During the search for natural antioxidants from fungal metabolites, three new sesquiterpene derivatives (1-3) have been isolated from the culture broth of Coprinopsis echinospora. Their structures ...were determined by spectroscopic methods, mainly NMR and mass spectrometric analyses. These compounds exhibited antioxidant activity with IC
values in the range of 34.4-144.5 μM in the 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical-scavenging assay.
The timing of surgery in the context of acute endocarditis associated with valvular failure and large vegetations is controversial. In this randomized trial in South Korea, early surgery was ...associated with fewer clinically significant embolic events than conventional treatment.
Despite advances in medical and surgical treatment, infective endocarditis remains a serious disease that carries a considerable risk of death and morbidity.
1
,
2
The role of surgery in the treatment of infective endocarditis has been expanding, and current guidelines advocate surgical management for complicated left-sided infective endocarditis.
2
,
3
Early surgery is strongly indicated for patients with infective endocarditis and congestive heart failure,
1
,
4
but the indications for surgical intervention to prevent systemic embolism remain to be defined.
5
Early identification of patients with large vegetations and a high risk of embolism,
6
increased experience with complete excision of infected tissue and valve . . .
► We propose a multivariate mutual information-based feature selection for multi-label classification. ► Label interactions without resorting to problem transformation have been considered. ► The ...calculation of high-dimensional entropy is decomposed into a cumulative sum of multivariate mutual information.
Recently, classification tasks that naturally emerge in multi-label domains, such as text categorization, automatic scene annotation, and gene function prediction, have attracted great interest. As in traditional single-label classification, feature selection plays an important role in multi-label classification. However, recent feature selection methods require preprocessing steps that transform the label set into a single label, resulting in subsequent additional problems. In this paper, we propose a feature selection method for multi-label classification that naturally derives from mutual information between selected features and the label set. The proposed method was applied to several multi-label classification problems and compared with conventional methods. The experimental results demonstrate that the proposed method improves the classification performance to a great extent and has proved to be a useful method in selecting features for multi-label classification problems.
Multi-label feature selection involves selecting important features from multi-label data sets. This can be achieved by ranking features based on their importance and then selecting the top-ranked ...features. Many multi-label feature selection methods for finding a feature subset that can improve multi-label learning accuracy have been proposed. In contrast, computationally efficient multi-label feature selection methods have not been studied extensively. In this study, we propose a fast multi-label feature selection method based on information-theoretic feature ranking. Experimental results demonstrate that the proposed method generates a feature subset significantly faster than several other multi-label feature selection methods for large multi-label data sets.
•A score function from mutual information between a feature and labels was derived.•Unnecessary computations from the score function were discarded.•A strategy to identify important labels from sparse label set was proposed.•The computational cost of each component was analyzed theoretically.
•We propose a new unsupervised feature selection based on information theory.•A single optimization problem is designed considering dependence among features.•We analyze the convergence of the ...proposed iterative algorithm.
Many research topics present very high dimensional data. Because of the heavy execution times and large memory requirements, many machine learning methods have difficulty in processing these data. In this paper, we propose a new unsupervised feature selection method considering the pairwise dependence of features (feature dependency-based unsupervised feature selection, or DUFS). To avoid selecting redundant features, the proposed method calculates the dependence among features and applies this information to a regression-based unsupervised feature selection process. We can select small feature set with the dependence among features by eliminating redundant features. To consider the dependence among features, we used mutual information widely used in supervised feature selection area. To our best knowledge, it is the first study to consider the pairwise dependence of features in the unsupervised feature selection method. Experimental results for six data sets demonstrate that the proposed method outperforms existing state-of-the-art unsupervised feature selection methods in most cases.
Multi-label feature selection involves the selection of relevant features from multi-labeled datasets, resulting in a potential improvement of multi-label learning accuracy. In conventional ...multi-label feature selection methods, the final feature subset is obtained by identifying the features of high relevance with low redundancy. Thus, accurate score evaluation is a key factor for obtaining an effective feature subset. However, conventional methods suffer from inaccurate conditional relevance evaluation when a large number of labels are involved. As a result, irrelevant features can be a member of the final feature subset, leading to low multi-label learning accuracy. In this paper, we propose a new multi-label feature selection method. Using a scalable relevance evaluation process that evaluates conditional relevance more accurately, the proposed method significantly improves multi-label learning accuracy compared with conventional multi-label feature selection methods.
•A multi-label feature selection method for multi-label classification is proposed.•We propose a new scalable relevance evaluation process for feature evaluation.•The proposed method is designed to use a simpler dependency calculation process.•An effective approximation for the relevance evaluation is devised.
