Lasso Regression Methods Research Articles

Integrated performance measurement system for Slovak heating industry: A balanced scorecard approach

The prerequisite for businesses’ success, competitiveness, and non-bankruptcy is their performance. An effective performance measurement system is a suitable tool for measuring and improving business performance. The development in performance measures moved from financial measures focused on company profitability to measurement systems combining different methods, approaches, and tools. The paper aims to identify key performance indicators for Slovak heating companies based on the developed integrated performance measurement system. The analysis sampled 292 Slovak companies within SK NACE 35 (heating industry). The performance measurement system was built on balanced scorecard principles, while the least absolute shrinkage and selection operator (Lasso regression) method was used to select financial indicators. Based on the combination of the above methods, a performance measurement system framework for the analyzed sample of businesses was created. The results show that when managing performance, the analyzed businesses should focus on the following financial performance indicators: Receivables turnover ratio, Return on equity, Return on costs, Total debt to total assets, Material intensity, Labor to revenue ratio, Netto cash flow to assets, Net working capital to total assets, and Short-term liabilities to assets. When building performance measurement system based on balanced scorecard principles, financial indicators were supplemented by non-financial ones. In addition to the original balanced scorecard principles, the performance measurement system was extended by environmental constituents. Also, the paper’s deliverable combines Lasso regression and balanced scorecard principles in order to select key performance indices. AcknowledgmentThis paper is prepared within the grant scheme VEGA No. 1/0741/20 (the application of variant methods in detecting symptoms of possible bankruptcy of Slovak businesses in order to ensure their sustainable development).

An association study of clock genes with major depressive disorder

ObjectiveTo study the relationship between clock genes and Major Depressive Disorder (MDD). MethodsGEO database was used to obtain the chip data and clinical information of datasets GSE98793, GSE39653 and GSE52790. The differentially expressed clock genes were found through the analysis of the differentially expressed genes between MDD and healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) enrichment analysis were performed on the differential expressed clock genes. Lasso Regression and Support Vector Machine (SVM) method were used for screening the differential expressed clock genes. Logistic regression was used to establish a diagnostic model for depression with the screened genes. Receiver Operating Characteristic (ROC) Curve was used to verify the model. Gene differential expression analysis was performed for MDD with high scores and MDD with low scores in the diagnostic model. Gene Set Enrichment Analysis (GSEA) enrichment analysis was performed for differentially expressed genes. Single-gene GSEA was used to analyze each gene in the model separately. Cibersort method was used to analyze the immune infiltration of MDD and healthy controls, and the correlation between immune cells and clock genes was analyzed. Cytoscape was used to analyze the clock gene interaction network. The DGIdb website was used to predict potentially effective therapeutic drugs for clock genes closely related to MDD. ResultsSix genes were identified by differential expression analysis of clock genes between MDD and healthy controls. GO and KEGG enrichment analysis of 6 genes showed that their pathways were concentrated such as circadian rhythm, rhythmic process, TGF − beta signaling pathway, longevity regulating pathway-multiple species, adipocytokine signaling pathway and so on. Lasso regression and SVM were used to screen out 5 clock genes (HDAC1, ID3, NFIL3, PRKAA1, TNF) for MDD. The diagnostic model of depression was established according to the 5 clock genes. The area under the curve (AUC) of the established depression diagnostic model was 0.686. Gene difference analysis was performed between MDD patients with high score of clock gene diagnostic model and MDD patients with low score. GSEA was performed for the differential genes showed that the most enriched pathways were:adipocytokine signaling pathway, TGF beta signaling pathway, oxidative phosphorylation, primary immunodeficiency, and so on. The single gene GSEA showed that the most enriched pathways were Toll like receptor signaling pathway, glucolipid metabolism, amino acid metabolism, neuroactive ligand receptor interaction, and so on. The results of immune infiltration analysis showed that NK cells resting and Macrophages M2 were different between MDD and control groups. In MDD, the gene closely related to NK cells resting was HDAC1, and the genes closely related to Macrophages M2 were HDAC1 and NFIL3. The RNA interactions network of clock genes shows that the regulation process is complex, which can provide a reference for subsequent related research. Potential therapeutic drugs predict display, among the 5 clock genes, TNF, HDAC1, and PRKAA1 may have potential effective therapeutic drugs. ConclusionAmong all CLOCK genes, HDAC1, ID3, NFIL3, PRKAA1, TNF are closely related to MDD. Among them, TNF, HDAC1, and PRKAA1 may have potential effective therapeutic drugs.

