi exact solver. Extensive computational results and statistical analyses showed that GARVND yields favorable solutions, outperforming all the compared algorithms by producing smaller gaps with global optimal objective values. In addition, to illustrate the practical value of the CSAHMCP and the GARVND method, we performed a cost-flow sensitivity analysis for the AP Central Sydney network and came up with the best expansion scenarios to be adopted by the management.

Assessing the Effectiveness of ADS-B Mitigations

The rise in aerial traffic necessitates aircraft localisation methods that go beyond radar technology's built-in capabilities. The Automated Dependent Surveillance-Broadcast (ADS-B) system is a novel aircraft localisation method that promises to provide the necessary precision to handle the current air traffic surge. The Federal Aviation Administration has, therefore, enforced ADS-B's deployment. However, the architecture of the ADS-B system holds several vulnerabilities. Most of these vulnerabilities are because ADS-B is designed to rely on wireless networks. This paper provides an in-depth analysis of the ADS-B threat landscape and potential mitigations to better understand their distinct characteristics and impact on the ADS-B system. Addressing these security concerns is imperative to ensure ADS-B systems' robustness and trustworthiness and safeguard the aviation industry from potential cyber threats. The paper concludes with a critical review of how well the proposed mitigations address the identified security threats.

Continuity, Resilience, and Change in Rural Settlement Patterns from the Roman to Islamic Period in the Sicani Mountains (Central-Western Sicily)

This study aims to analyze the dynamics of change in settlement models from the Roman, late antique, and Byzantine periods, focusing on how these transformations influenced the formation of Islamic societies in the rural landscapes of western Sicily. The study is centered around the territory of Corleone in the Sicani Mountains (central-western Sicily). This region, strategically located between the significant cities of Palermo on the Tyrrhenian Sea and Agrigento on the Strait of Sicily, has been pivotal in the communication network spanning from the Roman era to the Middle Ages and beyond. The area has been subject to extensive surveys and excavations, revealing diverse dynamics of continuity, resilience, and innovation in settlement patterns from the Roman to the Islamic periods. Beyond presenting the results of archaeological fieldwork, this study employs GIS-based spatial and statistical analyses and utilizes a range of topographic (elevation, slope, aspect, topographic position index (TPI), and distance to water sources) and ecological factors (vegetation series). These analyses aim to assess the evolving relationships and site positioning within the territory over time. Combining archaeological data with topographic and ecological landscape analysis, this integrated approach elucidates the complex transition dynamics from the Roman settlement system to the Islamic age’s landscape formation in western Sicily’s rural areas. The study thereby contributes to a deeper understanding of the intricate interplay between historical developments and environmental factors in shaping rural settlement patterns.

Landscape image recognition and analysis based on deep learning algorithm

Garden landscape is the combination of nature and humanity, with high aesthetic value, ecological value and cultural value, has become an important part of people’s life. Modern people have a higher pursuit for the spiritual food such as garden landscape after the material life is satisfied, which brings new challenges to the construction of urban garden landscape. As an advanced type of machine learning, deep learning applied to landscape image recognition can solve the problem of low quality and low efficiency of manual recognition. Based on this, this paper proposes a garden landscape image recognition algorithm based on SSD (Single Shot Multibox Detector), which realizes accurate extraction and recognition of image features by positioning the target, and can effectively improve the quality and efficiency of landscape image recognition. In order to test the feasibility of the algorithm proposed in this paper, experimental analysis was carried out in the CVPR 2023 landscape data set. The experimental results show that the algorithm has a high recognition accuracy for landscape images, and has excellent performance compared with traditional image recognition algorithms.

Identification of PDGFA as a Neutrophil-related Biomarker Linked to the Advancement of Diabetic Retinopathy through Integrated Bioinformatics Analysis.

