Entropy Score Research Articles

Speech intelligibility of children with an auditory brainstem implant: a triple-case study

ABSTRACT Auditory brainstem implantation (ABI) is a relative recent development in paediatric hearing restoration. Consequently, young-implanted children’s productive language has not received much attention. This study investigated speech intelligibility of children with ABI (N = 3) in comparison to children with cochlear implants (CI) and children with typical hearing (TH). Spontaneous speech samples were recorded from children representing the three groups matched on cumulative vocabulary level. Untrained listeners (N = 101) rated the intelligibility of one-word utterances on a continuous scale and transcribed each utterance. The rating task yielded a numerical score between 0 and 100, and similarities and differences between the listeners’ transcriptions were captured by a relative entropy score. The speech intelligibility of children with CI and children with TH was similar. Speech intelligibility of children with ABI was well below that of the children with CI and TH. But whereas one child with ABI’s intelligibility approached that of the control groups with increasing lexicon size, the intelligibility of the two other children with ABI did not develop in a similar direction. Overall, speech intelligibility was only moderate in the three groups of children, with quite low ratings and considerable differences in the listeners’ transcriptions, resulting in high relative entropy scores.

Share‐loan pledging and relaxation of share‐repurchase restrictions in China

AbstractWe examine a period in which the in‐principle prohibition of share repurchases was relaxed in 2018 to allow for the repurchase of shares whose prices dropped materially or were below book value. We find that share‐loan pledges by controlling shareholders are significantly and positively associated with share repurchases for a sample of 3,531 Chinese firms. This finding is robust using entropy and propensity score matched samples, 2SLS IV regressions, regression discontinuity design (RDD), and two exogenous shocks (the China–US trade war in 2018 and the COVID‐19 pandemic in 2020). The association remains robust but becomes less strong with state ownership and with above industry average firm agency problems, leverage ratios and financial constraints/distress (i.e., other share repurchase motives). Our findings highlight the importance of financial market regulations on share‐loan pledging and share repurchases in emerging markets during periods of heightened firm‐specific and systemic margin call risk and impending liquidation of share‐loan pledges.

Unique Pattern of Intrahepatic T-cell Clonality in Biliary Atresia Livers Versus Intestinal Controls: A Pilot Study

Biliary atresia (BA) is a rare infantile cholangiopathy of unclear etiology proposed by some to be due to virus-induced autoreactive T-cell-mediated inflammation. The hallmark of T cell activity is clonal expansion of T lymphocytes expressing similar T-cell receptor (TCR) variable regions of the β-chain. Objective: To test our hypothesis that BA liver tissues would show clonal expansion of 1 or several TCRs. Methods: The complementarity-determining region 3 region of the β-chain of the TCR was characterized using next-generation sequencing of 7 BA liver samples (age 51 ± 14 days) and 9 intestinal control samples (age 38 ± 16 days). Following sequencing, clonality scores, various VDJ recombinations, total and productive templates, and complementarity-determining region 3 length were measured using the immunoSEQ Analyzer. Results: Next-generation sequencing revealed 1 common TCR rearrangement in 3 BA samples not found in controls. There was a highly diverse TCR population among BA liver and the control samples. The clonality scores ranged from 0.0004 to 0.0062 using a Shannon’s entropy score, with numbers close to 0 being highly diverse and numbers close to 1 being highly clonal. The most common TCR VDJ recombinations comprised 1.47–12.9% of the total population of TCR for the BA tissues and 1.05–10.3% for the control samples. Conclusions: Our results show a highly diverse TCR repertoire among all of our samples. However, predominant TCR clonality was not found in any sample. Further studies are required for any possible antigenic triggers responsible for the unique T-cell rearrangements observed in the BA samples.

Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score

With evaluating the randomness and the nonlinear dynamic change of time sequence effectively, multiscale fuzzy distribution entropy (MFDE) is proposed to extract fault features from vibration signal. However, it is unsuitable for multivariate signals, which contain more abundant fault information. Through changing the coarse-grained and calculation process of MFDE, a novel entropy named as multivariate multiscale fuzzy distribution entropy (MMFDE) is proposed, which not only has the characteristics of MFDE, but also considers the within- and cross-channel correlations. By applying MMFDE to two kinds of simulation signals, the results show the proposed entropy is stable and reliable. A novel fault diagnosis method for rotating machinery is proposed by extracting fault features with MMFDE, selecting sensitive ones with Fisher score (FS), and identifying working state with support vector machine (SVM). Two experiments show the better diagnosis performance of the proposed rotating machinery fault diagnosis method than the related methods with the accuracy of 98.95% and 96.80% respectively.