Trade-offs among productive-living-ecological lands for facilitating low-carbon and efficient cities: evidences in 30 major Chinese cities from 2015 to 2020

ABSTRACT Low-carbon and efficient city, a sustainable city development mode, requires the land to carry more people and economy, but cause no ecological damage, which is essentially a trade-off among diverse land functional zones (LFZs), e.g. living, productive, and ecological lands, from a land system perspective. Unfortunately, there is little knowledge about how to trade off LFZs for building low-carbon and efficient cities. To fill the gap, the study first proposes a fine-grained LFZ mapping method to sort the land into seven kinds of LFZs based on multimodal geographic data; then, the study defines carbon efficiency (CE) as a reference to evaluate efficient cities considering their economics, society, and ecology; finally, the study presents a cross lasso regression method to explore how LFZ patterns as well as their temporal changes influence CE, based on which LFZ features are optimized at both national and city levels for improving CE and facilitating efficient cities. The proposed methods are verified in 30 major Chinese cities from 2015 to 2020. The experimental results indicate that the 30 cities have different LFZ patterns in area proportion, morphology, and temporal change, where LFZ proportions are master control factors to CE; and LFZ feature trade-off at national level works better than per city, as it can improve CE by 58.5% compared to that in 2020, which increases population carrying capacity by 88.5% and economy producing capacity by 81.0% with no change in carbon emission, or reduces carbon emission by 55.2% by keeping population and economy constant. These findings directly contribute to low-carbon and efficient city planning, and facilitating sustainable city development.

Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a highly heterogeneous malignant tumor associated with a poor prognosis, is a common cause of cancer-related deaths worldwide, with a limited survival benefit for patients despite ongoing therapeutic breakthroughs. Coronavirus disease 2019 (COVID-19), a severe infectious disease caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), is a global pandemic and a serious threat to human health. The increased susceptibility to SARS-CoV-2 infection and a poor prognosis in patients with cancer necessitate the exploration of the potential link between the two. No studies have investigated the relationship of COVID-19 genes with the prognosis and tumor development in patients with HCC. We screened prognosis-related COVID-19 genes in HCC, performed molecular typing, developed a stable and reliable COVID-19 genes signature for predicting survival, characterized the immune microenvironment in HCC patients, and explored new molecular therapeutic targets. Datasets of HCC patients, including RNA sequencing data and clinical information, were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Prognosis-related COVID-19 genes were identified by univariate Cox analysis. Molecular typing of HCC was performed using the consensus non-negative matrix factorization method (cNMF), followed by the analysis of survival, tumor microenvironment, and pathway enrichment for each subtype. Prognostic signatures were constructed using LASSO-Cox regression models, and receiver operating characteristic (ROC) curves were used to validate the predictive performance of the signature. The same approach was used for the test and external validation sets. Seven software packages were applied to determine the abundance of immune infiltration in HCC patients and investigate its relationship with the risk scores. Gene set enrichment analysis (GSEA) was used to explore the potential mechanisms by which the COVID-19 genes affect hepatocarcinogenesis and prognosis. Three types of machine learning methods were combined to identify the most critical genes in the signature and localize their expression at the single cell level. We identified 53 prognosis-related COVID-19 genes and classified HCC into two molecular subtypes (C1, C2) by using the NMF method. The prognosis of C2 was significantly better than that of C1, and the two subtypes differed remarkably in terms of the tumor immune microenvironment and biological functions. The 17 COVID-19 genes were screened using the LASSO regression method to develop a 17 COVID-19 genes signature, which demonstrated a good predictive performance for 1-, 2- and 3-year OS of patients with HCC. The risk score as an independent prognostic factor for HCC has better predictive accuracy than traditional clinical variables. Patients in the TCGA cohort were categorized by risk score into the high- and low-risk groups, with the high-risk group mainly enriched in the immune modulation-related pathways and the low-risk group mainly enriched in the metabolism-related pathways, suggesting that the COVID-19 genes may affect disease progression and prognosis by regulating the tumor immune microenvironment and metabolism in HCC. NOL10 was identified as the most critical gene in the signature and hypothesized to be a potential therapeutic target for HCC. Objectively, the COVID-19 genes signature developed in this study, as an independent prognostic factor in HCC patients, is closely associated with the prognosis and tumor immune microenvironment of HCC patients and indicates that they may regulate the development of HCC in multiple ways, providing us with new perspectives for understanding the molecular mechanisms of HCC and finding effective therapeutic targets.

