bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of extract guava, bitter melon, broccoli, and shallots on liver damage due to indoor pollutant exposure
Authors: Faisal M , Juswono U P , Santoso D R 
Published: March 2021

Abstract
Air freshener is a chemical-based product commonly used to freshen the air in the room. However, it usually has a variety of harmful compounds, and if it enters the body it can trigger the formation of free radicals, which in turn can cause cell and tissue damage in vital organs such as liver. One way that can be used to treat cell damage due to free radicals is by consuming foods that contain antioxidants. In this study, mice were used to determine the effectiveness of the antioxidant extracts of guava, bitter melon, broccoli, and shallots. Types of free radicals and levels of liver cells damage of mice due to free radicals were analyzed using quantitative atomic spectroscopy and levels assay of malondialdehyde (MDA). The results showed that the effect of the antioxidants mentioned above could reduce the number of free radicals and cell damage that occurred in the liver of mice exposed to air freshener, and the optimal dose of antioxidants was obtained at 69.5 mg per 20 grams of mice.
An experimental study of generating electricity from urban tropical forest plants
Authors: Muladi M. , Jalil M. F. A. , Arifin R. F. , Aripriharta A. , Zaini I. A. E. , Sendari S. , Hidayat S. , Utomo W. M. 
Published: March 2021

Abstract
Coal is a fossil energy source that is still dominant in the generation of world electricity. The use of this energy source causes environmental damages and pollutions. Coal mining causes irreparable damage to land, water, and natural resources around the mine. Exhaust gas emissions from energy generation with coal fuel contribute 44% of total global emissions. Alternative energy sources that can be renewable, sustainable, and environmentally friendly (green) need to be developed to reduce and stop environmental pollution. This research was conducted to explore alternative energy sources that are green, namely the source of electrical energy from living plants. Research on the generation of electrical energy from living plants has been widely carried out and gives satisfying results. In this study, the living plants studied were urban tropical forest plants so that the electricity generated could be used for lighting or sources of charging batteries for electronic devices or electric vehicles. The generation of electrical energy is carried out on each tree by using different electrodes combinations that work according to the principle of the Voltaic cell. The results showed that the combination of the gold-zinc electrode (Au-Zn) produced the highest voltage of 750mV in the sengon tree. The electric voltage generated in each tree species varies according to the combination of the electrodes used and the distance between the two electrodes. Experiments carried out on seven tree species and three combinations of electrodes produced an average electric voltage of 325.6mV. This voltage can be used to charge a 5V battery by connecting 20 cells serially.
Numerical solution of the schrödinger equation with periodic coulomb potential
Authors: Abdurrouf  , Pamungkas M A , Nurhuda M 
Published: March 2021

Abstract
This paper investigates the energy spectrum of a periodic one-dimensional Coulomb potential. The Schrödinger equation is solved numerically by our newly developed filter method (Phys. Rev E 96(3), 033302 (2017)). We observe that the energy spectrum can be obtained with a limited number of lattice. The results show the presence of an energy band structure as a function of lattice width and edge width. The comparison with the other potential model is also discussed.
First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors
Authors: Siviero F. , Arcidiacono R. , Cartiglia N. , Costa M. , Ferrero M. , Legger F. , Mandurrino M. , Sola V. , Staiano A. , Tornago M. 
Published: March 2021

Abstract
RSDs (Resistive AC-Coupled Silicon Detectors) are n-in-p silicon sensors based on the LGAD (Low-Gain Avalanche Diode) technology, featuring a continuous gain layer over the whole sensor area. The truly innovative feature of these sensors is that the signal induced by an ionising particle is seen on several pixels, allowing the use of reconstruction techniques that combine the information from many read-out channels.
In this contribution, the first application of a machine learning technique to RSD devices is presented. The spatial resolution of this technique is compared to that obtained with the standard RSD reconstruction methods that use analytical descriptions of the signal sharing mechanism. A Multi-Output regressor algorithm, trained with a combination of simulated and real data, leads to a spatial resolution of less than 2 μm for a sensor with a 100 μm pixel.
The prospects of future improvements are also discussed.
Regeneration potential of decellularized periodontal ligament cell sheets combined with 15-deoxy-Δ12,14-prostaglandin J2 nanoparticles in a rat periodontal defect
Authors: Jiang Yao , Liu Jia-Mei , Huang Jia-Ping , Lu Ke-Xi , Sun Wei-Lian , Tan Jing-Yi , Li Bo-Xiu , Chen Li-Li , Wu Yan-Min 
Published: March 2021

Abstract
Periodontitis is a chronic inflammatory disease characterized by loss of attachment and destruction of the periodontium. Decellularized sheet, as an advanced tissue regeneration engineering biomaterial, has been researched and applied in many fields, but its effects on periodontal regeneration remain unclear. In this study, the biological properties of decellularized human periodontal ligament cell (dHPDLC) sheets were evaluated in vitro. Polycaprolactone/gelatin (PCL/GE) nanofibers were fabricated as a carrier to enhance the mechanical strength of the dHPDLC sheet. 15-deoxy-

Δ

12
,
14

-prostaglandin J2 (15d-PGJ2) nanoparticles were added for anti-inflammation and regeneration improvement. For in vivo analysis, dHPDLC sheets combined with 15d-PGJ2 nanoparticles, with or without PCL/GE, were implanted into rat periodontal defects. The periodontal regeneration effects were identified by microcomputed tomography (micro-CT) and histological staining, and immunohistochemistry. The results revealed that DNA content was reduced by 96.6%. The hepatocyte growth factor, vascular endothelial growth factor, and basic fibroblast growth factor were preserved but reduced. The expressions or distribution of collagen I and fibronectin were similar in dHPDLC and nondecellularized cell sheets. The dHPDLC sheets maintained the intact structure of the extracellular matrix. It could be recellularized by allogeneic human periodontal stem ligament cells and retain osteoinductive potential. Newly formed bone, cementum, and PDL were observed in dHPDLC sheets combined with 15d-PGJ2 groups, with or without PCL/GE nanofibers, for four weeks post-operation in vivo. Bringing together all these points, this new construct of dHPDLC sheets can be a potential candidate for periodontal regeneration in an inflammatory environment of the oral cavity.
Changing Amazon deforestation patterns: urgent need to restore command and control policies and market interventions
Authors: Trancoso Ralph 
Published: March 2021

Abstract
Market and public policies govern deforestation trends and patterns globally. Here I show that in the Brazilian Amazon, the largest tropical forest in the world, the size of deforestation polygons - the individual portions of cleared forest patches - has significantly increased in response to the current environmental governance. The average size of deforestation polygons in the current government is 61% greater than in the 10 previous years when environmental policies and programs were maintained. As a result, very large polygons (> 100 ha) are now dominating deforestation, suggesting a remarkable change in deforestation patterns and a new wave of destruction of the Amazon forest. To control increasing deforestation trends and changing patterns, command and control policies need to be strengthened along with interventions in the supply chain of Amazon commodities and sustainable development incentives, ensuring a transition to an environmentally sustainable economy.
Challenges and advantages of electrospun nanofibers in agriculture: a review
Authors: Meraz-Dávila S , Pérez-García C E , Feregrino-Perez Ana A 
Published: March 2021

Abstract
The application of nanotechnology in the field of agriculture has been growing piecemeal; however, it has been very limited only to the use of nanoparticles with respect to the intake, accumulation, and interaction of these in plants specifically in cells, tissues, and soils. Nevertheless, the effect of the mechanism of contribution in plants and sprouts has not been thoroughly studied. In the area of nanotechnology, the research of nanofiber materials is still considerably more limited with respect to the impact on plant metabolism. Even so, it has been observed that the fabrication of nanofibers has many advantages due to simplicity, low cost of production, high porosity, and contact surface. In this review, we will explore some applications and advantages of the use of nanofibers by electrospinning in agricultural applications mainly in growth and protection of the seeds, plants, and soils as well as in the detection of compounds.
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
Authors: Li Pengyong , Wang Jun , Qiao Yixuan , Chen Hao , Yu Yihuan , Yao Xiaojun , Gao Peng , Xie Guotong , Song Sen 
Published: March 2021

Abstract
How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method
Authors: Wu Zhenxing , Jiang Dejun , Hsieh Chang-Yu , Chen Guangyong , Liao Ben , Cao Dongsheng , Hou Tingjun 
Published: March 2021

