Letteratura scientifica selezionata sul tema "Co-Occurence network"

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

Identifikasi penyakit berdasarkan iridologi dengan metode terbaik sangat dinantikan para ahli sehingga dapat menjadi kemajuan dalam diagnosis medis. Untuk itu penting menghasilkan sistem yang akurat untuk identifikasi penyakit berdasarkan iridologi. Sistem yang akurat diperlukan metode terbaik yang menghasilkan akurasi yang tinggi. Maka dalam tulisan ini dilakukan tinjauan pustaka sistematis/ systematic literature review (slr) untuk menganalisis metode yang digunakan dalam setiap tahap identifikasi penyakit melalui citra iris mata. Hasilnya didapatkan metode Gray Level Co-Occurence Matrix (GLCM) untuk ekstraksi ciri dan metode untuk Convolutional Neural Network (CNN) dan SVM untuk klasifikasi/matching.

Various methods are available to perform feature extraction on satellite images. Among the available alternatives, deep convolutional neural network (ConvNet) is the state of the art method. Although previous studies have reported successful attempts on developing and implementing ConvNet on remote sensing application, several issues are not well explored, such as the use of depthwise convolution, final pooling layer size, and comparison between grayscale and RGB settings. The objective of this study is to perform analysis to address these issues. Two feature learning algorithms were proposed, namely ConvNet as the current state of the art for satellite image classification and Gray Level Co-occurence Matrix (GLCM) which represents a classic unsupervised feature extraction method. The experiment demonstrated consistent result with previous studies that ConvNet is superior in most cases compared to GLCM, especially with 3x3xn final pooling. The performance of the learning algorithms are much higher on features from RGB channels, except for ConvNet with relatively small number of features.

The plant microbiome plays an important role in plant biology, ecology, and evolution. While recent technological developments enabled the characterization of plant-associated microbiota, we still know little about the impact of different biotic and abiotic factors on the diversity and structures of these microbial communities. Here, we characterized the structure of bacterial microbiomes of fruits, leaves, and soil collected from two olive genotypes (Sinopolese and Ottobratica), testing the hypothesis that plant genotype would impact each compartment with a different magnitude. Results show that plant genotype differently influenced the diversity, structure, composition, and co-occurence network at each compartment (fruits, leaves, soil), with a stronger effect on fruits compared to leaves and soil. Thus, plant genotype seems to be an important factor in shaping the structure of plant microbiomes in our system, and can be further explored to gain functional insights leading to improvements in plant productivity, nutrition, and defenses.

Abstract Background Papillary renal cell carcinoma (PRCC) is the second most common adult kidney cancer histology, constituting 15-20% of cases. While some patients may have indolent PRCC tumors that grow slowly, other tumors rapidly metastasize. Some PRCC tumors respond to immune checkpoint inhibitors. Thus, understanding the immune tumor microenvironment and how it correlates with patient outcomes in this relatively rare disease is a critical need. Spatial interrogation of patient samples has the potential to offer novel insights into the tumor-immune axis and provide avenues for enhanced diagnosis and treatment. Addressing questions of spatial arrangement within tumors has remained a technical and biological challenge, but emerging spatial biology technologies provide molecular data at single cell resolution. We hypothesized that the spatial interaction of immune, stromal and tumor cells would be prognostic for PRCC patients. Methods A tissue microarray was assembled from an archive of ~100 patients presenting with PRCC. This dataset was assayed with PhenoCycler/CODEX (Akoya Biosciences) using a 31-antibody panel with immune- and cancer-related proteins. We have developed methodology and novel algorithms to perform signal normalization, cell segmentation, and cell typing. We computed neighborhoods for each of 2.5 million cells and performed network analysis to identify spatial clusters. This method allowed us to identify clusters consistently present across multiple TMA spots from the same patient and across multiple patients. Results Using spatial neighborhood analysis, we have identified diverse spatial clusters of potential clinical relevance, including five distinct M2 macrophage spatial clusters. We have described one of the clusters as being physically associated with helper T cells, which is visualized on a PRCC spot and shows co-occurence of M2-macrophages (CD163) and helper T cells (CD4). This pattern was replicated in additional TMA spots from the same patient. In contrast, other clusters of M2-macrophages (M2-M2 spatial cluster and M2-M1 macrophages) are visualized to show vastly different cellular neighborhoods. Clinical associations show that the patients with presence of the M2-T helper cluster have a poor cancer-associated survival (p=0.005). In comparison, the total proportion of M2 macrophages is not associated with survival (p=0.4), highlighting the importance of characterizing spatial interactions beyond cell type quantitation. Conclusions In summary, we highlight the utility of spatial biology to explain the heterogeneity in patients’ tumors and to uncover novel correlates of clinical phenotypes, establishing a platform for future discovery in this field and the identification of additional spatial correlates of patient outcome and clinical response. CDMRP DOD Funding: yes

