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PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262460
Author(s):  
Gifty E. Acquah ◽  
Javier Hernandez-Allica ◽  
Cathy L. Thomas ◽  
Sarah J. Dunham ◽  
Erick K. Towett ◽  
...  

With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97–0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0–100 mg kg-1 (R2 values were from 0.78–0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10–0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump.


Author(s):  
Xin-yu Li ◽  
Jian-xiong You ◽  
Lu-yu Zhang ◽  
Li-xin Su ◽  
Xi-tao Yang

Background: Necroptosis is a newly recognized form of cell death. Here, we applied bioinformatics tools to identify necroptosis-related genes using a dataset from The Cancer Genome Atlas (TCGA) database, then constructed a model for prognosis of patients with prostate cancer.Methods: RNA sequence (RNA‐seq) data and clinical information for Prostate adenocarcinoma (PRAD) patients were obtained from the TCGA portal (http://tcga-data.nci.nih.gov/tcga/). We performed comprehensive bioinformatics analyses to identify hub genes as potential prognostic biomarkers in PRAD u followed by establishment and validation of a prognostic model. Next, we assessed the overall prediction performance of the model using receiver operating characteristic (ROC) curves and the area under curve (AUC) of the ROC.Results: A total of 5 necroptosis-related genes, namely ALOX15, BCL2, IFNA1, PYGL and TLR3, were used to construct a survival prognostic model. The model exhibited excellent performance in the TCGA cohort and validation group and had good prediction accuracy in screening out high-risk prostate cancer patients.Conclusion: We successfully identified necroptosis-related genes and constructed a prognostic model that can accurately predict 1- 3-and 5-years overall survival (OS) rates of PRAD patients. Our riskscore model has provided novel strategy for the prediction of PRAD patients’ prognosis.


2022 ◽  
Vol 11 ◽  
Author(s):  
Adrián Mosquera Orgueira ◽  
Miguel Cid López ◽  
Andrés Peleteiro Raíndo ◽  
Aitor Abuín Blanco ◽  
Jose Ángel Díaz Arias ◽  
...  

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Feiyue Qiu ◽  
Guodao Zhang ◽  
Xin Sheng ◽  
Lei Jiang ◽  
Lijia Zhu ◽  
...  

AbstractE-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.


Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 90
Author(s):  
Sarah E. Marzen ◽  
James P. Crutchfield

Reservoir computers (RCs) and recurrent neural networks (RNNs) can mimic any finite-state automaton in theory, and some workers demonstrated that this can hold in practice. We test the capability of generalized linear models, RCs, and Long Short-Term Memory (LSTM) RNN architectures to predict the stochastic processes generated by a large suite of probabilistic deterministic finite-state automata (PDFA) in the small-data limit according to two metrics: predictive accuracy and distance to a predictive rate-distortion curve. The latter provides a sense of whether or not the RNN is a lossy predictive feature extractor in the information-theoretic sense. PDFAs provide an excellent performance benchmark in that they can be systematically enumerated, the randomness and correlation structure of their generated processes are exactly known, and their optimal memory-limited predictors are easily computed. With less data than is needed to make a good prediction, LSTMs surprisingly lose at predictive accuracy, but win at lossy predictive feature extraction. These results highlight the utility of causal states in understanding the capabilities of RNNs to predict.


2022 ◽  
Author(s):  
Willson B Gaul ◽  
Dinara Sadykova ◽  
Hannah J White ◽  
Lupe León-Sánchez ◽  
Paul Caplat ◽  
...  

Aim: Soil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. Location: Island of Ireland. Methods: We tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. Results: Using spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main Conclusions: Spatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.


2021 ◽  
Author(s):  
Huifeng Cao ◽  
Dayin Chen ◽  
Zhihui Zhang ◽  
Liang Cheng ◽  
Zhenguo Luo ◽  
...  

Abstract Objectives: Bladder carcinoma (BLCA) is one of the most common malignant diseases of urinary system. Our study aimed to investigate the autophagy-related signatures in the tumor immune microenvironment and construct effective prognosis prediction model.Methods: RNA sequencing data and corresponding clinical information of BLCA were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Autophagy-related genes were extracted from TCGA dataset for consensus clustering analysis, and differences in survival rate were analyzed. STIMATE algorithm was used to analyze the tumor microenvironment (TME) and immune cell infiltration was compared between different clusters. Differentially expressed genes (DEGs) between different clusters were identified, followed by function annotation. Independent prognostic signatures were further revealed to construct prognostic prediction model.Results: We identified 35 autophagy-related genes associated with prognosis. Survival rate of samples in cluster 1 was significant lower than that in cluster 2. Cluster 2 had markedly lower tumor purity and significantly higher estimate score and stromal score than cluster 1. The proportions of T cells CD8, macrophages M1, T cells CD4 memory activated, NK cells activated, and dendritic cells activated were higher in cluster 1. There were 1,275 DEGs which were mainly enriched in functions and pathways related to immune response and cancer. Seven genes (ATF6, CAPN2, NAMPT, NPC1, P4HB, PIK3C3, and RPTOR) were further identified as the independent prognostic signatures to construct risk score prediction model, which had good prediction performance.Conclusion: Prognosis prediction model based on 7 autophagy-related genes may have great value in BLCA prognosis prediction.


2021 ◽  
Author(s):  
Yuting Zhao ◽  
Shouyu Li ◽  
Lutong Yan ◽  
Zejian Yang ◽  
Na Chai ◽  
...  

Abstract Background: Due to the rarity of invasive micropapillary carcinoma (IMPC) of the breast, no randomized trial has investigated the prediction of overall survival (OS) for patients with IMPC after breast-conserving surgery (BCS). This study aimed to construct a nomogram for predicting OS in IMPC patients after BCS. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database, 481 eligible cases diagnosed with IMPC were collected. OS in IMPC patients after BCS were assessed through multivariable Cox analyses, Harrell’s concordance indexes (C-indexes), receiver operating characteristics (ROCs) curves, calibration curves, decision curve analyses (DCA), and survival analyses. Results: 336 patients were randomly assigned into training cohort and 145 cases in validation cohort. The multivariate Cox regression analyses revealed that age at diagnosis, American Joint Committee on Cancer (AJCC) stage, marital status, hormone receptor status and chemotherapy were significant prognostic factors for OS in conservatively operated IMPC patients. The nomogram had a good prediction performance with the C-indices 0.771 (95%CI, 0.712-0.830) and 0.715 (95%CI, 0.603-0.827) in training and validation cohorts, respectively, and good consistency between the predicted and observed probability, with calibration curves plotted and the slope was close to 1. Based on calculation of the model, participants in low-risk group had a better OS in comparison with those in high-risk group (P < 0.001). Conclusions: A nomogram was developed to predict individualized risk of OS for IMPC patients after BCS. By risk stratification, this model is expected to guide treatment decision making in improving long-term follow-up strategies for IMPC patients.


Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 1
Author(s):  
Jary Pomponi ◽  
Simone Scardapane ◽  
Aurelio Uncini

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.


Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 530
Author(s):  
Congmin Yang ◽  
Tao Zhu ◽  
Yang Zhang ◽  
Huansheng Ning ◽  
Liming Chen ◽  
...  

The particle swarm optimization (PSO) algorithm has been widely used in various optimization problems. Although PSO has been successful in many fields, solving optimization problems in big data applications often requires processing of massive amounts of data, which cannot be handled by traditional PSO on a single machine. There have been several parallel PSO based on Spark, however they are almost proposed for solving numerical optimization problems, and few for big data optimization problems. In this paper, we propose a new Spark-based parallel PSO algorithm to predict the co-authorship of academic papers, which we formulate as an optimization problem from massive academic data. Experimental results show that the proposed parallel PSO can achieve good prediction accuracy.


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