The dysregulation of the innate immune system plays a crucial role in the development of Diabetic Retinopathy (DR). To gain an insight into the underlying mechanism of DR, it is essential to identify specific biomarkers associated with immune cell infiltration. In this study, we retrieved the GSE94019 and GSE60436 datasets from the Gene Expression Omnibus (GEO) database. By utilizing CIBERSORT, MCPcounter, and xCell algorithms, we conducted a comprehensive analysis of the immune cell infiltration landscape in DR. The limma package was employed to identify Differentially Expressed Necroptosis-related Genes (DENRGs). Subsequently, enrichment analysis was performed to investigate the potential functions of the DENRGs. To identify the core DENRGs, the CytoHubba plug-in in Cytoscape software was utilized. The expression levels of these core DENRGs were verified in an independent dataset. Our analysis identified 213 DENRGs, and among them, Platelet-derived Growth Factor subunit A (PDGFA) was identified as a core DENRG. Notably, the expression of PDGFA was found to be upregulated in DR, and this finding was further validated in the GSE102485 dataset. Additionally, the results of GSVA and GSEA revealed that in the high PDGFA group, there was activation of pathways related to inflammation and the immune system. Moreover, analysis of immune infiltration demonstrated a significant association between PDGFA gene expression and the infiltration levels of specific immune cells, including basophils, macrophages M1, macrophages, neutrophils, monocytes, NK cells, and B cells. The involvement of neutrophils in the development and progression of DR is suggested. PDGFA has emerged as a potential marker and is linked to the infiltration of immune cells in DR. These findings shed new light on the underlying mechanisms of DR.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Opportunities for Integration in the Dual Medicare-Medicaid Population: North Carolina Landscape Analysis.

NC Medicaid is amid a transformation to value-based care models. The transformation requires the consideration of models that may best serve the 1 in 5 NC Medicaid beneficiaries who are dually enrolled in Medicare. The lack of integration of Medicare and Medicaid administration, financing, and care may contribute to suboptimal health outcomes and care experiences. Integrating Medicare and Medicaid to improve care for dually enrolled individuals requires knowledge of the demographic, eligibility, and enrollment trends, as well as the medical needs of these individuals throughout North Carolina. We performed a cross-sectional analysis of 2019 NC Medicaid administrative enrollment data and 2015-2020 Medicare and Medicaid public use files. Nearly half of North Carolina's full-benefit dual-eligible (FBDE) population was eligible for Medicare due to disability. About one-third of the FBDE population lost full Medicaid benefits at some point during 2019; of these, 65% were under age 65. Most FBDE beneficiaries were enrolled in Medicaid fee-for-service, with 3.5% enrolled in Community Alternatives Program (CAP) waivers, 2% in Medicaid waivers for beneficiaries with behavioral health or intellectual and developmental disabilities (BH/IDD) or traumatic brain injury (TBI), and 1% in the Program of All-Inclusive Care for the Elderly (PACE). Enrollment in Medicare Duals Special Needs Plans (D-SNPs) increased from 7.3% of the FBDE population in 2015 to 32.5% in 2021. This descriptive, cross-sectional evaluation of North Carolina duals may not be generalizable to other time periods and contexts. Medicare-Medicaid integration presents an opportunity to improve the value of care for dual-eligible beneficiaries and aligns with the goals of NC Medicaid transformation. Integration strategies that leverage the current infrastructure may minimize disruption of services for beneficiaries and mitigate the impacts of unstable enrollment, particularly among working-age adults and disabled beneficiaries. The diversity within the dual-eligible populations requires a strategy designed to improve health equity across race, disability, eligibility, geography, and health care needs.

Analysis of the Conformational Landscape of the N-Domains of the AAA ATPase p97: Disentangling the Continuous Conformational Variability in Partially Symmetrical Complexes.

Single-particle cryo-electron microscopy (cryo-EM) has been shown to be effective in defining the structure of macromolecules, including protein complexes. Complexes adopt different conformations and compositions to perform their biological functions. In cryo-EM, the protein complexes are observed in solution, enabling the recording of images of the protein in multiple conformations. Various methods exist for capturing the conformational variability through analysis of cryo-EM data. Here, we analyzed the conformational variability in the hexameric AAA + ATPase p97, a complex with a six-fold rotational symmetric core surrounded by six flexible N-domains. We compared the performance of discrete classification methods with our recently developed method, MDSPACE, which uses 3D-to-2D flexible fitting of an atomic structure to images based on molecular dynamics (MD) simulations. Our analysis detected a novel conformation adopted by approximately 2% of the particles in the dataset and determined that the N-domains of p97 sway by up to 60° around a central position. This study demonstrates the application of MDSPACE in analyzing the continuous conformational changes in partially symmetrical protein complexes, systems notoriously difficult to analyze due to the alignment errors caused by their partial symmetry.

