Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

BackgroundTransient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement.ResultsThe presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions.ConclusionCurrent methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

Construction of potential natural vegetation (PNV) poses particular challenges in landscapes heavily altered by human activity and must be based on transparent, repeatable methods. We integrated the concept of ancient forest (AF) and ancient forest species (AFS) into a four-step procedure of PNV mapping: 1) classification of forest vegetation releves; 2) selection of those vegetation types that can serve as PNV units, based on AF and AFS; 3) merging of selected vegetation types into five PNV units that can be predicted from a digital morphogenetic soil map; 4) mapping of three additional PNV units based on additional environmental data. The second step, concerning the selection of reference forest vegetation, is of particular interest for PNV construction in Flanders (northern Belgium), where forest cover has been subject to temporal disruption and spatial fragmentation. Among the variety of extant forest recovery states, we chose as PNV units those vegetation types for which a high proportion of releves had been located in AF and that contained many AFS. As the frequency of AFS depends on site conditions, we only compared and selected vegetation types that are found on similar sites according to average Ellenberg indicator values. While succession is irrelevant for the definition of PNV, colonization rates of AFS can be used to estimate the time required for PNV to be restored in a site.

We performed a gap analysis of protected area networks in Italy to assess the representativeness of potential natural vegetation (PNV) types of the European Natura 2000 network compared with the National Protected Area network. In this context, the PNV map, reflecting the diversity and spatial arrangement of the natural terrestrial ecosystems, can be considered as an appropriate proxy of environmental and biogeographical diversity of Italy. In this country, 775 protected areas are registered in the Official List of Protected Areas (OLPA), 2281 sites are listed as Sites of Community Interest and 590 as Special Protection Areas, constituting the Natura 2000 network. The adopted conservation target considered that any PNV type included for less than the 10% in the PAs network (OLPA, Natura 2000) was defined as a gap in the system. In particular we defined four categories of PNV protection: any PNV types with a representation of less than 10% in both the OLPA and N2000 was defined as a “total gap” (i.e., under-protected); any PNV type with a representation of less than 10% in either the OLPA or the N2000 was defined as a “partial gap”; any PNV type with a representation of between 10 and 50% in both the OLPA and N2000 was defined as “protected”; lastly, any PNV type with a representation of more than 50% in both the OLPA and N2000 was defined as “widely-protected”. Digital overlays of PNV and PAs networks were separately performed and statistics produced, indicating the current state of protection of Potential Natural Vegetation types in the two networks (OLPA and Natura 2000). We found that more than 59% of PNV types recognized on the Italian territory is not protected by the OLPA network. On the contrary, regarding Natura 2000 network, 68% of PNV types are protected, accounting for 27% more than OLPA. Compared to the National network of OLPA, the European network Natura 2000 is characterized by a larger percentage of territory in terms of area (18% of the Italian territory for Natura 2000 vs 10% of OLPA) but also by a smaller size of the sites, allowing for a more coherent distribution and efficiency in the protection of habitat remnants (68% PNV types protected by Natura 2000 vs 41% by OLPA). The proposed PNV approach can help guiding decisions on where and how to spend scarce conservation management resources.

The potential natural vegetation (PNV) is a tool for landscape planning, nature preservation and the assessment of naturalness. It is mostly constructed by the application of expert knowledge. This paper shows the advantages of using a more sophisticated and formalized PNV construction that overlays vegetation types and site factor maps by applying a Bayes model and herewith improving existing PNV maps solely based on expert knowledge. The investigation was conducted in the forest complex of the Bavarian Forest National Park (Germany) and the adjacent Sumava National Park (Czech Republic). The project reached two major results: (1) The existing heterogeneous country-specific databases of natural site conditions and of vegetation types could be adapted to each other to construct a solid scientific basis to deduce a PNV map. The habitat requirements of the occurring harmonized vegetation types can now be quantitatively described in a formalized way. (2) The combination of terrestrial PNV mapping and numerical modeling allows the synthesis of the views of the different experts that generated the maps used for model calibration. However, the modeled map loses the details of the expert-based map that cannot be derived from the underlying site maps. A common modeled PNV map of both national parks covering an area of about 92,000 ha was created. While the former expert-based PNV maps display breaks along the country border, the modeled PNV presents a harmonized view based on the common database of both national parks.

Fire is a major factor shaping the distribution of vegetation types. In this study, we used a recent high resolution map of potential natural vegetation (PNV) types and MODIS fire products to model and investigate the importance of fire as driver of vegetation distribution patterns in Ethiopia. We employed statistical modeling techniques to estimate the distribution of fire and the PNVs under current climatic conditions, and used the calibrated models to project distributions for different climate change scenarios. Results show a clear congruence between distribution patterns of fire and major vegetation types. The effect of climate change varies considerably between climate change models and scenarios, but as general trend expansions of moist Afromontane forest and Combretum–Terminalia woodlands were predicted. Fire-prone areas were also predicted to increase, and including this factor in vegetation distribution models resulted in stronger expansion of Combretum–Terminalia woodlands and a more limited increase of moist Afromontane forests. These results underline the importance of fire as a regulating factor of vegetation distribution patterns, and how fire needs to be factored into predict the possible effects of climate change. For conservation strategies to effectively address conservation challenges caused by rapid climate shifts, it is imperative that they not only consider the direct influence of climate changes on the vegetation, species species, or biodiversity patterns, but also the influence of future fire regimes.

Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

Due to the large number of factors in churn prediction, it is challenging to choose and provide a model to do churn prediction and find the most important factors to deal with. In this paper, multiple machine learning methods will be combined to be compared to strengthen the validity of prediction. More specifically, there are four ways listed in this paper, which are Logistic Regression, Decision Tree, Light Gradient Boosting Machine, Support Vector Machine, K-means, and Random forest. The data selected from Kaggle will be the datasets in this paper to be analyzed. The data is preprocessed by turning figures into numbers so that it can be analyzed by mathematics way like polynomial or linear process. In addition, the important factors in telecom churn are also included through machine learning. The score of different kinds of machine learning will be given to choosing the best model. Experimental results indicate that Support Vector Machine with linear kernel is the best model, and the most important factors are contract and tenure.

Recognition and prediction of individual thermal comfort requirement based on local skin temperature

Preoperative prediction of lymph node metastasis and prognosis in gastric cancer patients: A machine learning model based on 18F-FDG metabolic parameters.

The study investigated the impact of historical and future climate changes on potential natural vegetation (PNV) types and net primary productivity (NPP) in Australia, using the Comprehensive and Sequential Classification System model and the Miami model coupled with climate of the 1931–70 and 1971–2010 periods and the projected climate in 2050. Twenty-eight vegetation classes were classified based on the key climate indicators with four of them being the major vegetation classes corresponding to Australian rangelands and accounting for 75% of total land area. There was a substantial shift in areas of vegetation classes from the 1931–70 period to the 1971–2010 period due to the increased rainfall over large areas across Australia. The modelling projected a range of changes in vegetation classes for 2050 depending on the climate-change scenario used. Many vegetation classes with more intense land use (e.g. steppe and forest) were projected to decrease in 2050, which may have significant impact on the grazing industry and biodiversity conservation. By 2050, NPP was projected to increase in central and northern Australia and to decrease in southern and eastern coastal areas and was projected to be higher on average than that of the 1931–70 period. The vegetation classes approximately corresponding to Australian rangelands mostly had increased NPP projections compared with the 1931–70 period. Although actual response will partially depend on human management activities, fire and extreme events, the projected increase in average NPP in 2050 indicates that Australian vegetation, particularly the rangeland vegetation, will likely be a net carbon sink rather than a carbon source by 2050, with the exception of a ‘warm-dry’ scenario.

In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways. We obtained the aging-related genes of heart failure through WGCNA and CellAge database. We elucidated the biological functions and signaling pathways involved in heart failure and aging through GO and KEGG enrichment analysis. We used three machine learning algorithms: LASSO, RF and SVM-RFE to further screen the aging-related genes of heart failure, and fitted and verified them through a variety of machine learning algorithms. We searched for drugs to treat age-related heart failure through the DSigDB database. Finally, We use CIBERSORT to complete immune infiltration analysis of aging samples. We obtained 57 up-regulated and 195 down-regulated aging-related genes in heart failure through WGCNA and CellAge databases. GO and KEGG enrichment analysis showed that aging-related genes are mainly involved in mechanisms such as Cellular senescence and Cell cycle. We further screened aging-related genes through machine learning and obtained 14 key genes. We verified the results on the test set and 2 external validation sets using 15 machine learning algorithm models and 207 combinations, and the highest accuracy was 0.911. Through screening of the DSigDB database, we believe that rimonabant and lovastatin have the potential to delay aging and protect the heart. The results of immune infiltration analysis showed that there were significant differences between Macrophages M2 and T cells CD8 in aging myocardium. We identified aging signature genes and potential therapeutic drugs for heart failure through bioinformatics and multiple machine learning algorithms, providing new ideas for studying the mechanism and treatment of age-related cardiac decline.

Multiple machine learning (ML) algorithms were developed using artificial intelligence, including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN), to predict the yield of biodiesel production in an acid-catalyzed alcoholysis process using green seed canola oil. Catalyst loading, methanol-to-oil (M/O) molar ratio, and reaction time were considered as input parameters, while the yield of biodiesel production was selected as the output parameter. The performance of the developed ML models was assessed using evaluation metrics such as the coefficient of determination (R2) and the root mean squared error (RMSE). The R2 values obtained for LR, RF, DT, and KNN models were 0.80, 0.95, 0.97, and 0.84, respectively. Furthermore, the corresponding RMSE values for these models were 2.48, 1.51, 0.89, and 4.51, respectively. According to the results, the DT model exhibited superior accuracy and reliability for predicting biodiesel production compared to the other models. The values of the input variables to potentially yield the highest biodiesel output were identified through a systematic trial-and-error approach using the DT model. The results showed that a biodiesel yield of 88 % can be achieved with 5 wt% catalyst loading, a 22 M/O molar ratio, and a reaction time of 5 hours.

Worldwide, cancer is the most frequent cause of passing away for women. Any development in predicting and diagnosing cancer is crucial for a healthy life. As such, vital cancer accuracy in predicting patients' survival parameters and treatment aspects is necessary. Machine learning methods significantly impact breast cancer diagnosis and early diagnosis. This study aims to increase prediction accuracy using a novel statistical feature selection technique. This article examines the classification test accuracy, standard data precision, and the process performance of multiple machine learning (ML) algorithms, such as Random; using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the following models were used: Support Vector Machine, Logistic Regression, Decision Tree (C4.5), Forest Naïve Bayes (NB), Linear Regression (LR), k-nearest-neighbors (KNN), and Multilayer Perceptron (MLP). The data set is partitioned to use the machine learning algorithm: 20% is used during the test phase, while 80% is used throughout training. Hyper-parameters that are manually assigned are utilized to modify the classifier. When applied to a subset of data, it showed that combining SVM and model in machine learning reached the maximum accuracy of up to 90%, which was noticeably superior to the other ML model.