Plot Matrix Research Articles

Machine learning prediction of SCOBY cellulose yield from Kombucha tea fermentation

The commercialization of by-product of Kombucha SCOBY (Symbiotic consortium of bacteria and yeast) could be the sustainable way of transforming waste into value-added products. This study aims at developing a robust machine learning model for the prediction of SCOBY yield. Concentrations of tea, sucrose, SCOBY, inoculum, pH, temperature, and fermentation time were the input parameters considered. The robustness of the models was studied using correlation coefficient and root mean square error. Among the algorithms studied, XGB (eXtreme Gradient Boosting) was the most resilient model with high accuracy. By hyperparameter tuning and k-fold cross-validations, the model performance was improved to attain an R2 value of 0.9048. The relationship between variables was depicted as Pair plot and Pearson correlation matrix. Fermentation temperature was the most influential parameter affecting SCOBY yield. Shapley additive explanations dependence plots and summary plot provided insights on the combined effects of input parameters on the SCOBY yield.

Application of multivariate data analysis to biogeochemical exploration at the Twin Lakes Deposit, Monument Bay Gold Project, Manitoba, Canada

The Twin Lakes Deposit, located in the northwest of the Superior Province, is a promising Archean Greenstone-hosted orogenic gold deposit. Biogeochemical and statistical techniques were used for the first time in this area to assess the geochemical dispersion patterns of Au, As, Bi, Se, Sb, Tl, Fe, Co, Ni, Cr, Mo, Cd, Pb, Zn, Mn, K, P, B, Mg, Ca, Ba, and Cu in black spruce samples collected perpendicularly to the overall trend of shear-hosted gold mineralization. ICP-MS analysis of ashed black spruce bark, twigs, and needles has outlined a biogeochemical response of black spruce to the concealed mineralization. Most elements are accumulated preferentially in twigs and bark, while B, K, Mg, Mn, and P are highly concentrated in needles. High Au and Tl enrichments were identified in all sample types compared to the background level. The scatter plot matrix demonstrated a positive bivariate relationship between Au and Fe, Mg, Cu, Mo, and Zn; and horizontal to slightly negative correlation with Ca, K, Mn, P, and B. Investigations on the spatial distribution pattern of Au and its pathfinder elements using a robust log-transformed inverse distance weighted (IDW) interpolation method revealed the presence of zones of gold enrichment in the north to the northwest of the survey, where Au is accompanied by Tl-As-Cd > Bi, Pb, Se > Sb. According to the Robust RQ-mode principal component analysis (PCA), two main components control the distribution of elements in black spruce, including (1) a geological factor that mostly reflects bedrock composition; and (2) a physiological factor that reflects biological processing of essential elements. Of importance is that Au and its pathfinder elements, including As, Se, Tl, and to a lesser extent, Bi, Cd, and Pb were well-separated as a subgroup of the geological factor with a clear spatial relationship to gold mineralization. This study demonstrates a biogeochemical response in black spruce is associated with gold mineralization at the Monument Bay Gold Project using multivariate statistical methods. The results of this study are promising and can be extrapolated to wider surveys across the Monument Bay Gold Project to vector toward new gold mineralized zones or extensions of the Twin Lakes gold deposit. The results also provide a suite of gold pathfinder elements that support exploration efforts and regional target generation.

Актуалізація класичного тексту в жанрі римейку: телесеріал «Спіймати Кайдаша» (2020)