Radiomic Features Are Predictive of Response in Rectal Cancer Undergoing Therapy.

Rectal cancer is a major mortality cause in the United States (US), and its treatment is based on individual risk factors for recurrence in each patient. In patients with rectal cancer, accurate assessment of response to chemoradiotherapy has increased in importance as the variety of treatment options has grown. In this scenario, a controversial non-operative approach may be considered in some patients for whom complete tumor regression is believed to have occurred. The recommended treatment for locally advanced rectal cancer (LARC, T3-4 ± N+) is total mesorectal excision (TME) after neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has become a standard technique for local staging of rectal cancer (tumor, lymph node, and circumferential resection margin [CRM] staging), in both the US and Europe, and it is getting widely used for restaging purposes. In our study, we aimed to use an MRI radiomic model to identify features linked to the different responses of chemoradiotherapy of rectal cancer before surgery, and whether these features are helpful to understand the effectiveness of the treatments. We retrospectively evaluated adult patients diagnosed with LARC who were subjected to at least 2 MRI examinations in 10-12 weeks at our hospital, before and after nCRT. The MRI acquisition protocol for the 2 exams included T2 sequence and apparent diffusion coefficient (ADC) map. The patients were divided into 2 groups according to the treatment response: complete or good responders (Group 1) and incomplete or poor responders (Group 2). MRI images were segmented, and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model. We included 38 patients (26 males and 12 females), who are classified from T2 and T4 stages in the rectal cancer TNM. After the nCRT, the patients were divided into Group 1 (13 patients), complete or good responders, and Group 2 (25 patients), incomplete or poor responders. Analysis at baseline generated the following significant features for the Mann-Whitney test (out of a total of 107) for each sequence. Also, the analysis at the end of the follow-up yielded a high number of significant features for the Mann-Whitney test (out of a total of 107) for each image. Features selected by the LASSO regression method for each image analyzed; ROC curves relative to each model are represented. We developed an MRI-based radiomic model that is able to differentiate and predict between responders and non-responders who went through nCRT for rectal cancer. This approach might identify early lesions with high surgical potential from lesions potentially resolving after medical treatment.

The nexus between agricultural land use, urbanization, and greenhouse gas emissions: Novel implications from different stages of income levels

The current study establishes theoretical and empirical linkages among urbanization, economic growth, land use, and greenhouse gas (GHG) emissions. The prime objective of this article is to draw novel conclusions and policies for the different income levels of countries regarding the urbanization and agriculture sector land on environmental pollution. Employing panel data of 50 countries for the period 1990 to 2019, this study uses the lasso regression and non-parametric regression panel data methods to investigate the impacts of land use (arable, permanent pastures, and cropland), urbanization growth, and economic progress on the pollution levels. After estimating a Lasso regression to find the best auto-regressive predictive specification, we used an auto-regressive partially linear regression where each of the drivers’ effects was modelled non-parametrically. The elasticity effect of the urban population on emissions is significantly positive and sizable. In addition, the effect distribution shows a non-negligible share of observations with an elasticity higher than one. Urban population growth is a serious threat to climate change, as it seems to increase sharply CO2 emissions (although with an elasticity pace smaller than one). The elasticity effect of GDP is significantly negative, which implies that the scale of production, by triggering efficiency, can have a positive effect on emissions reduction. The results argue that agglomeration negative effects put in place by larger urban population can partly explain this finding. Overall, the study argues that urbanization growth and economic activities lead to GHG emissions, whereas the study also discusses novel implications and the role of agricultural land use apropos Sustainable Development Goals (SDGs). The empirical findings allow us to draw novel conclusions and guidelines in line with SDGs. The agricultural reforms might include irrigation and farming techniques such as spin farming, solar tube wells, tunnel farming, technology use agreements, plant double helix, etc.

Development of a butyrate metabolism-related gene-based molecular subtypes and scoring system for predicting prognosis and immunotherapy response in bladder cancer.