Abstract
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure–activity relationship (QSAR) methods have been established as indispensable tools that facilitate fast, reliable and affordable assessments of physicochemical and biological properties of compounds in drug-discovery programs. Currently, there are mainly two types of QSAR methods, descriptor-based methods and graph-based methods. The former is developed based on predefined molecular descriptors, whereas the latter is developed based on simple atomic and bond information. In this study, we presented a simple but highly efficient modeling method by combining molecular graphs and molecular descriptors as the input of a modified graph neural network, called hyperbolic relational graph convolution network plus (HRGCN+). The evaluation results show that HRGCN+ achieves state-of-the-art performance on 11 drug-discovery-related datasets. We also explored the impact of the addition of traditional molecular descriptors on the predictions of graph-based methods, and found that the addition of molecular descriptors can indeed boost the predictive power of graph-based methods. The results also highlight the strong anti-noise capability of our method. In addition, our method provides a way to interpret models at both the atom and descriptor levels, which can help medicinal chemists extract hidden information from complex datasets. We also offer an HRGCN+'s online prediction service at https://quantum.tencent.com/hrgcn/.
HCMMCNVs: hierarchical clustering mixture model of copy number variants detection using whole exome sequencing technology
Authors: Song Chi , Su Shih-Chi , Huo Zhiguang , Vural Suleyman , Galvin James E , Chang Lun-Ching 
Published: March 2021

Abstract

Summary
In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package ‘HCMMCNVs’ is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts.

Availability and implementation
HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM_CNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371.

Supplementary information
Supplementary data are available at Bioinformatics online.
Immaterial Monuments, Narrative Inequality and Glocal Social Work. Towards Critical Participatory Community Art-Based Practices
Authors: Livholts Mona B 
Published: March 2021

Abstract
The aim of this study is to ask critical questions about the immaterial narrative force of monuments and social justice related to place and space, and to develop an interdisciplinary, participatory, art-based methodology for the study of narrative inequality in glocal social work. The study begins with the glocal turn in social work, a condition characterised by a critical understanding of the interlinked and intersectional localities within a world perspective, theoretically and methodologically reshaped towards space, the arts, architecture and interdisciplinary practice-based collaborations. It develops participatory art as a tool for dialogue and critical spatial practice in urban environment by drawing upon critical scholarship on participatory art projects and the author’s experiences as a guest participant within the project ‘Immaterial Monuments’ in the Western Balkans. Experiences from this project are used as a catalyst to illustrate and discuss how participatory community art and social sculpture, with illustrations from photography and poetry, are useful for visualising the language of narrative inequality. The study argues that the question of how immaterial monuments reflect the worldviews and dominant narratives of privileged elites restrict spaces for marginalised people, and that glocal social work research, education and practice needs to promote anti-discriminatory and social justice perspectives through the critical study of immaterial monuments.
Low-protein diet accelerates wound healing in mice post-acute injury
Authors: Hew Jonathan J , Parungao Roxanne J , Mooney Craig P , Smyth Julian K , Kim Sarah , Tsai Kevin H-Y , Shi Huaikai , Chong Cassandra , Chan Renee C F , Attia Beba , Nicholls Caroline , Li Zhe , Solon-Biet Samantha M , Le Couteur David G , Simpson Stephen J , Jeschke Marc G , Maitz Peter K , Wang Yiwei 
Published: March 2021

AbstractBackgroundWound healing processes are influenced by macronutrient intake (protein, carbohydrate and fat). The most favourable diet for cutaneous wound healing is not known, although high-protein diets are currently favoured clinically. This experimental study investigates the optimal macronutrient balance for cutaneous wound healing using a mouse model and the Geometric Framework, a nutrient modelling method, capable of analyzing the individual and interactive effects of a wide spectrum of macronutrient intake.MethodsTwo adjacent and identical full-thickness skin excisions (1 cm2) were surgically created on the dorsal area of male C57BL/6 mice. Mice were then allocated to one of 12 high-energy diets that varied in protein, carbohydrate and fat content. In select diets, wound healing processes, cytokine expression, energy expenditure, body composition, muscle and fat reserves were assessed.ResultsUsing the Geometric Framework, we show that a low-protein intake, coupled with a balanced intake of carbohydrate and fat is optimal for wound healing. Mice fed a low-protein diet progressed quickly through wound healing stages with favourable wound inflammatory cytokine expression and significantly accelerated collagen production. These local processes were associated with an increased early systemic inflammatory response and a higher overall energy expenditure, related to metabolic changes occurring in key macronutrient reserves in lean body mass and fat depots.ConclusionsThe results suggest that a low-protein diet may have a greater potential to accelerate wound healing than the current clinically used high-protein diets.
Post-cyclone (Aila), transforming lives and gendered relations in Bangladesh
Authors: Roy Sajal , Mathbor Golam M 
Published: March 2021