Testing the feasibility of targeting a conserved region on the S2 domain of the SARS-CoV-2 spike protein

The efficacy of vaccines against the SARS-CoV-2 virus significantly declines with the emergence of mutant strains, prompting investigation into the feasibility of targeting highly conserved but often cryptic regions on the S2 domain of spike protein. Using tools from molecular dynamics, we find that exposure of a conserved S2 epitope located in the central helices below the receptor binding domains would require large-scale motion beyond receptor binding domain up-down motion, but, along the reaction coordinates we explored, it is unlikely to be exposed by such large-scale dynamic fluctuations of the S1 domain without any external facilitating factors, despite some previous computational evidence suggesting transient exposure of this region. Furthermore, glycans, particularly those on N165 and N234, hinder S2-exposing opening dynamics, and thus stabilize spike in addition to immunologically shielding the protein surface. Although the S2 epitope region examined here is central to large-scale conformational changes during viral entry, free energy landscape analysis obtained using the path coordinate formalism reveals no inherent “loaded spring” effect, suggesting that a vaccine immunogen would tend to present the epitope in a prefusion-like conformation and may be effective in neutralization. These findings contribute to a deeper understanding of the dynamic origins of the function of the spike protein, as well as further characterizing the feasibility of the S2 epitope as a therapeutic target.

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

China’s Fushun open-pit mine is the largest century-old coal mine in Asia. Large-scale mining and the use of coal has caused dramatic changes in the regional urban landscape pattern, seriously affecting the ecological function and ecosystem stability of its surrounding landscape. Evaluating the ecological risks of the landscape in the urban areas of open-pit mines contributes to the risk management of regional ecosystems and the sustainable development of society. This study selected six-phase Landsat ETM/OLI remote sensing images from 1995 to 2020 and combined them with the random forest model to carry out an LULC classification of the open-pit mine and its surrounding areas and, on this basis, discusses the evolution of its landscape pattern and evaluates the ecological risks. It fills the gap in the research on the evolution of regional landscape patterns and ecological risks in the study area and improves the automatic classification efficiency of LULC for use in open-pit mines. The results show that the classification accuracy of LULC regarding open-pit mines based on image pixels and the random forest model can reach 30 m, and the rate of accuracy can reach 92–97%. From 1995 to 2020, the coverage area of forest land and building land in the study area has increased and is mainly composed of grassland and undeveloped land. The use of land was transferred to the mining area, the water body area maintained a relative dynamic balance, and the overall vegetation coverage of the mining area was greatly improved; the forest land began to expand from the surrounding area to the mining area in 2010, and the construction land began to move from the areas surrounding the mining area to the surrounding valleys in 2015. The landforms have extended radially, and the landscape sprawl index has increased, indicating the optimization of the ecological environment; the high- and medium-risk areas decreased by 75.51 km2, the low-risk areas expanded by 461.48 km2, and the overall ecological risk index decreased. From this, it is possible to conclude that the landscape restoration project adopted in the study area has achieved great results, and the improvement of the ecological environment also directly affects the increase of construction land. These research results can provide scientific guidance for the rational utilization and sustainable development of land resources in urban areas of open-pit mines.

Smart Glasses for Cultural Heritage: A Survey

This paper presents a comprehensive survey on the utilization of smart glasses in the context of cultural heritage. It offers a systematic exploration of prevailing trends, the latest state-of-the-art technologies, and notable projects within this emerging field. Through a meticulous examination of diverse works, this study endeavors to categorize and establish a taxonomy, thereby facilitating a structured analysis of the current landscape. By distilling key insights from this categorization, the paper aims to draw meaningful conclusions and provide valuable insights into the potential future trajectory of SGs technology in the realm of CH preservation and appreciation.

Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health

When data is scarce, software analytics can make many mistakes. For example, consider learning predictors for open source project health (e.g., the number of closed pull requests in 12 months time). The training data for this task may be very small (e.g., 5 years of data, collected every month means just 60 rows of training data). The models generated from such tiny datasets can make many prediction errors. Those errors can be tamed by a landscape analysis that selects better learner control parameters. Our niSNEAK tool (a) clusters the data to find the general landscape of the hyperparameters, then (b) explores a few representatives from each part of that landscape. niSNEAK is both faster and more effective than prior state-of-the-art hyperparameter optimization algorithms (e.g., FLASH, HYPEROPT, OPTUNA). The configurations found by niSNEAK have far less error than other methods. For example, for project health indicators such as C = number of commits, I = number of closed issues, and R = number of closed pull requests, niSNEAK ’s 12-month prediction errors are {I=0%, R=33% C=47%}, whereas other methods have far larger errors of {I=61%,R=119% C=149%}. We conjecture that niSNEAK works so well since it finds the most informative regions of the hyperparameters, then jumps to those regions. Other methods (that do not reflect over the landscape) can waste time exploring less informative options. Based on the preceding, we recommend landscape analytics (e.g., niSNEAK ) especially when learning from very small datasets. This article only explores the application of niSNEAK to project health. That said, we see nothing in principle that prevents the application of this technique to a wider range of problems. To assist other researchers in repeating, improving, or even refuting our results, all our scripts and data are available on GitHub at https://github.com/zxcv123456qwe/niSneak.