Improved hepatocellular carcinoma fatality prognosis using ensemble learning approach

Hepatocellular Carcinoma (HCC) is the most common type of liver cancer which accounts for around 75% of all liver cancer cases. From statistical data, it has been found that fatality due to liver cancer is higher regardless of improved screening and discoveries in medicines, HCC escalate fatality rate. This paper presents an ensemble learning model for HCC survival prediction. The input predictors for the proposed model consist of geographical information, risk factors and clinical trial information of HCC patients. Fifteen different models are presented to evaluate the prediction. These models present data pre-processing, feature reduction/elimination and survival classification phase. For feature evaluation, LASSO Regression (L-1 penalization), Ridge Regression (L-2 penalization), Genetic Algorithm (GA) Optimization and Random Forest (RF) are proposed for weight valuation of features wherein features with significant weights are selected for prediction. With the aid of feature evaluators, L-1 penalized Nu-Support Vector Classification (Nu-SVC) model, L-2 penalized Nu-SVC model, GA optimized Nu-SVC model, RF-NuSVC model, L-1 penalized RidgeCV (RCV) model, L-2 penalized RCV model, GA optimized RCV model, RF-RCV model, L-1 penalized Gradient Boosting Ensemble Learning (GBEL) model, L-2 penalized GBEL model, GA optimized GBEL model and RFGBEL model are presented for survival prediction. The prediction performances of models were measured in terms of accuracy, recall/sensitivity, F-1 score, Log-Loss score, Jaccard score and Area Under Receiver Operating Curves (AUROC). The results indicate that RFGBEL model shows excellent performance in contrast to other proposed models. The proposed RFGBEL model achieves an accuracy of 93.92%, sensitivity of 94.73%, F-1 score of 0.93, Log-Loss/Cross entropy score of 5.89 and Jaccard score of 0.72. RFGBEL estimates value of area under the curve as 0.932. Comparison of RFGBEL model with other existing state of the art models are presented for performance assessment. Overall, the RFGBEL model has a capability to predict the result with more accuracy and sensitivity by means of machine learning and data mining approach.

Research and Application of Business Ability Evaluation Based On DBSCAN Algorithm and Entropy Method

Based on the data of 95598 work sheets and quality inspection, this paper constructs customer commissioners business capability evaluation models of different business types. Firstly, through DBSCAN algorithm we mine the potential category characteristics of customer commissioners. Then, entropy method is used to score the customer commissioners under the target category comprehensively. Finally combining the clustering results and entropy score, the customer commissioners with relatively strong and weak business capabilities are identified accurately. By marking the business capacity labels for the corresponding customer commissioners, it has guiding significance for the post adjustment and business training, which can improve the quality of power supply service and customer satisfaction for customer service center.

Identifying Expert and Novice Visual Scanpath Patterns and Their Relationship to Assessing Learning-Relevant Student Characteristics

The paper addresses cognitive processes during a teacher's professional task of assessing learning-relevant student characteristics. We explore how eye-movement patterns (scanpaths) differ across expert and novice teachers during an assessment situation. In an eye-tracking experiment, participants watched an authentic video of a classroom lesson and were subsequently asked to assess five different students. Instead of using typically reported averaged gaze data (e.g., number of fixations), we used gaze patterns as an indicator for visual behavior. We extracted scanpath patterns, compared them qualitatively (common sub-pattern) and quantitatively (scanpath entropy) between experts and novices, and related teachers' visual behavior to their assessment competence. Results show that teachers' scanpaths were idiosyncratic and more similar to teachers of the same expertise group. Moreover, experts monitored all target students more regularly and made recurring scans to re-adjust their assessment. Lastly, this behavior was quantified using Shannon's entropy score. Results indicate that experts' scanpaths were more complex, involved more frequent revisits of all students, and that experts transferred their attention between all students with equal probability. Experts' visual behavior was also statistically related to higher judgment accuracy.

