A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

Aim: Breast cancer stands as a prominent cause of female mortality on a global scale, underscoring the critical need for precise and efficient diagnostic techniques. This research significantly enriches the body of knowledge pertaining to breast cancer classification, especially when employing breast ultrasound images, by introducing a novel method rooted in the two dimensional empirical mode decomposition (biEMD) method. In this study, an evaluation of the classification performance is proposed based on various texture features of breast ultrasound images and their corresponding biEMD subbands. Methods: A total of 437 benign and 210 malignant breast ultrasound images were analyzed, preprocessed, and decomposed into three biEMD sub-bands. A variety of features, including the Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Histogram of Oriented Gradient (HOG), were extracted, and a feature selection process was performed using the least absolute shrinkage and selection operator method. The study employed GLCM, LBP and HOG, and machine learning techniques, including artificial neural networks (ANN), k-nearest neighbors (kNN), the ensemble method, and statistical discriminant analysis, to classify benign and malignant cases. The classification performance, measured through Area Under the Curve (AUC), accuracy, and F1 score, was evaluated using a 10-fold cross-validation approach. Results: The study showed that using the ANN method and hybrid features (GLCM+LBP+HOG) from BUS images' biEMD sub-bands led to excellent performance, with an AUC of 0.9945, an accuracy of 0.9644, and an F1 score of 0.9668. This has revealed the effectiveness of the biEMD method for classifying breast tumor types from ultrasound images. Conclusion: The obtained results have revealed the effectiveness of the biEMD method for classifying breast tumor types from ultrasound images, demonstrating high-performance classification using the proposed approach

The Importance of Morphological Knowledge in the Reading Comprehension Difficulties in a Highly Agglutinative Language: Evidence from Poor Comprehenders

This study examined the importance of morphological knowledge in the reading comprehension difficulties of poor comprehenders reading in a highly agglutinative language, Turkish. Participants were 56 students recruited from the second and third grades. In the assessment process, we applied three experimental paradigms addressing the participants' morphological and morpho-syntactical knowledge at the lexical and the supralexical levels. Data were collected in individual sessions and analyzed by running a series of GLM ANOVAs and calculating the Spearman–Brown correlation coefficient. Findings suggest morphological knowledge is an important indicator of reading comprehension difficulties in Turkish, a highly agglutinative language. The acquisition of adequate reading comprehension seems to be modified by particularities of the morphological knowledge. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

The Effects of Resilience and Cyberbullying on Self-Esteem

To investigate the effects of resilience and cyberbullying on students’ self-esteem, predictive correlational design was employed as a method of investigation. Data were collected from 580 ninth-grade students at Anatolian High Schools. Findings of the study demonstrated that as the resilience level increases, self-esteem level increases. Also, as cyberbullying increases, self-esteem decreases. © 2019 Trustees of Boston University

Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds

This paper proposed various feature extraction procedures to separate crackles and rhonchi of pathological lung sounds from normal lung sounds. The feature extraction process for distinguishing crackles and rhonchus from normal sounds comprises three signal-processing modules with the following functions: (1) f(min)/f(max) was the frequency ratio from the conventional technique of power spectral density (PSD) based on the Welch method. (2) The average instantaneous frequency (IF) and the exchange time of the instantaneous frequency were calculated by the Hilbert Huang transform (HHT). (3) The eigenvalues were obtained from the singular spectrum analysis (SSA) method. In the classification process, a support vector machine (SVM) was used to distinguish the crackles, rhonchus and normal lung sounds. The results showed that the selected features positively represented the characteristic changes in sounds. The PSD frequency ratio and the eigenvalues demonstrate higher classification accuracy (between 90% and 100%) than the calculations of average and exchange time of IF. The calculated features are extremely promising for the evaluation and classification of other biomedical signals as well as other lung sounds. (C) 2014 Elsevier Inc. All rights reserved

REVIEW OF PUBLIC TWEETS OVER TURKEY WITHIN A PRE-DETERMINED TIME

Spatial data by using public knowledge is the most popular way to gather data in terms of social media within the last decade. Literature defines, public or volunteers are accepted bionic sensors detecting their surroundings and share what they detect in terms of their social media applications or microblogs. Besides being cheapest and fastest and easy way of spatial data acquisition, public or volunteers provides not only spatial data but also attribute data which makes the data more valuable. To understand and interpret those data have some difficulties according to locality. Although some difficulties like difference of languages, society structure and the time period would affect tweets depending on locality, gathering public knowledge or volunteered data contribute many scientific or private researches like Urban, Environmental, and Market side. To extract information, data should be reviewed locally according to main aim of research. In this study, our aim is to draw a perspective for a PhD research about volunteered data in the case of Turkey

Selective adsorption of L1210 leukemia cells/human leukocytes on micropatterned surfaces prepared from polystyrene/polypropylene-polyethylene blends

The objective of this study is to prepare polymeric surfaces which will adsorb L1210 leukemia cells selectively more than that of healthy human leukocytes in order to develop new treatment options for people with leukemia. Chemically heterogeneous and micropatterned surfaces were formed on round glass slides by dip coating with accompanying phase-separation process where only commercial polymers were used. Surface properties were determined by using optical microscopy, 3D profilometry, SEM and measuring contact angles. Polymer, solvent/nonsolvent types, blend composition and temperature were found to be effective in controlling the dimensions of surface microislands. MTT tests were applied for cell viability performance of these surfaces. Polystyrene/polyethylene-polypropylene blend surfaces were found to show considerable positive selectivity to L1210 leukemia cells where L1210/healthy leukocytes adsorption ratio approached to 9-fold in vitro. Effects of wettability, surface free energy, microisland size geometry on the adsorption performances of L1210/leukocytes pairs are discussed. © 2013 Elsevier B.V

Can Functional Connectivity at Resting Brain in ADHD Indicate the Impairments in Sensory-Motor Functions and Face/Emotion Recognition?

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disease known to cause impair-ments in cognitive, sensory-motor functions and face/emotion recognition. This study aimed to examine the resting-state brain networks in children with ADHD using functional magnetic resonance imaging. We performed seed-to-voxel and region of interest (ROI) analyses including all Broadmann areas (BAs) comprehensively. Thirty right-handed children aged between 9 and 16 years (15 with ADHD and 15 typically developing control subjects closely matched for age and gender) were included. Ninety five brain regions including 84 BAs and 11 Default Mode network (DMN)-related regions (rsREL) were studied using seed-based and ROI-to-ROI analysis and connectivity measures were calculated (p < 0.001). Between-group differences were assessed by using t-statistics (p < 0.05). Seed-based analysis showed connectivity differences in the sensory-motor and face/emotion recognition regions in both groups. The between-group whole-brain comparison showed greater magnitude of activation in children with ADHD than in control subjects in brain regions that included the face/emotion recognition system and prefrontal cortex based on ROI-to-ROI analysis. This work revealed that the sensory-motor regions and regions related to face/emotion recognition showed atypical functional connectivities in ADHD patients compared to the controls. Observation of the differences in these regions supports previous findings in the literature based on task-based functional magnetic resonance imaging (fMRI) studies. Our study exhibited that these atypical differences can also occur in the resting brain. These results suggest that further investigations of regions related to motor-sensory and face/emotion recognition are required to better understand ADHD

Osteoselection supported by phase separated polymer blend films

The instability of implants after placement inside the body is one of the main obstacles to clinically succeed in periodontal and orthopedic applications. Adherence of fibroblasts instead of osteoblasts to implant surfaces usually results in formation of scar tissue and loss of the implant. Thus, selective bioadhesivity of osteoblasts is a desired characteristic for implant materials. In this study, we developed osteoselective and biofriendly polymeric thin films fabricated with a simple phase separation method using either homopolymers or various blends of homopolymers and copolymers. As adhesive and proliferative features of cells are highly dependent on the physicochemical properties of the surfaces, substrates with distinct chemical heterogeneity, wettability, and surface topography were developed and assessed for their osteoselective characteristics. Surface characterizations of the fabricated polymer thin films were performed with optical microscopy and SEM, their wettabilities were determined by contact angle measurements, and their surface roughness was measured by profilometry. Long-term adhesion behaviors of cells to polymer thin films were determined by F-actin staining of Saos-2 osteoblasts, and human gingival fibroblasts, HGFs, and their morphologies were observed by SEM imaging. The biocompatibility of the surfaces was also examined through cell viability assay. Our results showed that heterogeneous polypropylene polyethylene/polystyrene surfaces can govern Saos-2 and HGF attachment and organization. Selective adhesion of Saos-2 osteoblasts and inhibited adhesion of HGF cells were achieved on micro-structured and hydrophobic surfaces. This work paves the way for better control of cellular behaviors for adjustment of cell material interactions. © 2014 Wiley Periodicals, Inc