In recent times, multiple molecular subtypes with varying prognoses have been identified in bladder cancer (BLCA). However, the attributes of butyrate metabolism-related (BMR) molecular subtypes and their correlation with immunotherapy response remain inadequately explored in BLCA. We utilized 594 samples of BLCA to investigate the molecular subtypes mediated by BMR genes and their correlation with the immunotherapy response. To quantify the BMR features of individual tumors, we developed a BMR score through the COX and LASSO regression methods. Clinical-related, tumor microenvironment, drug-sensitive and immunotherapy analyses were used to comprehensively analyze BMR scores. Two distinct molecular subtypes related to butyrate metabolism were identified in BLCA, each with unique prognostic implications and immune microenvironments. BMR score was constructed based on 7 BMR genes and was used to classify the patients into two score groups. Clinical analysis revealed that the BMR score was an independent prognostic factor. The higher the score, the worse the prognosis. The BMR score can also predict tumor immunity. The results demonstrated that a low BMR score was associated with higher efficacy of immunotherapy, which was also validated by an external dataset. Our study proposes both molecular subtypes and a BMR-based score as promising prognostic classifications in BLCA. These findings may offer new insights for the development of precise targeted cancer therapies.

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

Characterization of pyroptosis-related genes in esophageal cancer and construction of a prognostic model.

Esophageal cancer (EC) is a highly malignant digestive system tumor that often lacks evident early symptoms and has a poor prognosis. Pyroptosis, a form of programmed cell death, has been shown to be associated with the occurrence and progression of many malignancies. However, its role in esophageal cancer remains unclear. This work aimed to evaluate the prognostic value of pyroptosis-related genes (PRGs) in EC using data from The Cancer Genome Atlas (TCGA) cohort. The RNA-seq data from 171 esophageal samples in the TCGA database were employed. Differential expression genes (DEGs) between tumor and non-tumor samples were compared. Protein-protein interaction (PPI) networks were constructed using the STRING database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analyses were performed using the "clusterProfiler" package in R. Furthermore, based on the DEGs, all esophageal cancer cases were classified into three subtypes. A risk model for gene features was established using the LASSO regression method, and EC patients in the TCGA cohort were divided into high-risk and low-risk groups. A total of 614 PRGs were identified. Among them, 32 DEGs (31 upregulated and 1 downregulated) were found between normal esophageal tissue and EC tissue. PPI analysis identified key genes including IL-1β, CASP1, AIM2, HMGB1, GSDMD, PYCARD, IL-18, BAK1, and TP53. On the other hand, the low-risk group exhibited a significantly higher survival rate than the high-risk group (p < 0.001). Combined with the clinical characteristics of the TCGA cohort, it was found that the risk score was an independent prognostic factor for overall survival (OS) prediction in EC patients. KEGG and GO analyses revealed the enrichment of genes associated with cell proliferation in the high-risk group. PRGs play a crucial role in the occurrence and development of EC and can be used to predict the prognosis of EC patients.

Development and validation of cuproptosis molecular subtype-related signature for predicting immune prognostic characterization in gliomas.

Cuproptosis, a novel programmed cell death, plays an important role in glioma growth, angiogenesis, and immune response. Nonetheless, the role of cuproptosis-related genes (CRGs) in the prognosis and tumor microenvironment (TME) of gliomas remains unknown. By non-negative matrix factorization consensus clustering, 1286 glioma patients were classified based on the mRNA expression levels of 27 CRGs and investigated the association of immune infiltration and clinical characteristics with cuproptosis subtypes. A CRG-score system was constructed using LASSO and multivariate Cox regression methods and validated in independent cohorts to predict the prognosis of glioma patients. Glioma patients were divided into two cuproptosis subtypes. Cluster C2 was enriched in immune-related pathways, had higher macrophage M2, neutrophils, and CD8 + T cells, and poorer prognosis compared with cluster C1 which was enriched in metabolism-related pathways. We further constructed and validated the ten-gene CRG risk scores. Glioma patients in the high CRG-score group had higher tumor mutation burden, higher TME scores, and poorer prognoses compared with the low CRG-score group. Additionally, the AUC value of the CRG-score was 0.778 in predicting the prognosis of gliomas. WHO grading, IDH mutation, 1p/19q codeletion, and MGMT methylation were significant differences between high and low CRG-score groups. This study demonstrated that CRG-score was related to immune cell infiltration and could accurately predict gliomas' prognosis. Our findings may provide a novel understanding of the potential role of cuproptosis molecular pattern and TME in the immune response and prognosis of glioma patients.

Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis.

Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming. We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction. The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods. Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.

Generalized Support Vector Regression and Symmetry Functional Regression Approaches to Model the High-Dimensional Data

The analysis of the high-dimensional dataset when the number of explanatory variables is greater than the observations using classical regression approaches is not applicable and the results may be misleading. In this research, we proposed to analyze such data by introducing modern and up-to-date techniques such as support vector regression, symmetry functional regression, ridge, and lasso regression methods. In this study, we developed the support vector regression approach called generalized support vector regression to provide more efficient shrinkage estimation and variable selection in high-dimensional datasets. The generalized support vector regression can improve the performance of the support vector regression by employing an accurate algorithm for obtaining the optimum value of the penalty parameter using a cross-validation score, which is an asymptotically unbiased feasible estimator of the risk function. In this regard, using the proposed methods to analyze two real high-dimensional datasets (yeast gene data and riboflavin data) and a simulated dataset, the most efficient model is determined based on three criteria (correlation squared, mean squared error, and mean absolute error percentage deviation) according to the type of datasets. On the basis of the above criteria, the efficiency of the proposed estimators is evaluated.

Characterization of a cuproptosis-related signature to evaluate immune features and predict prognosis in colorectal cancer.

Cuproptosis is a newly discovered type of cell death. Little is known about the roles that cuproptosis related genes (CRGs) play in colorectal cancer (CRC). The aim of this study is to evaluate the prognostic value of CRGs and their relationship with tumor immune microenvironment. TCGA-COAD dataset was used as the training cohort. Pearson correlation was employed to identify CRGs and paired tumor-normal samples were used to identify those CRGs with differential expression pattern. A risk score signature was constructed using LASSO regression and multivariate Cox stepwise regression methods. Two GEO datasets were used as validation cohorts for confirming predictive power and clinical significance of this model. Expression patterns of seven CRGs were evaluated in COAD tissues. In vitro experiments were conducted to validate the expression of the CRGs during cuproptosis. A total of 771 differentially expressed CRGs were identified in the training cohort. A predictive model termed riskScore was constructed consisting of 7 CRGs and two clinical parameters (age and stage). Survival analysis suggested that patients with higher riskScore showed shorter OS than those with lower (P<0.0001). ROC analysis revealed that AUC values of cases in the training cohort for 1-, 2-, and 3-year survival were 0.82, 0.80, 0.86 respectively, indicating its good predictive efficacy. Correlations with clinical features showed that higher riskScore was significantly associated with advanced TNM stages, which were further confirmed in two validation cohorts. Single sample gene set enrichment analysis (ssGSEA) showed that high-risk group presented with an immune-cold phenotype. Consistently, ESTIMATE algorithm analysis showed lower immune scores in riskScore-high group. Expressions of key molecules in riskScore model are strongly associated with TME infiltrating cells and immune checkpoint molecules. Patients with a lower riskScore exhibited a higher complete remission rate in CRCs. Finally, seven CRGs involved in riskScore were significantly altered between cancerous and paracancerous normal tissues. Elesclomol, a potent copper ionophore, significantly altered expressions of seven CRGs in CRCs, indicating their relationship with cuproptosis. The cuproptosis-related gene signature could serve as a potential prognostic predictor for colorectal cancer patients and may offer novel insights into clinical cancer therapeutics.

Construction of a CCL20-centered circadian-signature based prognostic model in cervical cancer

BackgroundRather low vaccination rates for Human papillomavirus (HPV) and pre-existing cervical cancer patients with limited therapeutic strategies ask for more precise prognostic model development. On the other side, the clinical significance of circadian clock signatures in cervical cancer lacks investigation.MethodsSubtypes classification based upon eight circadian clock core genes were implemented in TCGA-CESC through k-means clustering methods. Afterwards, KEGG, GO and GSEA analysis were conducted upon differentially expressed genes (DEGs) between high and low-risk groups, and tumor microenvironment (TME) investigation by CIBERSORT and ESTIMATE. Furthermore, a prognostic model was developed by cox and lasso regression methods, and verified in GSE44001 by time-dependent receiver-operating characteristic curve (ROC) analysis. Lastly, FISH and IHC were used for validation of CCL20 expression in patients’ specimens and U14 subcutaneous tumor models were built for TME composition.ResultsWe successfully classified cervical patients into high-risk and low-risk groups based upon circadian-oscillation-signatures. Afterwards, we built a prognostic risk model composed of GJB2, CCL20 and KRT24 with excellent predictive value on patients’ overall survival (OS). We then proposed metabolism unbalance, especially for glycolysis, and immune related pathways to be major enriched signatures between the high-risk and low-risk groups. Then, we proposed an ‘immune-desert’-like suppressive myeloid cells infiltration pattern in high-risk group TME and verified its resistance to immunotherapies. Finally, CCL20 was proved positively correlated with real-world patients’ stages and induced significant less CD8+ T cells and more M2 macrophages infiltration in mouse model.ConclusionsWe unraveled a prognostic risk model based upon circadian oscillation and verified its solidity. Specifically, we unveiled distinct TME immune signatures in high-risk groups.

Use of ARIMA, Ridge Regression and Lasso Regression Methods Enhanced by Extraterrestrial Radiation in Hourly Radiation Estimation

Yeşil enerjiye artan ilgi, özellikle güneş enerjisinden elektrik enerjisi üretimini popüler hale getirmiştir. Güneş enerjisinin kesikli yapısı nedeniyle gelecekteki enerji üretim tahmini, elektrik enerjisinin kesintisiz ve doğaya dost biçimde karşılanması açısından oldukça önemlidir. Bu nedenle, güneş ışınımının tahmin edilmesinin, elektrik üretimi, iletimi, dağıtımı ve tüketimine kadar bütün süreçlerin planlanması ve yönetilmesinde hayati öneme sahip olacağı açıktır. Bu çalışmada Afyon Kocatepe Üniversitesi (AKÜ) Güneş ve Rüzgar Enerjileri Uygulama ve Araştırma Merkez Müdürlüğü’nden (GÜRAM) alınan saatlik bazdaki iki yıllık güneş ışınım verisi üzerinde, zaman serisi analiz yöntemi olan Oto Regresif Entegreli Hareketli Ortalama (Auto Regressive Integrated Moving Average-ARIMA) ve regresyon modelleri olan ridge ve lasso regresyon tahmin modelleri kullanılarak bir yıllık saatlik güneş ışınımı tahmini işlemi gerçekleştirilmiştir. Ayrıca yöntemlerin yapılarında Dünya Dışı Işınım Filtresi (DDIF) uygulanarak tahmin sonuçları iyileştirilmiştir. Kullanılan yöntemler ile elde edilen tahmin değerleri ve gerçek ışınım değerleri arasındaki ilişkinin yakınlığı grafikler ve istatistiksel yöntemler ile ortaya koyulmaya çalışılmıştır.

Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features.

The objective is to develop a mitotic prediction model and preoperative risk stratification nomogram for gastrointestinal stromal tumor (GIST) based on computed tomography (CT) radiomic features. A total of 267 GIST patients from 2009.07 to 2015.09 were retrospectively collected and randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest was delineated from the portal-phase images on contrast-enhanced (CE)-CT, and radiomic features were extracted. Lasso regression method was used to select valuable features to establish a radiomic model for predicting mitotic index in GIST. Finally, the nomogram of preoperative risk stratification was constructed by combining the radiomic features and clinical risk factors. Four radiomic features closely related to the level of mitosis were obtained, and a mitotic radiomic model was constructed. The area under the curve (AUC) of the radiomics signature model used to predict mitotic levels in training and validation cohorts (training cohort AUC = 0.752; 95% confidence interval [95%CI] 0.674-0.829; validation cohort AUC = 0.764; 95% CI 0.667-0.862). Finally, the preoperative risk stratification nomogram combining radiomic features was equivalent to the clinically recognized gold standard AUC (0.965 vs. 0.983) (p = 0.117). The Cox regression analysis found that the nomogram score was one of the independent risk factors for the long-term prognosis of the patients. Preoperative CT radiomic features can effectively predict the level of mitosis in GIST, and combined with preoperative tumor size, accurate preoperative risk stratification can be performed to guide clinical decision-making and individualized treatment.

Distribution of Relaxation Times Based on Lasso Regression: A Tool for High-Resolution Analysis of IMPS Data in Photoelectrochemical Systems.