Introduction: Black cumin (Nigella sativa) is widely used to treat various diseases. It is also believed to relief skin conditions accompanied by itching symptom, such as atopic dermatitis (AD) or eczema. However, the anti-eczema mechanism of action is still unclear. The aims of this syudy was to identify anti-eczema mechanism of action of N. sativa for AD using computer aided prediction and pathway analysis based on protein-chemical networks. Methods: We utilized dataset consisting chemical compounds of N. sativa from KNApSAcK. It is a comprehensive species-metabolite relationship database. Using canonical SMILES strings that encode molecular structures of each compound, we predicted the probabilities of activity (Pa) for anti-eczema effect based on PASS algorithms. The compounds with Pa >0.7 were included for pathway analysis based on protein-chemical interaction networks in STITCH database. We selected interactomes built by experimental data, gene co-expression, closest gene position, fusion, co-occurence, computational prediction, and other secondary data. Results: Thirty-five active compounds of N. sativa have been utilized and 19 of them have potential anti-eczema effects. Oleic acid and lauric acid were predicted with Pa-value of 0.947 and 0.920 for anti-eczema effect, respectively. However, only lauric acid was confirmed having a plausible mechanism of action via LY96-TLR4- PIK3R1 pathway for lipopolysaccharide receptor activity (false discovery rate [FDR] = 0.0243) and low-density lipoprotein particle receptor binding (FDR = 0.0118). Conclusion: Lauric acid in N. sativa has potential antieczema effect to prevent relaps in AD patients by controlling opportunistic bacterial infection that aggravates itching symptom in this condition.

Abstract. The Asian monsoon system has been recognised as an important tipping element in Earth's climate. In this work, we apply recurrence networks, a recently developed technique for nonlinear time series analysis of palaeoclimate data, for detecting episodes with pronounced changes in Asian monsoon dynamics during the last 10 ka in speleothem records from 10 caves covering the major branches of the Asian monsoon system. Our methodology includes multiple archives, explicit consideration of dating uncertainties with the COPRA approach and rigorous significance testing for the coexistence of monsoonal regime shifts at multiple locations to ensure a robust detection of continental-scale changes in monsoonal dynamics. This approach enables us to identify several epochs characterised by nonlinear regime shifts in Asian monsoon variability (8.5–8.0, 5.7–5.4, 4.1–3.6 and 2.8–2.2 ka BP), the timing of which suggests a connection to high-latitude Bond events and other episodes of Holocene rapid climate change (RCC). Interestingly, we also observe a previously unnoticed episode of significantly increased regularity of monsoonal variations around 7.3 ka BP, a timing that is consistent with the typical 1.0–1.5 ka return intervals of Bond events. A possible solar forcing of the detected nonlinear regime shifts in Asian monsoon dynamics is suggested by their co-occurence with pronounced minima and strong variability in solar activity. Drawing on a comprehensive review of the Holocene archeological record in the Asian monsoon realm, we find that these regime shifts partly coincide with known major periods of migration, pronounced cultural changes, and the collapse of ancient human societies. These findings indicate that also future transitions in monsoonal dynamics could induce potentially severe socio-economic impacts of climate change.

The advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face with a corresponding mobile phone based on the heterogeneous trajectories. We propose a framework which associates face trajectories with their corresponding mobile phone trajectories using elaborate and explainable features. Our solution includes two stages: an initial selection of phone trajectories for a given face trajectory and a subsequent identification of which phone trajectory provides an exact match to the given face trajectory. In the first stage, we propose a Multi-Granularity SpatioTemporal Window Searching (MGSTWS) algorithm to select candidate mobile phones that are spatiotemporally close to a given face. In the second stage, we first build an affinity function to score face–phone trajectory point pairs selected by MGSTWS, and construct a feature set for building a face–phone trajectory matching determinator which determines whether a phone trajectory matches a given face trajectory. Our well-designed features guarantee high model simplicity and interpretability. Among the feature set, BGST intelligently leverages disassociation between a face and a mobile phone even if there exists some co-occurence for a non-matching face–phone pair. Based on the feature set, we represent the face–phone matching task as a binary classification problem and train various models, among which LightGBM achieves the best performance with 92.6% accuracy, 96.9% precision, 88.5% recall, and 92.5% F1. Our framework is acceptable in most application scenarios and may benefit some downstream tasks. The preselection-refining architecture of our framework guarantees the applicability and efficiency of the face–phone trajectory pair matching frame.