A multi-stage group decision making for strategic supplier selection based on prospect theory with interval-valued q-rung orthopair fuzzy linguistic sets

With the aggravation of market competition, strategic supplier is becoming more and more critical for the success of manufacturing enterprises. Suppler selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the long-term development of organizations. Hence, strategic supplier selection must be restructures considering the long-term relationships and prospects for sustainable cooperation. This paper proposes a novel multi-stage multi-attribute group decision making method under an interval-valued q-rung orthopair fuzzy linguistic set (IVq-ROFLS) environment considering the decision makers’ (DMs) psychological state in the group decision-making process. First, the initial comprehensive fuzzy evaluations of DMs are represented as IVq-ROFLS. Subsequently, two new operators are proposed for aggregating different stages and DMs’ preferences respectively by extending generalized weighted averaging (GWA) to IVq-ROFLS context. Later, a new hamming distance based linear programming method based on entropy measure and score function is introduced to evaluate the unknown criteria weights. Additionally, the Euclidean distance is employed to compute the gain and loss matrix, and objects are prioritized by extending the popular Prospect theory (PT) method to the IVq-ROFLS context. Finally, the practical use of the proposed decision framework is validated by using a strategic supplier selection problem, as well as the effectiveness and applicability of the framework are discussed by using comparative analysis with other methods.

Advancing GIST-Based Solvent Functionals through Multiobjective Optimization of Solvent Enthalpy and Entropy Scoring Terms.

Water molecules and their impact on the enthalpy and entropy of protein-ligand binding are of considerable interest in drug discovery. In this contribution, we use multiobjective optimization to fit the solvent enthalpy and entropy scoring terms of grid inhomogeneous solvation theory (GIST)-based solvent functionals to measured isothermal titration calorimetry (ITC) data of protein-ligand-binding reactions for ligand pairs of the protein thrombin. For the investigated ligand pairs, the overwhelming contribution to the relative binding affinity difference is assumed to be attributed to the contribution of water molecules. We present different implementations of the solvent functionals and then proceed by analyzing the most successful one in more detail through error assessment and presentation of the scoring regions in the binding pocket and the unbound ligands of selected examples. We find overall good agreement between calculated and experimental data and, although physically not fully justified, the ligand-desolvation score increases binding affinity, thus suggesting that the solvent molecules on the surface of the unbound ligand constitute a proxy for interactions gained through the protein. Furthermore, we find limited transferability of the parameters even between similar protein targets, thus suggesting refitting for each new protein target. Possible reasons for the limited transferability may arise through the initial assumption of dominating water contributions to binding affinity. Nonetheless, overall our study demonstrates a consistent approach to assign thermodynamic quantities to water molecules that is sensible to measured thermodynamic signatures and enables bridging the gap between experimentally determined water positions in protein-ligand complexes and measured thermodynamic data.

Detection of ICE states from mechanical vibrations using entropy measurements and machine learning algorithms

1. Zheng P, Yang H. Generalized composite multiscale permutation entropy and laplacian score based rolling bearing fault diagnosis. Mechanical Systems and Signal Processing. 2018; 99: 229–243. https://doi.org/10.1016/j.ymss.... CrossRef Google Scholar

Spatial Dynamics of Racialized Income Inequality in the Washington, DC, Metropolitan Area

This article presents findings of an analysis of the association between changes in neighborhoods’ racial composition and income inequality in the Washington, DC, metropolitan area. We cluster U.S. Census data at the ward and district levels and find that the connection between race and income inequality appears potent in Washington, DC. Racially more homogeneous wards have lower income inequalities than those that are racially heterogeneous. We also show that two alternative measures of neighborhood-level racial diversity—proportion white and an entropy score—account for substantial shares in the variation in household incomes in these wards. In contrast, these patterns in the surrounding counties are less pronounced. Our analysis suggests that the U.S. capital’s growing prosperity and improved fiscal health have intensified income inequality across the DC metropolitan area especially within Washington, DC. We probe into these differences and argue that place stratification theory is helpful to understand changing neighborhood compositions in the entire Washington, DC, metropolitan area. Our results illustrate a need for more effective policy coordination across jurisdictions as well as greater consideration of housing policy to help decrease residential segregation and support vulnerable residents in and around the U.S. capital city.

Fragment-based Discovery of Potential Anticancer Lead: Computational and in vitro Studies.

