bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sulfonated graphene oxide impregnated cellulose acetate floated beads for adsorption of methylene blue dye: optimization using response surface methodology
Authors: Basha Islam K. , Abd El-Monaem Eman M. , Khalifa Randa E. , Omer Ahmed M. , Eltaweil Abdelazeem S. 
Published: June 2022

AbstractNew multi-featured adsorbent beads were fabricated through impregnation of sulfonated graphene (SGO) oxide into cellulose acetate (CA) beads for fast adsorption of cationic methylene blue (MB) dye. The formulated SGO@CA composite beads were thoroughly characterized by several tools including FTIR, TGA, SEM, XRD, XPS and zeta potential. The optimal levels of the most significant identified variables affecting the adsorption process were sequential determined by the response surface methodology (RSM) using Plackett–Burman and Box–Behnken designs. The gained results denoted that the surface of SGO@CA beads displayed the higher negative charges (− 42.2 mV) compared to − 35.7 and − 38.7 mV for pristine CA and SGO, respectively. In addition, the floated SGO@CA beads demonstrated excellent floating property, fast adsorption and easy separation. The adsorption performance was accomplished rapidly, since the adsorption equilibrium was closely gotten within 30 min. Furthermore, the adsorption capacity was greatly improved with increasing SGO content from 10 to 30%. The obtained data were followed the pseudo-second order kinetic model and agreed with Langmuir adsorption isotherm model with a maximum adsorption capacity reached 234.74 mg g−1. The thermodynamic studies designated the spontaneity and endothermic nature of MB dye adsorption. Besides, the floated beads exposed acceptable adsorption characteristics for six successive reuse cycles, in addition to their better adsorption selectivity towards MB dye compared to cationic crystal violet and anionic Congo red dyes. These findings assume that the formulated SGO@CA floated beads could be used effectively as highly efficient, easy separable and reusable adsorbents for the fast removal of toxic cationic dyes.
Back-translating GWAS findings to animal models reveals a role for Hgfac and Slc39a8 in alcohol and nicotine consumption
Authors: Banna F. K. El , Otto J. M. , Mulloy S. M. , Tsai W. , McElroy S. M. , Wong A. L. , Cutts G. , Vrieze S. I. , Lee A. M. 
Published: June 2022

AbstractAlcohol and tobacco are the most commonly used addictive substances, with high comorbidity rates between alcohol use disorder and tobacco use disorder. Risk for alcohol and nicotine addiction is highly heritable, and they share common genetic factors. A GWAS in over 1 million individuals has revealed 566 genetic variants in 406 loci associated with multiple stages of alcohol and tobacco use. Three novel genes—SLC39A8, GRK4 and HGFAC—within loci associated with altered alcoholic drinks per week (ADW) or cigarettes per day (CPD) were selected to further study their role in alcohol and tobacco use disorder. The role of these genes was assessed using the two-bottle choice addiction paradigm in transgenic mice for each of the genes. We found significant decreases in chronic alcohol consumption and preference in femaleHgfacknockout (KO) mice, and decreased nicotine preference in maleHgfacKO compared with wild-type (WT) mice. Additionally, maleSlc39a8hypomorph mice showed greater overall nicotine preference compared with WT mice, while no differences were detected forGrk4KO mice in alcohol or nicotine consumption and preference in either sex. Thus, this study implicatesHgfacandSlc39a8in alcohol and tobacco use in a sex-specific manner.
A preliminary study on the strand stress of transmission line in tension setting out
Authors: Hu Xin , Zhang Qingming , Zhao Zezhong , Shang Cheng , Rui Xiaoming 
Published: June 2022

AbstractTransmission lines often suffer from conductor damage during operation, most of which are caused by excessive local stress of aluminum strands. However, one of the causes of conductor damage may be the tension paying off passing the pulley. At the same time, there are relatively few researches on the damage of conductor causing by passing pulley. Therefore, this paper studies the inter strand stress characteristics of aluminum strand when tension paying off passes through the pulley. The influence of envelope angle, tension load and friction on the stress characteristics between aluminum strands is studied by numerical simulation. The results show that the equivalent stress of the neutral layer of the aluminum strand is small, and the maximum equivalent stress appears at the contact position of the adjacent strand. The larger the envelope angle and tension load of the aluminum strands, the greater the equivalent stress in the cross section. Furthermore, the friction between aluminum strands has a certain effect on reducing the equivalent stress in the cross section. The equivalent stress of the aluminum strand increases from the outer layer to the inner layer, so the inner aluminum strand is more likely to be damaged than the outer layer. Finally, the experiment and the simulation have both been carried on, and then it is shown that the stress value in the corresponding section has a good consistency.
Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains
Authors: Soltani Soheil , Ojaghi Ashkan , Qiao Hui , Kaza Nischita , Li Xinyang , Dai Qionghai , Osunkoya Adeboye O. , Robles Francisco E. 
Published: June 2022

AbstractIdentifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.
Cdkl5 mutant zebrafish shows skeletal and neuronal alterations mimicking human CDKL5 deficiency disorder
Authors: Varela Tatiana , Varela Débora , Martins Gil , Conceição Natércia , Cancela M. Leonor 
Published: June 2022

AbstractCDKL5 deficiency disorder (CDD) is a rare neurodevelopmental condition characterized primarily by seizures and impairment of cognitive and motor skills. Additional phenotypes include microcephaly, dysmorphic facial features, and scoliosis. Mutations in cyclin-dependent kinase-like 5 (CDKL5) gene, encoding a kinase essential for normal brain development and function, are responsible for CDD. Zebrafish is an accepted biomedical model for the study of several genetic diseases and has many advantages over other models. Therefore, this work aimed to characterize the phenotypic, behavioral, and molecular consequences of the Cdkl5 protein disruption in a cdkl5 mutant zebrafish line (sa21938). cdkl5sa21938 mutants displayed a reduced head size, suggesting microcephaly, a feature frequently observed in CDD individuals. Double staining revealed shorter craniofacial cartilage structures and decrease bone mineralization in cdkl5 homozygous zebrafish indicating an abnormal craniofacial cartilage development and impaired skeletal development. Motor behavior analysis showed that cdkl5sa21938 embryos had less frequency of double coiling suggesting impaired glutamatergic neurotransmission. Locomotor behavior analysis revealed that homozygous embryos swim shorter distances, indicative of impaired motor activity which is one of the main traits of CCD. Although no apparent spontaneous seizures were observed in these models, upon treatment with pentylenetetrazole, seizure behavior and an increase in the distance travelled were observed. Quantitative PCR showed that neuronal markers, including glutamatergic genes were dysregulated in cdkl5sa21938 mutant embryos. In conclusion, homozygous cdkl5sa21938 zebrafish mimic several characteristics of CDD, thus validating them as a suitable animal model to better understand the physiopathology of this disorder.
Non-covalent Fc-Fab interactions significantly alter internal dynamics of an IgG1 antibody
Authors: Natesan Ramakrishnan , Agrawal Neeraj J. 
Published: June 2022

AbstractThe fragment-antigen-binding arms (Fab1 and Fab2) in a canonical immunoglobulin G (IgG) molecule have identical sequences and hence are always expected to exhibit symmetric conformations and dynamics. Using long all atom molecular simulations of a human IgG1 crystal structure 1HZH, we demonstrate that the translational and rotational dynamics of Fab1 and Fab2 also strongly depend on their interactions with each other and with the fragment-crystallizable (Fc) region. We show that the Fab2 arm in the 1HZH structure is non-covalently bound to the Fc region via long-lived hydrogen bonds, involving its light chain and both heavy chains of the Fc region. These highly stable interactions stabilize non-trivial conformer states with constrained fluctuations. We observe subtle modifications in Fab1 dynamics in response to Fab2-Fc interactions that points to novel allosteric interactions between the Fab arms. These results yield novel insights into the inter- and intra-fragment motions of immunoglobulins which could help us better understand the relation between their structure and function.
Utility of plasma circulating tumor DNA and tumor DNA profiles in head and neck squamous cell carcinoma
Authors: Chikuie Nobuyuki , Urabe Yuji , Ueda Tsutomu , Hamamoto Takao , Taruya Takayuki , Kono Takashi , Yumii Kohei , Takeno Sachio 
Published: June 2022

AbstractEarly recurrence detection of head and neck squamous cell carcinoma (HNSCC) is important for improving prognosis. Recently, circulating tumor DNA (ctDNA) has been reported to be useful in early detection or treatment response determination in various carcinomas. This study aimed to identify the utility of ctDNA for predicting recurrent metastasis in patients with HNSCC. We collected pre-treatment tissues (malignant and normal tissues) and multiple plasma samples before and after treatment for 20 cases of HNSCC treated with radical therapy. ctDNA was detected in pre-treatment plasma in 10 cases; however, there were no significant associations with tumor recurrence and staging. During follow-up, ctDNA was detected in 5 of the 7 plasma samples of recurrent cases but not in the 13 recurrence-free cases. Moreover, there was a significant difference in post-treatment relapse-free survival time between the groups with and without detected ctDNA (20.6 ± 7.7 vs. 9.6 ± 9.1 months, respectively; log-rank test, p < 0.01). Moreover, for two of the five cases with ctDNA detected after treatment, ctDNA detection was a more sensitive predictor of recurrence than imaging studies. ctDNA detection during treatment follow-up was useful in patients with HNSCC for predicting the response to treatment and recurrent metastasis.
Clinical significance of metabolic quantification for retinal nonperfusion in diabetic retinopathy
Authors: Jeong Areum , Yao Xue , van Hemert Jano , Sagong Min 
Published: June 2022