La diversité, la composition et les assemblages du microbiome du sol et des plantes sont conditionnés en partie par l’environnement et les interactions biotiques. L’oasis d’AlUla, située dans le désert d’Arabie Saoudite, est caractérisé par de fortes contraintes abiotiques, liées à un pH hyper alcalin et à la faible disponibilité en eau et en nutriments. L’activité et la croissance des organismes est donc soumise à cette disponibilité ponctuelle et spatialement hétérogène des ressources. En résulte une répartition spatiale discontinue des végétaux, les îlots de fertilité, qui influencent la composition du sol et des communautés microbiennes. Les plantes sont également associées dans des relations symbiotiques à des microorganismes, qui vont influencer leur santé, leur résistance à la sècheresse, améliorer l’acquisition des ressources minérales et en eau, et sont particulièrement critiques dans ces milieux à fortes contraintes. Enfin, ces écosystèmes, déjà fragilisés par le changement climatique, sont également soumis à des pressions agricoles importantes qui conduisent à une dégradation des sols et de leur biodiversité associée. La biodiversité des écosystèmes chauds et arides est aujourd’hui peu connue, particulièrement concernant l’Arabie Saoudite, un pays aux frontières longtemps restées fermées.Le but de cette thèse est de caractériser le microbiome bactérien et fongique du sol et des plantes, en lien avec son environnement, et sa réponse à différents usages des terres par le biais de séquençage d’amplicons. Afin de dépasser le cadre analytique de l’étude de la diversité et de la composition des communautés, j’ai cherché à utiliser les métriques des réseaux de co-occurrence et explorer de nouvelles méthodologies pour leur étude. Dans un premier chapitre, l’influence mutuelle des plantes, du sol et des microorganismes dans un micro-environnement, les îlots de fertilité, est caractérisée. Puis, une campagne d’échantillonnage sur le terrain au cours de deux saisons m'a permis de réaliser une analyse extensive du microbiome des plantes par l’approche des réseaux de co-occurrence. En se focalisant sur les relations intra- et inter-royaumes microbiens des taxons symbiotiques, il y est démontré la redondance de l’assortativité des champignons mycorhiziens, et de l’intégration des bactéries fixatrices d’azote dans le microbiome étendu des plantes. La réponse du microbiome à un événement simulé de précipitation sur le terrain a également permis de caractériser la réponse taxonomique du microbiome à la disponibilité en eau dans le sol. Dans un troisième chapitre, la réponse du microbiome face à des contingences historiques de nature anthropique ou naturelle, décrit comment des cycles de dessiccation et d’inondation affectent les communautés microbiennes contemporaines. Par l’étude de la stabilité de leur interaction, il y est montré comment l’activité agricole passé impacte de manière durable la structure du microbiome. Enfin, la quantification des processus d’assemblage des communautés a permis de déterminer l’effet de perturbations anciennes sur les processus de sélection bactérienne et fongique.Collectivement, les résultats de cette thèse permettent d’améliorer la compréhension de l’assemblage et de la structure du microbiote du sol et des plantes dans un écosystème désertique encore peu connu. De plus, les analyses de cooccurrence se sont révélées un outil précieux dans la formulation de nouvelles hypothèses fondamentales sur le rôle fondateur des symbioses, et de la réponse du microbiote aux perturbations. La poursuite de l’étude de la stucture complexe des réseaux, complémentée par l’étude des fonctions microbiennes et d’approches réductionnistes pour lier les relations de covariance aux processus écologiques promet de prochaines avancées majeures en écologie microbienne
The diversity, composition and assemblages of the soil and plant microbiome are partly determined by the environment and biotic interactions. The AlUla oasis, located in the Saudi Arabian desert, is characterized by strong abiotic constraints, linked to a hyper-alkaline pH and low availability of water and nutrients. The activity and growth of organisms is therefore subject to this punctual and spatially heterogeneous availability of resources. This leads to a spatially discontinuous distribution of plants, known as islands of fertility, which influence soil composition and microbial communities. Plants also form symbiotic relationships with microorganisms, which influence their health, resistance to drought and the acquisition of mineral and water resources, and are particularly critical in this ecosystem. Finally, these ecosystems, already weakened by climate change, are also subject to significant agricultural pressures, leading to soil degradation and associated biodiversity loss. Little is known at present about the biodiversity of hot, arid ecosystems, particularly in Saudi Arabia, a country whose borders have long remained closed.The goal of this thesis is to characterize the bacterial and fungal microbiome of soil and plants in relation to its environment, and its response to different land uses through amplicon sequencing. In order to extend the analytical framework of the study of community diversity and composition, I have sought to use co-occurrence network metrics and explore new methodologies for their study. In a first chapter, the mutual influence of plants, soil and microorganisms in a micro-environment, the fertility islands, is characterized. Then, a field sampling campaign over two seasons enabled me to carry out an extensive analysis of the plant microbiome using the co-occurrence network approach. Focusing on the intra- and inter-kingdom relationships of symbiotic taxa, this work demonstrated the redundant assortativity of mycorrhizal fungi, and the integration of nitrogen-fixing bacteria into the extended plant microbiome. The microbiome's response to a simulated precipitation event in the field was also used to characterize the microbiome's taxonomic response to water availability in the soil. In the third chapter, the microbiome's response to historical contingencies of an anthropogenic or natural nature, describes how cycles of desiccation and flooding affect contemporary microbial communities. By studying the stability of their interactions, it shows how past agricultural activities has had a lasting impact on the structure of the microbiome. Finally, the quantification of community assembly processes has made it possible to determine the effect of past disturbances on bacterial and fungal selection processes.Collectively, the results of this thesis improve our understanding of the assembly and structure of soil and plant microbiota in a little-known desert ecosystem. In addition, co-occurrence analyses have proven to be a valuable tool in the formulation of new fundamental hypotheses on the founding role of symbioses, and the response of the microbiota to disturbance. Continued study of the complex structure of networks, complemented by the exploration of microbial functions and reductionist approaches to be able to couple covariance relationships to ecological processes, promises major advances in microbial ecology in the future