Abstract
This study examines the long-term effects of Cyclone Aila on the traditional livelihood of the Munda community in the Sundarbans forest of Bangladesh, together with its socially and culturally assigned gendering. Building on existing critiques of gender and critical developmental studies, the present study uses an ethnographic approach and traces resilience alongside a new gender equality between males and females, as they jointly discover new commercial and home industry occupations to replace the damaged Sundarbans forest heritage, discarding gendered role stereotypes in the jobs they do and the way they contribute to family income.
One Way or Another: Cortical Language Areas Flexibly Adapt Processing Strategies to Perceptual And Contextual Properties of Speech
Authors: Klimovich-Gray Anastasia , Barrena Ander , Agirre Eneko , Molinaro Nicola 
Published: March 2021

Abstract
Cortical circuits rely on the temporal regularities of speech to optimize signal parsing for sound-to-meaning mapping. Bottom-up speech analysis is accelerated by top–down predictions about upcoming words. In everyday communications, however, listeners are regularly presented with challenging input—fluctuations of speech rate or semantic content. In this study, we asked how reducing speech temporal regularity affects its processing—parsing, phonological analysis, and ability to generate context-based predictions. To ensure that spoken sentences were natural and approximated semantic constraints of spontaneous speech we built a neural network to select stimuli from large corpora. We analyzed brain activity recorded with magnetoencephalography during sentence listening using evoked responses, speech-to-brain synchronization and representational similarity analysis. For normal speech theta band (6.5–8 Hz) speech-to-brain synchronization was increased and the left fronto-temporal areas generated stronger contextual predictions. The reverse was true for temporally irregular speech—weaker theta synchronization and reduced top–down effects. Interestingly, delta-band (0.5 Hz) speech tracking was greater when contextual/semantic predictions were lower or if speech was temporally jittered. We conclude that speech temporal regularity is relevant for (theta) syllabic tracking and robust semantic predictions while the joint support of temporal and contextual predictability reduces word and phrase-level cortical tracking (delta).
School Mental Health Providers’ Perspectives on the Impact of COVID-19 on Racial Inequities and School Disengagement
Authors: Jones Tiffany M , Williford Anne , Spencer Michael S , Riggs Nathaniel R , Toll Rebecca , George Melissa , Becker Kaylee , Bruick Samantha 
Published: March 2021

Abstract
School disengagement is a critical factor that will likely exacerbate long-standing racial inequities in educational outcomes during the aftermath of the coronavirus disease (COVID-19) pandemic. Due to their training and contact with at-risk students, school social workers and other school-based mental health professionals (SMHP) are in an ideal position to understand the impact of COVID-19 and virtual learning on K–12 students. To that end, this study reports on findings from a survey of SMHP about the differential impact that the COVID-19 outbreak is having on students and their families. The findings suggest that COVID-19 has had a tremendous impact on families overall, especially among populations who were experiencing hardships before the current outbreak. In addition, several barriers were noted for student learning, with many students—particularly students of color—completely disengaging from school during spring 2020. However, SMHP reported several important insights on how to support students and their families—some requiring immediate action on the part of school districts and others requiring greater community-level investment into the economic, social, emotional, and overall health of families. Findings are discussed in light of addressing disengagement and educational inequities for students of color.
Guided by images—Chilean novice teachers’ visions of themselves
Authors: Glas Katharina , Dittmar Patricia , Allendes Paz 
Published: March 2021

Abstract
Mental imagery of ‘future selves’ is considered a powerful means for motivating learners, and also teachers, whose own motivation is an important basis for the creation of engaging teaching–learning environments. This multiple case study accompanied Chilean novice teachers of English over 18 months and explores the use of visualizations of teacher selves as a reflection tool in early career development. What images of themselves and their classroom environments do novice teachers create when prompted to visualize themselves as ‘motivating teachers’? What role do these visualizations play in supporting novice teachers in implementing methodological innovations? And do novice teachers value the use of visualizations? Two contrasting cases demonstrate variability in the evolution of visions, and illustrate how these can fulfil different roles in assisting teachers during their induction phase.