CNN-HT: A Two-Stage Algorithm Selection Framework.

The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories. The two-stage approach can adapt to different algorithm combinations without the need to retrain the entire model, and modifications can be made in the second stage only, which is an improvement of one-stage approaches. To provide a more general structure for the classification model, we adopt Exploratory Landscape Analysis (ELA) features of the problem as input and utilize feature selection techniques to reduce the redundant ones. In problem classification, the average accuracy of classifying problems using CNN is 96%, which demonstrates the advantages of CNN compared to Random Forest and Support Vector Machines. After feature selection, the accuracy increases to 98.8%, further improving the classification performance while reducing the computational cost. This demonstrates the effectiveness of the first stage of the CNN-HT method, which provides a basis for algorithm selection. In the experiments, CNN-HT shows the advantages of the second stage algorithm as well as good performance with better average rankings in different algorithm combinations compared to the individual algorithms and another algorithm combination approach.

Advancing rare disease treatment: EMA’s decade-long insights into engineered adoptive cell therapy for rare cancers and orphan designation

Adoptive cell therapy (ACT), particularly chimeric antigen receptor (CAR)-T cell therapy, has emerged as a promising approach for targeting and treating rare oncological conditions. The orphan medicinal product designation by the European Union (EU) plays a crucial role in promoting development of medicines for rare conditions according to the EU Orphan Regulation.This regulatory landscape analysis examines the evolution, regulatory challenges, and clinical outcomes of genetically engineered ACT, with a focus on CAR-T cell therapies, based on the European Medicines Agency’s Committee for Orphan Medicinal Products review of applications evaluated for orphan designation and maintenance of the status over a 10-year period. In total, 30 of 36 applications were granted an orphan status, and 14 subsequently applied for maintenance of the status at time of marketing authorisation or extension of indication. Most of the products were autologous cell therapies using a lentiviral vector and were developed for the treatment of rare haematological B-cell malignancies. The findings revealed that 80% (29/36) of the submissions for orphan designation were supported by preliminary clinical data showing a potential efficacy of the candidate products and an added clinical benefit over currently authorised medicines for the proposed orphan condition. Notably, in 89% (32/36) of the cases significant benefit of the new products was accepted based on a clinically relevant advantage over existing therapies. Twelve of fourteen submissions reviewed for maintenance of the status at time of marketing authorisation or extension of indication demonstrated significant benefit of the products over existing satisfactory methods of treatment within the approved therapeutic indications, but one of the applications was withdrawn during the regulatory evaluation.This article summarises the key findings related to the use of engineered ACT, primarily CAR-T cell therapies, in targeting and treating rare cancers in the EU. It emphasises the importance of use of clinical data in supporting medical plausibility and significant benefit at the stage of orphan designation and highlights the high success rate for these products in obtaining initial orphan designations and subsequent maintaining the status at the time of marketing authorisation or extension of indication.

Genome-wide repeat landscapes in cancer and cell-free DNA.

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Comparative Analysis of Mass Media in Nigeria and Ghana

This study conducts a comparative analysis of the mass media landscapes in Nigeria and Ghana, focusing on their historical development, regulatory frameworks, and content focus. Drawing on a diverse array of sources, including books, journal articles, and government reports, the research evaluates the trajectory of media evolution in both countries from their colonial past to the present day. The study employs a theoretical framework that combines the political economy of communication and the cultural studies perspective. The political economy of communication approach highlights the economic, political, and social factors that shape media content and structure, while the cultural studies perspective emphasizes the role of media in shaping cultural values and identities. The findings reveal that both countries have shared priorities in promoting national unity, addressing social and economic development issues, and preserving cultural values. The study concludes that while both countries share some commonalities, such as the emergence of indigenous broadcasters and the growth of online news, there are also notable differences. The study recommends that both Nigeria and Ghana should work to foster international collaboration in the mass media industry.

Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives.

This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.

Development of a prognostic model for lung adenocarcinoma polarity-related genes and analysis of immune landscape.

Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)-LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity-related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high- and low-risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low-risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high-risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high-risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low-risk group. XAV-939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.