The human epidermal growth factor receptor 2 (HER2) plays a role in the propagation of different types of cancers. It was identified in many types of cancer tissues like; breast, ovarian, lung, prostate, and stomach cancers. Therefore, inhibition of HER2 can lead to the discovery of novel anticancer agents. The study aims to discover a lead scaffold with drug-like properties and high affinity toward HER2. A list of HER2 inhibitors were collected, analyzed, and subjected to fragmentation and molecular docking. The in silico study computed the affinity, clash score, and ligand entropy score. A pharmacophore model for an ideal inhibitor designed, and tested against breast, lung, and prostatic cancer cell lines. The discovered lead compound achieved several hydrogen bonds with the primary residues found in the active site of HER2, such as; Met801, Gln99, Lys753, and Thr862 with a computational affinity - 13.45 kcal/mol. In addition to a hydrophobic interaction with leu800. The in vitro cytotoxic activity against; breast cancer MCF-7, prostatic cancer PC-3 and lung cancer A-549 cell lines showed (IC50 = 86.38 ±1.1 mmol/ml), (IC50 = 157.02 ±1.3 mmol/ml), and (IC50 = 181.1 ±2.4 mmol/ml) respectively. The discovered lead is an excellent drug-like candidate for further development and optimization.

An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: Application in pharmacological therapy selection for type 2 diabetes

In the context of medical decision making, the Type 2 Diabetes (T2D) pharmacological therapy selection problem involves several medications that can be stipulated to manage the blood glucose level of patients. The extensive range of hyperglycemia lowering agents with varying outcomes and several side effects makes the decision quite complicated and uncertain. Pythagorean Fuzzy Sets (PFSs) are proven as one of the valuable tools to handle ambiguous and ill-defined problems. This paper introduces an innovative Complex Proportional Assessment (COPRAS) to solve the T2D medication selection problem under the Pythagorean fuzzy environment. In this methodology, a new formula based on entropy measure and score function is introduced to evaluate the unknown criteria weights. To doing so, the new entropy measure and score function are developed under the PFSs context. Further, an illustrative case study of the T2D pharmacological therapy selection problem is taken to express the feasibility and usefulness of the proposed method in real-world decision-making problems. Finally, the obtained result is compared with some existing methods, which confirms the strength and stability of the proposed COPRAS method.

Circulating HIV-1 Integrase Genotypes in Tanzania: Implication on the Introduction of Integrase Inhibitors-Based Antiretroviral Therapy Regimen.

Tanzania has recently adapted World Health Organization antiretroviral guidelines that include integrase strand transfer inhibitors (INSTIs) in the first-line regimen. However, there is lack of evidence on integrase (IN) gene polymorphisms in viral strains circulating in Tanzania. In this study, we characterize IN gene polymorphisms in viral strains circulating in Dar es Salaam, Tanzania, before introduction of INSTIs. Plasma viral RNAs were prepared from 158 HIV-1-infected subjects, including 111 treated, but viremic (INSTI-naïve), subjects. A part of the pol gene encompassing the IN-coding region was amplified and directly sequenced by the Sanger sequencing method. Subtype analysis revealed that subtypes A1, C, and D and intersubtype recombinants were 42%, 38%, 11%, and 9%, respectively. Although multiple subtypes cocirculate, the IN gene exhibited a relatively conserved amino acid sequence pattern with an average Shannon entropy score of 0.16. No major INSTI resistance mutations were found; however, accessory resistance mutations at positions T97A, E157Q, G163E/K, and 128A/T were detected in 5% of subjects.

Entropy based analysis of vertebrate sperm protamines sequences: evidence of potential dityrosine and cysteine-tyrosine cross-linking in sperm protamines

BackgroundSpermatogenesis is the process by which germ cells develop into spermatozoa in the testis. Sperm protamines are small, arginine-rich nuclear proteins which replace somatic histones during spermatogenesis, allowing a hypercondensed DNA state that leads to a smaller nucleus and facilitating sperm head formation. In eutherian mammals, the protamine-DNA complex is achieved through a combination of intra- and intermolecular cysteine cross-linking and possibly histidine-cysteine zinc ion binding. Most metatherian sperm protamines lack cysteine but perform the same function. This lack of dicysteine cross-linking has made the mechanism behind metatherian protamines folding unclear.ResultsProtamine sequences from UniProt’s databases were pulled down and sorted into homologous groups. Multiple sequence alignments were then generated and a gap weighted relative entropy score calculated for each position. For the eutherian alignments, the cysteine containing positions were the most highly conserved. For the metatherian alignment, the tyrosine containing positions were the most highly conserved and corresponded to the cysteine positions in the eutherian alignment.ConclusionsHigh conservation indicates likely functionally/structurally important residues at these positions in the metatherian protamines and the correspondence with cysteine positions within the eutherian alignment implies a similarity in function. One possible explanation is that the metatherian protamine structure relies upon dityrosine cross-linking between these highly conserved tyrosines. Also, the human protamine P1 sequence has a tyrosine substitution in a position expecting eutherian dicysteine cross-linking. Similarly, some members of the metatherian Planigales genus contain cysteine substitutions in positions expecting plausible metatherian dityrosine cross-linking. Rare cysteine-tyrosine cross-linking could explain both observations.