Intensity-modulated photocurrent spectroscopy (IMPS) has been largely employed in semiconductor characterization for solar energy conversion devices to probe the operando behavior with widely available facilities. However, the implementation of IMPS data analysis to complex structures, whether based on the physical rate constant model (RCM) or the assumption-free distribution of relaxation times (DRT), is generally limited to a semi-quantitative description of the charge carrier kinetics of the system. In this study, a new algorithm for the analysis of IMPS data is developed, providing unprecedented time resolution to the investigation of μs to s charge carrier dynamics in semiconductor-based systems used in photoelectrochemistry and photovoltaics. The algorithm, based on the previously developed DRT analysis, is herein modified with a Lasso regression method and available to the reader free of charge. A validation of this new algorithm is performed on a α-Fe2O3 photoanode for photoelectrochemical water splitting, identified as a standard platform in the field, highlighting multiple potential-dependent charge transfer paths, otherwise hidden in the conventional IMPS data analysis.

A bidirectional dictionary LASSO regression method for online water quality detection in wastewater treatment plants

During the online water quality detection of wastewater treatment plants, the organic ingredients hidden in suspended particles are usually ignored, which significantly degrades the online detection accuracy, especially in high turbidity environments. To tackle this problem, in this paper, a novel online detection method is proposed, which can effectively avoid the physical and chemical interferences caused by suspended particles. First, UV–vis spectroscopy is innovatively utilized in the oxidative digestion process to continuously monitor the substance transformation during the reaction. Then, based on dictionary learning and LASSO regression, a novel machine learning method, bidirectional dictionary LASSO regression (BD-LASSO), is developed. BD-LASSO can bidirectionally implement the dictionary learning from both spectrum and feature aspects, ensuring the information extraction at the feature level and therefore improving the detection accuracy and speed. Based on the experimental results, our method can more effectively eliminate the interference brought by turbidity with a much higher detection accuracy (relative error <10%) and a far shorter detection time (within 5 min).

RANKL acts an unfavorable prognostic biomarker and potential target in advanced KRAS‐mutated lung adenocarcinoma

Advanced lung cancers carrying Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation remain a group that lacks effective treatments. Receptor activator of nuclear factor-κB ligand (RANKL) has been demonstrated to drive malignant phenotypes in lung cancer; however, its role in KRAS-mutant (mt) lung adenocarcinoma (LUAD) is not yet fully elucidated. The data used to explore expression and prognosis were obtained from The Cancer Genome Atlas, Genotype-Tissue Expression databases, and from our hospital. The proliferation, invasion, and migration capacities of KRAS-mt LUAD cells were evaluated. The prediction model was established via Lasso regression method. RANKL is strongly expressed in advanced KRAS-mt LUAD, and significantly distinct association exists between high RANKL expression and poor survival. The enriched expression of RANKL in advanced KRAS-mt LUAD was confirmed by specimens from our hospital. Further, although not statistically significant, our clinical cohort (n = 57) revealed a longer median progression-free survival in advanced KRAS-mt LUAD patients treated with RANKL inhibitor than those without (300 vs. 133 days, p = 0.210), but not in KRAS-wt ones (208 vs. 250 days, p = 0.334). Decrease of KRAS-mt LUAD cells' capacity for proliferation, invasion, and migration was observed when RANKL was knocked down. Enrichment analysis suggested distinct roles of RANKL between KRAS-mt and KRAS-wt LUAD, with adhesion-related pathways and molecules significantly downregulated in the KRAS-mt RANKL-high tumors. Finally, a model for predicting overall survival of KRAS-wt LUAD was established according to four related key genes (BCAM, ICAM5, ITGA3, and LAMA3), which had good performance in prediction concordance. RANKL acts as an unfavorable prognostic biomarker for patients with advanced KRAS-mt LUAD. Inhibition of RANKL may be a feasible strategy for this subset of patients.

Regression-based Analysis of Ozone Layer via Machine Learning Models

Ozone protects the livings on the earth from ultraviolet radiation. The emission of ozone depleting substance causes the Antarctic ozone hole and reduces the ultraviolet radiation absorption rate by ozone layer. Researchers find that ozone depleting substances (ODS) accounts for ozone concentration between 9 km and 25km, by chemical-climate models and ozone concentration will increase by 15% until 2050. In this work, we used six major ODS consumption worldwide and mean stratospheric ozone concentration each year. Seven regression models are implemented to make prediction and k-fold cross validation is used for avoiding overfitting. Root mean squared error (RMSE), and standard deviation are two performance metrics of regression models. The results indicate that the prediction from support vector regression achieved the lowest RMSE. Random forester and k-nearest neighbor are also appropriate for make prediction. We also concluded that linear, polynomial, ridge, and lasso regression methods are hardly to fit the data in this application.