Chlorogenic acid (CGA), an ester of caffeic acid and quinic acid, is among the phenolic acid compounds which can be naturally found in green coffee extract and tea. CGA has been studied since it ...displays significant pharmacological properties. The aim of this study was to investigate the effects of CGA on cognitive function and neuroprotection including its mechanisms in the hippocampus following transient forebrain ischemia in gerbils. Memory and learning following the ischemia was investigated by eight-arm radial maze and passive avoidance tests. Neuroprotection was examined by immunohistochemistry for neuronal nuclei-specific protein and Fluoro-Jade B histofluorescence staining. For mechanisms of the neuroprotection, alterations in copper, zinc-superoxide dismutase (SOD1), SOD2 as antioxidant enzymes, dihydroethidium and 4-hydroxy-2-nonenal as indicators for oxidative stress, and anti-inflammatory cytokines (interleukin (IL)-4 and IL-13) and pro-inflammatory cytokines (tumor necrosis factor α (TNF-α) and IL-2) were examined by Western blotting and/or immunohistochemistry. As a result, pretreatment with 30 mg/kg CGA attenuated cognitive impairment and displayed a neuroprotective effect against transient forebrain ischemia (TFI). In Western blotting, the expression levels of SOD2 and IL-4 were increased due to pretreatment with CGA and, furthermore, 4-HNE production and IL-4 expressions were inhibited by CGA pretreatment. Additionally, pretreated CGA enhanced antioxidant enzymes and anti-inflammatory cytokines and, in contrast, attenuated oxidative stress and pro-inflammatory cytokine expression. Based on these results, we suggest that CGA can be a useful neuroprotective material against ischemia-reperfusion injury due to its antioxidant and anti-inflammatory efficacies.
•We present a memetic feature selection algorithm for multi-label classification.•This method employs memetic procedures to refine the feature subsets found through GAs.•This hybridization improves ...the multi-label classification performance compared to counterparts.
The use of multi-label classification, i.e., assigning unseen patterns to multiple categories, has emerged in modern applications. A genetic-algorithm based multi-label feature selection method has been considered useful because it successfully improves the accuracy of multi-label classification. However, genetic algorithms are limited to identify fine-tuned feature subsets that are close to the global optimum, which results in a long runtime. In this paper, we present a memetic feature selection algorithm for multi-label classification that prevents premature convergence and improves the efficiency. The proposed method employs memetic procedures to refine the feature subsets found through a genetic search, resulting in an improvement in multi-label classification. Empirical studies using various tests show that the proposed method outperforms conventional multi-label feature selection methods.
Background
There is a close link between
Fusobacterium nucleatum
and colorectal cancer (CRC) tumorigenesis and chemoresistance. However, the genetic characteristics and clinical significance of CRC ...related with
F. nucleatum
remains unclear. This study evaluated the relationship between
F. nucleatum
, pathway mutation, and patient prognosis.
Methods
Fusobacterium nucleatum
amount in the tumor tissue and adjacent normal tissue were measured by quantitative polymerase chain reaction in adjuvant (
N
= 128) and metastatic (
N
= 118) cohorts. Patients were divided into binary (
F. nucleatum
-high and
F. nucleatum
-low) according to
F. nucleatum
amount. Targeted next-generation sequencing of 40 genes included in the 5 critical pathways (WNT, P53, RTK-RAS, PI3 K, and TGF-β) was performed in the adjuvant cohort.
Results
Patients with MSI-H and CIMP-H had higher amount of
F. nucleatum
in tumor tissue.
Fusobacterium nucleatum
-high patients had higher rates of transition mutation and C to T (G to A) nucleotide change regardless of MSI status. In addition, mutation rate of
AMER1
and
ATM
genes, and TGF-β pathway was higher in
F. nucleatum
-high patients.
Fusobacterium nucleatum
-high was associated with poor overall survival (OS) in the palliative cohort (26.4 vs. 30.7 months,
p
= 0.042). Multivariate analysis revealed
F. nucleatum
-high as an independent negative prognostic factor for OS adjusted hazard ratio of 1.69 (95% confidence interval 1.04–2.75),
p
= 0.034. However,
F. nucleatum
amount was not associated with recurrence in the adjuvant cohort.
Conclusions
F. nucleatum
-high was associated with poor survival in metastatic CRC. In addition, we identified mutational characteristics of colorectal cancer according to
F. nucleatum
amount.
•We proposed a MI-based feature selection method without problem transformation.•A score function measuring dependency between features and labels was derived.•We derived theoretical bounds of score ...function.•Based on theoretical bounds, a score function of variations from MI was chosen.
Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than single-label feature selection tasks. In this paper, we propose the Mutual Information-based multi-label feature selection method using interaction information. This method is naturally able to measure dependencies among multiple variables. To develop an efficient multi-label feature selection method, we derive theoretical bounds for the interaction information. Empirical studies indicate that our proposed multi-label feature selection method discovers effective feature subsets for multi-label classification problems.
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