According to U. Eco, the notions of reproduction and repetition, in the sense of the dominant specific form of postmodern and postpostmodern discourse, manifest themselves in a number of genre types, where the genre-creating dominant is the reliance on a well-known classical sample. Hence, the article suggests a theoretical discourse regarding the concept remake. It outlines the boundaries of cinematic reception of the texts by the Ukrainian classic I. Nechuy-Levytsky: «Mykola Dgeria» (1927), «Vasylyna» (1928, based on the story «Burlachka»), «Chasing Two Hares» (based on the comedy «On Kozhumiaky», 1961), «Kyiv Beggars» (1992), «Kaidash’s Family» (1993), «To Catch the Kaidash» (2020). The article surveys the genre specificity of the TV series «To Catch the Kaidash» (2020) by screenwriter and producer Natalia Vorozhbyt and director Oleksandr Timenko, it also identifies the markers of its success. The film text preserves the plot matrix of the novel «Kaidash’s Family» (1878) by I. Nechuy-Levytsky as much as possible. At the same time, the classical plot is definitely modernized and acquires new social accents: here the familiar «traditional images» and «eternal questions» are creatively rethought. The primary source is updated not only at the level of chronotope, personosphere, plotting and historical context but also at the level of style paradigms of the new era. Significant modifications of the epic plot also occur at the genre level. The film-makers have introduced additional plot fragments with the participation of new characters, in this way significantly increasing the volume (the original text of one hundred and fifty pages turned into a twelve-part mini-epic). This expanded the problems of the prototext, while due to the acting, the psychological disclosure of the inner world of the characters became more expressive. The social-everyday satirical-humorous story-chronicle (transforming in relation to the specific dimensions of another art form) turned into a successful «tragicomic» cinema novel. The article claims that the existing resource of «Kaidash’s family» has shown a tendency to be updated in time and space by parodying stereotypes, to some extent – due to the demythologization and desacralization of the usual cultural codes. The remake «To Catch the Kaidash» confirmed the potential resource of the original text, the possibility of its successful re-interpretation.

Morpho-molecular Characterization of Maize Inbred Lines Accelerating Parental Selection for Hybridization

Characterization of genetic diversity is the foundation step for crop improvement, which provides a basis for analyzing combining ability and heterosis of inbred genotypes during a hybridization program. An investigation was carried out at the field laboratory of the Genetics and Plant Breeding department in Bangabandhu Sheikh Mujibur Rahman Agricultural University, to elucidate the genetic architecture by evaluating 12 morphological and 4 molecular (SSR) markers within 52 diverse S1 genotypes, and to assess the relationship of molecular and morphological GD. An almost equal amount of PCV and GCV coupled with high heritability and genetic advance for the traits cob weight (gm), NKPC, and NKPR lead to the selection of promising genotypes based on these characters. Correlation coefficient and scatter plot matrix established a positive and strong relationship of KL (mm), KW (mm), and KT (mm) with 100 kernel weight (gm) suggesting the importance of kernel morphology. Mahalanobis D2 statistics revealed the highest inter-cluster distance between I and II. The percentages of molecular variance within the population and among the population were 76% and 14 %, respectively. The optimum K-value was 5. Heatmap relying on molecular GD exposed MMIL-28, MMIL-54, and MMIL-96 as the most diverse lines. SHE analysis hypothesized the increase of richness and diversity over time. Less correlation between the divergence generated from morphological traits and molecular markers suggested that the morphological variation may be determined by environmental factors and also by genetic factors. A strategy for the effective selection of predicting parental lines for a future hybridization program was developed.

Implikasi Keberadaan Hutan Negara terhadap Perkembangan Desa di Kecamatan Segah Kabupaten Berau Provinsi Kalimantan Timur

The state forest existence is expected to provide maximum benefits for the people's prosperity. This study identified the forest state existence through a land tenure structure in Segah District Berau Regency East Kalimantan Province. It also described a portrait of rural development including the condition of infrastructure and community income/expenditure, and identified the influence of state forest existence on rural development. The methods used to analyze the structure of land tenure by overlaying land cover maps and boundaries of area functions in each village, then classifying villages using a matrix of plots based on the percentage of forest and forest area. Analysis of rural development was carried out using scalogram analysis and oneway ANOVA. Meanwhile, the analysis of the influence of state forest existence on rural development was carried out using descriptive analysis and multiple linear regression. The results of this study showed that the land cover of the forest and land tenure of forest area were dominant. This means that the land tenure structure in the administrative area of Segah District was dominated by state forest. Rural development in terms of infrastructure was found in a condition that was quite even lacking of the needs of the rural population, while average household expenditure is above the national average minimum expenditure limit. The state forest existence was indicated to have a role in influencing the development of rural infrastructure and household expenditure, especially in the activity of entering the forest which can reduce the expenditure of primary household needs.