AnnoGen: annotating genome-wide pragmatic features.

Genome annotation is an important step for all in-depth bioinformatics analysis. It is imperative to augment quantity and diversity of genome-wide annotation data for the latest reference genome to promote its adoption by ongoing and future impactful studies. We developed a python toolkit AnnoGen, which at the first time, allows the annotation of three pragmatic genomic features for the GRCh38 genome in enormous base-wise quantities. The three features are chemical binding Energy, sequence information Entropy and Homology Score. The Homology Score is an exceptional feature that captures the genome-wide homology through single-base-offset tiling windows of 100 continual nucleotide bases. AnnoGen is capable of annotating the proprietary pragmatic features for variable user-interested genomic regions and optionally comparing two parallel sets of genomic regions. AnnoGen is characterized with simple utility modes and succinct HTML report of informative statistical tables and plots. https://github.com/shengqh/annogen.

通过相对熵得分诊断个体状态

通过相对熵得分诊断个体状态

Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab.

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.

Pancreatic cancer: Assessment of neoadjuvant chemotherapy outcome based on radiomics of pretreatment computed tomography.

56 Background: To evaluate contrast enhanced-computed tomography (CE-CT) features to predicting treatment response and survival after neoadjuvant chemotherapy (NAC) for patients with borderline resectable and locally advanced pancreatic cancer. Methods: Sixty-one patients with pancreatic cancer receiving NAC were enrolled and underwent pre-treatment abdominal CE-CT. The patients were divided into groups according to post-treatment tumor size changes. Based on the pretreatment CE-CT images, 396 radiomic features were extracted from three-dimensional regions of interest. The optimal features were selected and three supervised machine learning classifiers were developed. Finally, univariate and multivariate analyses were performed to evaluate the ability of the selected features to predict the histopathological response and outcome. Results: Nine, seven, and five radiomic features, respectively, were selected as optimal features in three experiments. Two features, Haralick Entropy and Histogram Entropy, were consistent among the experiments and both were higher in patients with enlarged tumors. Moreover, a lower Histogram Entropy score was significantly associated with a better histopathological response (p = 0.008) and smaller tumor size (p = 0.041) in patients after tumor resection. In univariate Cox regression analysis, lower Histogram Entropy (p = 0.006) and lower Haralick Entropy (p = 0.001) predicted a better prognosis. Meanwhile, lower Haralick Entropy (p = 0.048) was an independent predictor of longer survival time in multivariate Cox regression analysis. Conclusions: Radiomic features are strongly correlated with NAC treatment response and prognosis in pancreatic cancer, suggesting the potential of imaging radiomics to help tailor the treatment of pancreatic cancer in the era of personalized medicine.

A feature extraction method based on composite multi-scale permutation entropy and Laplacian score for shearer cutting state recognition

In the field of coal mining, accurate cutting state recognition of shearer is the basis and premise for achieving automatic coal cutting. In this paper, a novel shearer cutting state recognition method is presented based on composite multi-scale permutation (CMPE), Laplacian score (LS) and fly optimization algorithm-based support vector machine classification (FOA-SVM). CMPE is proposed to overcome the shortcomings of MPE and can extract the hidden state characteristics from the vibration signals of shearer rocker arm. Some simulations are provided to select the appropriate parameter settings and prove the superiority of CMPE to MPE. In addition, LS algorithm is employed to sort the extracted features over different scales according to their importance and the sensitive feature combinations can be generated scientifically. The FOA-SVM classifier is constructed to achieve intelligent recognition of shearer cutting state. Finally, some experiments are presented and the comparison results indicated that the proposed method can realize the recognition of shearer cutting state with higher accuracy than the existing methods.