AbstractDiabetic retinopathy (DR) is characterized by microvascular changes including ischemia. Degradation and metabolic changes of various retinal cells occur during ischemia. Ischemic region containing more cells will lead to greater metabolic impairment. We analyzed the non-perfusion region (NPR) by integrating histologic mapping with ultra-widefield fluorescein angiography (UWF FA) images. We also investigated the correlations of the weighted ischemic index (ISI) considering the regional distribution of retinal cells with cytokines, macular edema (ME), and neovascularization (NV). In this study, 32 patients with treatment-naïve DR and 21 age-matched control participants were included. The difference between the non-weighted and weighted ISI of NPR with leakage was greatest at the posterior region. The weighted ISI of NPR with leakage was correlated with MCP-1, IL-8, IL-6, PlGF, and VEGF-A levels, while the non-weighted ISI of NPR with leakage was correlated with IL-8 and IL-6 levels. The presence of baseline ME or NV in patients with DR was associated with the weighted ISI, with a stronger association when cones and rods were weighted. The weighted ISI reflecting both metabolic activity and cell distribution demonstrated a better correlation with clinical features and was more valuable in NPR with leakage than non-weighted ISI, which previous studies conventionally used.
Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
Authors: Jee Donghyun , Yoon Ji Hyun , Ra Ho , Kwon Jin-woo , Baek Jiwon 
Published: June 2022

AbstractWe sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients.
An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator
Authors: Yin Shihong , Luo Qifang , Zhou Guo , Zhou Yongquan , Zhu Binwen 
Published: June 2022

AbstractIn order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.
Comparative analysis of two Korean irises (Iris ruthenica and I. uniflora, Iridaceae) based on plastome sequencing and micromorphology
Authors: Choi Bokyung , Park Inkyu , So Soonku , Myeong Hyeon-Ho , Ryu Jangseung , Ahn Yu-Eun , Shim Kyu-Chan , Song Jun-Ho , Jang Tae-Soo 
Published: June 2022

AbstractIris ruthenica Ker Gawl. and I. uniflora Pall. ex Link, which are rare and endangered species in Korea, possess considerable horticultural and medicinal value among Korean irises. However, discrimination of the species is hindered by extensive morphological similarity. Thus, the aim of the present study was to identify discriminating features by comparing the species’ complete plastid genome (i.e., plastome) sequences and micromorphological features, including leaf margins, stomatal complex distribution (hypostomatic vs. amphistomatic leaves), anther stomata density, and tepal epidermal cell patterns. Plastome comparison revealed slightly divergent regions within intergenic spacer regions, and the most variable sequences, which were distributed in non-coding regions, could be used as molecular markers for the discrimination of I. ruthenica and I. uniflora. Phylogenetic analysis of the Iris species revealed that I. ruthenica and I. uniflora formed a well-supported clade. The comparison of plastomes and micromorphological features performed in this study provides useful information for elucidating taxonomic, phylogenetic, and evolutionary relationships in Iridaceae. Further studies, including those based on molecular cytogenetic approaches using species specific markers, will offer insights into species delimitation of the two closely related Iris species.
Concrete-to-concrete shear friction behavior under cyclic loading: experimental investigation
Authors: Taklas Mohamad , Leblouba Moussa , Barakat Samer , Fageeri Ahmed , Mohamad Firass 
Published: June 2022

AbstractThis study investigated the concrete-to-concrete friction behavior under dynamic cyclic loading at different loading rates, vertical loads, and surface roughness. The present work answers essential questions about the dynamic behavior of concrete-to-concrete friction since most of the available literature deals with static or quasi-static loading conditions. To this end, an experimental program was devised by casting 96 concrete blocks. A total of 48 dynamic push–pull tests were performed on each pair of blocks (mobile top block and fixed bottom block). Test variables included three types of surface roughness, four different loading rates, and two normal stresses. Performance measures included the static and dynamic friction forces coefficients of static and kinetic friction in addition to effective stiffness and effective damping. Moreover, the test results showed that the static and kinetic friction coefficients, effective stiffness, and effective damping decrease with increasing loading rates. Moreover, increasing the normal stress increases the friction force, thus increasing the effective stiffness and reducing the effective damping surface for all surface roughness types. The effects of test variables on the hysteresis behavior were also investigated.
Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model
Authors: Liu Bing , Zhang Yirui 
Published: June 2022

Abstract
The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA–RVM–NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA–RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA–RVM–NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA–RVM–NAR model and other commonly used models such as multiple linear regression model, support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA–RVM–NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8–93.9%.
Deep learning features encode interpretable morphologies within histological images
Authors: Foroughi pour Ali , White Brian S. , Park Jonghanne , Sheridan Todd B. , Chuang Jeffrey H. 
Published: June 2022

AbstractConvolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = $$97.1\% \pm 2.8\%$$97.1%±2.8%for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = $$99.2\% \pm 0.12\%$$99.2%±0.12%). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy.