bluuebunny/crossref_metadata_2025_split huggingface dataset:
Topology selectivity of a conformationally flexible precursor through selenium doping
Authors: Cai Liangliang , Gao Tianhao , Wee Andrew T. S. 
Published: April 2024

AbstractConformational arrangements within nanostructures play a crucial role in shaping the overall configuration and determining the properties, for example in covalent/metal organic frameworks. In on-surface synthesis, conformational diversity often leads to uncontrollable or disordered structures. Therefore, the exploration of controlling and directing the conformational arrangements is significant in achieving desired nanoarchitectures. Herein, a conformationally flexible precursor 2,4,6-tris(3-bromophenyl)−1,3,5-triazine is employed, and a random phase consisting of C3h and Cs conformers is firstly obtained after deposition of the precursor on Cu(111) at room temperature to 365 K. At low coverage (0.01 ML) selenium doping, we achieve the selectivity of the C3h conformer and improve the nanopore structural homogeneity. The ordered two-dimensional metal organic nanostructure can be fulfilled by selenium doping from room temperature to 365 K. The formation of the conformationally flexible precursor on Cu(111) is explored through the combination of high-resolution scanning tunneling microscopy and non-contact atomic force microscopy. The regulation of energy diagrams in the absence or presence of the Se atom is revealed by density functional theory calculations. These results can enrich the on-surface synthesis toolbox of conformationally flexible precursors, for the design of complex nanoarchitectures, and for future development of engineered nanomaterials.
Nerve growth factor receptor limits inflammation to promote remodeling and repair of osteoarthritic joints
Authors: Zhao Lan , Lai Yumei , Jiao Hongli , Huang Jian 
Published: April 2024

AbstractOsteoarthritis (OA) is a painful, incurable disease affecting over 500 million people. Recent clinical trials of the nerve growth factor (NGF) inhibitors in OA patients have suggested adverse effects of NGF inhibition on joint structure. Here we report that nerve growth factor receptor (NGFR) is upregulated in skeletal cells during OA and plays an essential role in the remodeling and repair of osteoarthritic joints. Specifically, NGFR is expressed in osteochondral cells but not in skeletal progenitor cells and induced by TNFα to attenuate NF-κB activation, maintaining proper BMP-SMAD1 signaling and suppressing RANKL expression in mice. NGFR deficiency hyper-activates NF-κB in murine osteoarthritic joints, which impairs bone formation and enhances bone resorption as exemplified by a reduction in subchondral bone and osteophytes. In human OA cartilage, NGFR is also negatively associated with NF-κB activation. Together, this study suggests a role of NGFR in limiting inflammation for repair of diseased skeletal tissues.
Frugivores enhance potential carbon recovery in fragmented landscapes
Authors: Bello Carolina , Crowther Thomas W. , Ramos Danielle Leal , Morán-López Teresa , Pizo Marco A. , Dent Daisy H. 
Published: April 2024

AbstractForest restoration is fundamental to overcoming biodiversity crises and climate change. In tropical forests, animals can improve forest recovery as they disperse >70% of tree species. However, representing animals in restoration and climate change policies remains challenging because a quantitative assessment of their contribution to forest and carbon recovery is lacking. Here we used individual-based models to assess frugivore-mediated seed rain in open areas along a fragmentation gradient. Movements of large birds were limited in landscapes with <40% forest cover, although small birds continued to disperse seeds. Large birds disperse seeds of late-successional species with higher carbon storage potential. Their restricted movement therefore reduced potential biomass in future forests by 38%. Maintaining forest cover >40% is essential to optimizing animals’ contribution to restoration success. Active restoration (for example, planting trees) is required in more fragmented landscapes to achieve carbon and biodiversity targets.
Plant disease recognition using residual convolutional enlightened Swin transformer networks
Authors: Kalpana Ponugoti , Anandan R. , Hussien Abdelazim G. , Migdady Hazem , Abualigah Laith 
Published: April 2024

AbstractAgriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer’s life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.
Protein feature engineering framework for AMPylation site prediction
Authors: Prabhu Hardik , Bhosale Hrushikesh , Sane Aamod , Dhadwal Renu , Ramakrishnan Vigneshwar , Valadi Jayaraman 
Published: April 2024

AbstractAMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model’s capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.
Lab on a bead with oscillatory centrifugal microfluidics for fast and complete mixing enables fast and accurate biomedical assays
Authors: Williams David E. , Li Wei , Chandrasekhar Mithileshwari , Corazza Carsten Ma On Wong , Deijs Gerrit Sjoerd , Djoko Lionel , Govind Bhavesh , Jose Ellen , Kwon Yong Je , Lowe Tiffany , Panchal Anil , Reshef Gabrielle , Vargas Matheus J. T. , Simpson M. Cather 
Published: April 2024