Unraveling the interaction of an opium poppy alkaloid noscapine ionic liquid with human hemoglobin: Biophysical and computational studies

Apart from a wide range of applications, nowadays, Ionic liquids (ILs) are showing their major importance in the field of pharmaceuticals for the development of effective therapeutics. Along with them, proteins are the major targets of a drug and a little alteration in their conformation can lead them to act differently. In the present study, a step has been taken towards the binding of a noscapine (Nos) based ionic liquid [Pip-Nos]OTf with human hemoglobin (Hb), as the blood plasma has a special role in the transport and targeting of a drug. Noscapine, is a phthalideisoquinoline alkaloid, derived from an opium poppy (Papaver Somniferum) having various biological properties. UV–Vis absorption spectroscopy, steady state fluorescence spectroscopy, circular dichroism (CD) and computational studies have been used to study the molecular interaction of [Pip-Nos]OTf with human hemoglobin. UV–Vis spectra showed an increase in absorption intensity with a considerable red (bathochromic) shift on continuous addition of [Pip-Nos]OTf. A static quenching was observed with the formation of ground state complex in fluorescence spectroscopy. CD analysis showed no significant changes in the secondary structure of Hb on the addition of [Pip-Nos]OTf. It was found to bind strongly with hemoglobin in its catalytic groove with a high molecular docking score of −265.47 kJ/mol. Besides, molecular dynamics simulation studies delineate the stable and prolonged binding of [Pip-Nos]OTf to hemoglobin with minimal deviations in deformations plot and covariance matrix statistics. These favorable outcomes of stable binding of [Pip-Nos]OTf with hemoglobin, also depicted certain common spectroscopic and in silico features to the previously reported interaction of this ionic liquid with BSA and conclusively, showed its promising candidature as cancer therapeutics.

Modeling Eects of Climatic Variables on Tea Production in Kenya Using Linear Regression Model with Serially Correlated Errors

Aims/ Objectives: To formulated a linear regression model to capture the relationship between tea production and climatic variables in terms of ARIMA.Place and Duration of Study: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa, Nairobi, Kenya, between June 2019 and April 2021.Methodology: The study used time-series data for mean annual temperature, mean annual rainfall, humidity, solar radiation, and NDVI, collected from six counties, namely Embu, Kakamega, Kisii, Kericho, Meru, and Nyeri.Results: The study ndings noted that there is a presence of trend and seasonality for all the data. The scatter plot matrix for all the climatic variables for all the counties under the study indicated that tea production has a linear relationship with most climatic variables. Model t of the data indicated statistical signicance when tea production data is dierenced. A second linear model with tea production data deseasoned has mixed results in terms of a signicancetest. The variation of independent variables with tea production yielded very low values, suggesting that the data used has many variabilities.Conclusion: The study ndings show the climatic variables can be used to forecast tea production.Recommendation: Future studies may combine the analysis with other statistical modeling procedures such as the GARCH models.

Assessment of heavy metal pollution in Yamuna River, Delhi-NCR, using heavy metal pollution index and GIS

The present study was conducted on the river Yamuna, which passes through Delhi-NCR from Baghpat to Chhainssa, a distance of about 125 km, at six sampling locations to evaluate the concentrations of heavy metals in surface water using heavy metal pollution index (HPI) approach. The river serves both urban-industrial and rural areas in the study area; hence, domestic, industrial, and agricultural wastes are being contributed greatly in the contamination of river water. The Yamuna River is one of the major tributaries of the river Ganga originated in the Himalayas and is flowing through a varied geological terrain. Metals such as iron (Fe), copper (Cu), cobalt (Co), zinc (Zn), lead (Pb), cyanide (CN), nickel (Ni), and chromium (Cr) in selected sites of Yamuna River water were determined by using atomic absorption spectrophotometer. The concentrations of Fe, Cu, Co, Zn, Pb, CN, Ni, and Cr in the river water were found to be in the range of 40–190, 50–120, 4–66, 840–1800, 2–40, 100–600, 88–253, and 35–52 μg/L, respectively. The results show that the maximum heavy metal content was found at sampling site S3 (Nizamuddin) followed by S6 (Chhainssa), S4 (Okhla), S1 (Baghpat), S5 (Manjhawali), and S2 (Pachahira). The heavy metal data was integrated in GIS environment for preparing spatial distribution maps of sampling sites. A scatter plot matrix was created to assess the pattern and interrelationships between heavy metals. The average concentration of heavy metals was recorded high, often exceeding the permissible limits for drinking of surface water prescribed by the Bureau of Indian Standards (BIS) and World Health Organization (WHO). Based on HPI (varies from 98.2 to 555.1), about 85% of the river water was classified as highly polluted; hence, it is not recommended for drinking. Overall, significant variations were observed in concentrations of heavy metals from one location to the other which may be because of toxic industrial effluents and domestic sewage wastes being added to the river water by various anthropogenic activities in the study area. The present work highlights the pollution load of heavy metals in the river Yamuna and also advocates an urgent attention towards minimizing the health risk of people residing not only along the river banks and surrounding regions but also for city population.

GENRE FEATURES OF HORROR LITERATURE

The article is dedicated to the study of the genre features of horror literature, its plot matrices and principles of artistic modeling of pictures of the world. The article describes the genre-creating elements that have become basis for systematization of the genre composition of horror literature. Special attention is paid to the typology of characters, the specifics of the arranging artistic time and space, as well as the features of the emotional impact on the reader as one of leading principles of genre classification of this type of fiction. After all, much of the text of the works of representatives of horror literature is devoted to the construction of artistic space and time, in order to introduce the recipient into a certain range of components of the emotional state during reading, to achieve the goal of horror literature. Namely, to scare the reader. Considerable attention is paid by the authors of this type of literature to the modeling of characters as a genre component. The article describes a set of standardized elements and attributes describing the appearance and character of the characters, which creates a "horizon of expectations" for the reader's perception. It also acts as an element of genre differentiation within the cluster of horror literature. The definition of genre varieties of this type of fiction reveals a clear conditionality of the specifics of the reception, the orientation of the author's intentions to create a certain psychological effect. Established artistic constants, which show a fairly high level of variability, serve as a figurative and plot matrix for the authors, which provides a "recognizability" of the genre addressed to a certain readership.

Bat community response to intensification of biomass production for bioenergy across the southeastern United States.

Human demand for food, fiber, and space is accelerating the rate of change of land cover and land use. Much of the world now consists of a matrix of natural forests, managed forests, agricultural cropland, and urbanized plots. Expansion of domestic energy production efforts in the United States is one driver predicted to influence future land-use and land management practices across large spatial scales. Favorable growing conditions make the southeastern United States an ideal location for producing a large portion of the country's renewable bioenergy. We investigated patterns of bat occurrence in two bioenergy feedstocks commonly grown in this region (corn, Zea mays, and pine, Pinus taeda and P. elliottii). We also evaluated potential impacts of the three major pathways of woody biomass extraction (residue removal following clearcut harvest, short-rotation energy plantations, and mid-rotation forest thinning) to bat occurrence through a priori land-use contrasts. We acoustically sampled bat vocalizations at 84 sites in the Southeastern Plains and Southern Coastal Plains of the southeastern United States across three years. We found that mid-rotation thinning resulted in positive effects on bat occurrence, and potential conversion of unmanaged (reference) forest to managed forest for timber and/or bioenergy harvest resulted in negative effects on bat occurrence when effects were averaged across all species. The effects of short-rotation energy plantations, removal of logging residues from plantation clearcuts, and corn were equivocal for all bat species examined. Our results suggest that accelerated production of biomass for energy production through either corn or intensively managed pine forests is not likely to have an adverse effect on bat communities, so long as existing older unmanaged forests are not converted to managed bioenergy or timber plantations. Beyond bioenergy crop production, mid-rotation thinning of even-aged pine stands intended for timber production, increases to the duration of plantation rotations to promote older forest stands, arranging forest stands and crop fields to maximize edge habitat, and maintaining unmanaged forests could benefit bat communities by augmenting roosting and foraging opportunities.

Experimental Selection of Machine learning Techniques and Image features to Detect “Cactus” Diseases

Image is a very important data in machine learning. In order to select better features, feature extraction techniques and classifiers, intensive experiments are taken place using data. In this work, best feature, feature extraction technique and machine learning classifier are selected experimentally. Hence, bag of features were the best features experimentally out of color, texture and bag of features. Of color histogram, bag of features and GLCM (Gray-level co-occurrence matrix), bag of features extraction technique is found to be the best one experimentally. Of the machine learning classifiers shown in the scatter plot and confusion matrix, linear support vector machine is selected and the achieved accuracy is 97.2%.

Generation of rainfall for Mosul city using statistical downscaling method (SDSM)

This study deals with the application of statistical downscaling method for precipitation. Daily historical rainfall data for Mosul city during the period (1986 - 2014) and the National Centre for the Environmental Prediction (NCEP) re-analysis data are used to calibrate the model. The scatter plot and correlation matrix are used to investigate the degree of association between the selected predictors and predictand. Prior model calibration the fourth root power transform facility is selected for daily precipitation in order to produce none linear regression model. The results obtained from Statistical Downscaling Method (SDSM) model were compared with the observed data. The simulation from Hadly Centre climate model (Had-CM3) emission scenario have been used for future periods. The generation of future climate scenario used 10 years (2026-2035) of daily time scale under HadCM3 predictor data.

Relevance Analysis and Visualization of Students’ Employment and Their Courses Achievement

Exploring the relationship between college students’ employment and their academic performance is of great significance for optimizing talents training programs and curriculum system, assisting students’ academic planning and employment guidance. In this paper, we present an analysis method for exploring correlation between students’ employment and their academic achievements. Firstly, we divide the students into several groups according to the types of their employment. Then we use box plot matrix to express the distribution of compulsory course achievements for assisting the comparative analysis among different groups of students. In addition, we use the TF-IDF method based on distance weighting to calculate the preference of each group of students quantitatively on various elective courses, and use radar chart to display the results for assisting the analysis of the students’ behaviour in course selection. The method is applied to analyse the course achievement and employment data of students majoring in Computer Science at Beijing Technology and Business University, and some characteristics of the course achievement and course selection behaviour of the students in different groups are obtained. The analysis results show that the method is effective.

Nasseh method to visualize high-dimensional data

Today’s ever-increasing application of high-dimensional data sets makes it necessary to find a way to fully comprehend them. One of these ways is visualizing data sets. However, visualizing more than 3-dimensional data sets in a fathomable way has always been a serious challenge for researchers in this field. There are some visualizing methods already available such as parallel coordinates, scatter plot matrix, RadViz, bubble charts, heatmaps, Sammon mapping and self organizing maps. In this paper, an axis-based method (called Nasseh method) is introduced in which familiar elements of visualization of 1-, 2- and 3-dimensional data sets are used to visualize higher dimensional data sets so that it will be easier to explore the data sets in the corresponding dimensions. Nasseh method can be used in many applications from illustrating points in high-dimensional geometry to visualizing estimated Pareto-fronts for many-objective optimization problems.

Null model analyses of temporal patterns of bird assemblages and their foraging guilds revealed the predominance of positive and random associations.

Patterns of species associations have been commonly used to infer interactions among species. If species positively co‐occur, they may form predominantly neutral assemblages, and such patterns suggest a relatively weak role for compensatory dynamics. The main objective of this study was to test this prediction on temporal samples of bird assemblages (n = 19, 10–57 years) by the presence/absence and quantitative null models on assemblage and guild levels. These null model outcomes were further analyzed to evaluate the effects of various data set characteristics on the outcomes of the null models. The analysis of two binary null models in combination with three association indices revealed 20% with significant aggregations, 61% with random associations, and only 19% with significant segregations (n = 95 simulations). The results of the quantitative null model simulations detected more none‐random associations: 61% aggregations, 6% random associations, and 33% segregations (n = 114 simulations). Similarly, quantitative analyses on guild levels showed 58% aggregations, 20% segregations, and 22% random associations (n = 450 simulations). Bayesian GLMs detected that the outcomes of the binary and quantitative null models applied to the assemblage analyses were significantly related to census plot size, whereas the outcomes of the quantitative analyses were also related to the mean population densities of species in the data matrices. In guild‐level analyses, only 9% of the GLMs showed a significant influence of matrix properties (plot size, matrix size, species richness, and mean species population densities) on the null model outcomes. The results did not show the prevalence of negative associations that would have supported compensatory dynamics. Instead, we assume that a similar response of the majority of species to climate‐driven and stochastic factors may be responsible for the revealed predominance of positive associations.

Information visualisation for efficient knowledge discovery and informed decision in design by shopping

ABSTRACTDesign space exploration (DSE) describes the systematic activity of discovery and evaluation of the elements in a design space in order to identify optimal solutions by reducing the design space to an area of performance. Designers sample thousands of design points iteratively, explore the design space, gain knowledge about the problem and make design decision. The literature tells us that DSE results in a decision of quality called informed decision, which is supported by information visualisation. The representation of design points is seen as primordial to gain an understanding of the problem and make an informed decision. In our work, we have sought to identify what type of graph is best suited to the discovery phase, and enables designers to make an informed decision. We designed a web platform with four design problems, and carried out an experiment with 42 participants. We found a graph that was better suited to making a decision of quality and to gaining greater understanding: the scatter plot matrix.

Magnetic resonance spectroscopy imaging in diagnosis of suspicious prostate cancer: A meta-analysis.

Background:This meta-analysis was conducted to assess the value of magnetic resonance spectroscopy imaging (MRSI) in the diagnosis of suspected prostate cancer (PC).Methods:We identified all the relevant papers from the EMBASE, PubMed, EBSCO, Web of Science, and Cochrane Library databases and screened the reference lists. The quality assessment of diagnostic accuracy studies-version 2 tool was used to assess the study quality. Publication bias was analyzed using Deeks’ funnel plot asymmetry test. We calculated the pooled sensitivities, specificities, positive likelihood ratios, negative likelihood ratios, diagnostic odds ratio (DOR), and 95% confidence intervals. The results were evaluated by summary receiver-operating characteristic curves (SROCs). Ultimately, a univariable meta-regression and subgroup analysis, Fagan plot, and likelihood matrix were used to analyze this review.Results:A total of 19 articles, which were based on patient-level analysis of PC, were included. These studies had a pooled sensitivity, specificity, DOR, and an area under the SROC of 0.86, 0.78, 22, and 0.89, respectively, by patient-level analysis. From the likelihood matrix, the summary negative likelihood ratio and positive likelihood ratio for MRSI diagnosis of PC were concentrated on the right lower quadrant, which neither confirmed nor excluded the diagnosis of cancer.Conclusion:MRSI has a relative application value in the diagnosis of cases of suspected PC. While MRSI is still required for diagnosis along with other clinical data and comprehensive analysis.

Multivariate visualization of particle data

In this review paper, we review methods for interactive particle rendering techniques, multi-view particle visualization systems, multivariate visualization techniques, and methods for correlation visualizations. Visualization is vital for gaining insight into particle data. Multivariate particle data are generated to understand different aspects of the underlying physics. The visualization of multivariate particle data is typically performed in multiple linked view systems (multi-view systems) that render particles of interest that are selected by the user interactively with brushing-and-linking. To this end, the non-spatial aspects of particles are explored with multivariate visualization methods, e.g., scatter plots, scatter plot matrix, parallel coordinates, dimensional reduction and radial plots.

Interactive Visualization of Large-scale Design Data using Scatter Plot Matrix and Parallel Coordinate plot

Interactive Visualization of Large-scale Design Data using Scatter Plot Matrix and Parallel Coordinate plot

Stage II-III colon cancer: a comparison of survival calculators.

Individualized postoperative survival calculators for patients with cancer can be an aid for predicting prognosis and clinical decision making, such as the use of adjuvant chemotherapy. The aim of this study was to compare existing survival calculators for colon cancer and determine their performance using an independent cohort of patients. A retrospective analysis of a multi-site institutional experience was performed on patients diagnosed with stage II-III colon cancer between January 2012 and March 2013. Patient survival rates were estimated using Roswell Park Comprehensive Cancer Center (RPCCC), Memorial Sloan Kettering Cancer Center (MSKCC), and MD Anderson Cancer Center (MDACC) calculators. These calculators vary in the number and breadth of variables that are included. The agreement between selected models was obtained through a scatter plot matrix and related intra-class correlation coefficient (ICC). Calculators' performances were compared using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) values. After the application of inclusion and exclusion criteria, a total of 97 patients were included in the analysis. Survival data were available for all patients. Median follow-up was 57.6 months, and the overall 5-year survival rate was 0.74 (95% CI: 0.64-0.82). Overall, the different calculators tended to predict survival similarly (ICC =0.017). However, there was variation among calculator performance with the RPCCC calculator showing the highest performance (AUC =0.913), followed by the MSKCC calculator (AUC =0.803), and the MDACC calculator (AUC =0.644). Prognostic models incorporating a more comprehensive amount of patient and tumor specific variables may provide a more accurate estimate of individual patient survival rates. These tools can be an actual aid in the clinical practice, allowing physicians to personalize treatment and follow-up for patients with colon cancer.