AbstractRapid mixing and precise timing are key for accurate biomedical assay measurement, particularly when the result is determined as the rate of a reaction: for example rapid immunoassay in which the amount of captured target is kinetically determined; determination of the concentration of an enzyme or enzyme substrate; or as the final stage in any procedure that involves a capture reagent when an enzyme reaction is used as the indicator. Rapid mixing and precise timing are however difficult to achieve in point-of-care devices designed for small sample volumes and fast time to result. By using centrifugal microfluidics and transposing the reaction surface from a chamber to a single mm-scale bead we demonstrate an elegant and easily manufacturable solution. Reagents (which may be, for example, an enzyme, enzyme substrate, antibody or antigen) are immobilised on the surface of a single small bead (typically 1–2 mm in diameter) contained in a cylindrical reaction chamber subjected to periodically changing rotational accelerations which promote both mixing and uniform mass-transfer to the bead surface. The gradient of Euler force across the chamber resulting from rotational acceleration of the disc, dΩdisc/dt, drives circulation of fluid in the chamber. Oscillation of Euler force by oscillation of rotational acceleration with period,T, less than that of the hydrodynamic relaxation time of the fluid, folds the fluid streamlines. Movement of the bead in response to the fluid and the changing rotational acceleration provides a dynamically changing chamber shape, further folding and expanding the fluid. Bead rotation and translation driven by fluid flow and disc motion give uniformity of reaction over the surface. Critical parameters for mixing and reaction uniformity are the ratio of chamber radius to bead radius,rchamber/rbead, and the productTrchamber(dΩdisc/dt), of oscillation period and Euler force gradient across the fluid.We illustrate application of the concept using the reaction of horse radish peroxidase (HRP) immobilised on the bead surface with its substrate tetramethylbenzidine (TMB) in solution. Acceleration from rest to break a hydrophobic valve provided precise timing for TMB contact with the bead. Solution uniformity from reaction on the surface of the bead in volumes 20–50 uL was obtained in times of 2.5 s or less. Accurate measurement of the amount of surface-bound HRP by model fitting to the measured kinetics of colour development at 10 s intervals is demonstrated.
Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content
Authors: Fernandes Marcia Helena Machado da Rocha , FernandesJunior Jalme de Souza , Adams Jordan Melissa , Lee Mingyung , Reis Ricardo Andrade , Tedeschi Luis Orlindo 
Published: April 2024

AbstractGrasslands cover approximately 24% of the Earth’s surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.
The potential of carbon markets to accelerate green infrastructure based water quality trading
Authors: Limb Braden J. , Quinn Jason C. , Johnson Alex , Sowby Robert B. , Thomas Evan 
Published: April 2024

AbstractGreen infrastructure solutions can improve in-stream water quality in lieu of building electricity-consuming gray infrastructure. Permitted under the United States Clean Water Act, these programs allow regulated utilities to trade point-source water quality obligations with non-point source mitigation efforts in the watershed. Carbon financing can provide an incentive for water quality trading. Here we combine data on impaired waters, treatment technologies, and life cycle greenhouse gas emissions in the Contiguous United States, and compare traditional treatment technologies to alternative green infrastructure. We find green infrastructure could save $15.6 billion dollars, 21.2 terawatt-hours of electricity, and 29.8 million tonnes of carbon dioxide equivalent emissions per year while sequestering over 4.2 million tonnes CO2e per year over a 40 year time horizon. Green infrastructure solutions may have the potential to generate $679 million annually in carbon credit revenue (at $20 per credit), which represents a unique opportunity to help accelerate water quality trading.
Non-antibiotic pharmaceuticals are toxic against Escherichia coli with no evolution of cross-resistance to antibiotics
Authors: Hall Rebecca J. , Snaith Ann E. , Element Sarah J. , Moran Robert A. , Smith Hannah , Cummins Elizabeth A. , Bottery Michael J. , Chowdhury Kaniz F. , Sareen Dipti , Ahmad Iqbal , Blair Jessica M. A. , Carter Laura J. , McNally Alan 
Published: April 2024

AbstractAntimicrobial resistance can arise in the natural environment via prolonged exposure to the effluent released by manufacturing facilities. In addition to antibiotics, pharmaceutical plants also produce non-antibiotic pharmaceuticals, both the active ingredients and other components of the formulations. The effect of these on the surrounding microbial communities is less clear. We aimed to assess whether non-antibiotic pharmaceuticals and other compounds produced by pharmaceutical plants have inherent toxicity, and whether long-term exposure might result in significant genetic changes or select for cross-resistance to antibiotics. To this end, we screened four non-antibiotic pharmaceuticals (acetaminophen, ibuprofen, propranolol, metformin) and titanium dioxide for toxicity against Escherichia coli K-12 MG1655 and conducted a 30 day selection experiment to assess the effect of long-term exposure. All compounds reduced the maximum optical density reached by E. coli at a range of concentrations including one of environmental relevance, with transcriptome analysis identifying upregulated genes related to stress response and multidrug efflux in response ibuprofen treatment. The compounds did not select for significant genetic changes following a 30 day exposure, and no evidence of selection for cross-resistance to antibiotics was observed for population evolved in the presence of ibuprofen in spite of the differential gene expression after exposure to this compound. This work suggests that these compounds, at environmental concentrations, do not select for cross-resistance to antibiotics in E. coli.
A first-principles study of bilayer 1T'-WTe2/CrI3: a candidate topological spin filter
Authors: Staros Daniel , Rubenstein Brenda , Ganesh Panchapakesan 
Published: April 2024

AbstractThe ability to manipulate electronic spin channels in 2D materials is crucial for realizing next-generation spintronics. Spin filters are spintronic components that polarize spins using external electromagnetic fields or intrinsic material properties like magnetism. Recently, topological protection from backscattering has emerged as an enticing feature that can be leveraged to enhance the robustness of 2D spin filters. In this work, we propose and then characterize one of the first 2D topological spin filters: bilayer CrI3/1T’-WTe2. To do so, we use a combination of density functional theory, maximally localized Wannier functions, and quantum transport calculations to demonstrate that a terraced bilayer satisfies the principal criteria for being a topological spin filter: namely, that it is gapless, exhibits spin-polarized charge transfer from WTe2 to CrI3 that renders the bilayer metallic, and has a topological boundary which retains the edge conductance of monolayer 1T’-WTe2. In particular, we observe that small negative ferromagnetic moments are induced on the W atoms in the bilayer, and the atomic magnetic moments on the Cr are approximately 3.2 μB/Cr compared to 2.9 μB/Cr in freestanding monolayer CrI3. Subtracting the charge and spin densities of the constituent monolayers from those of the bilayer further reveals spin-orbit coupling-enhanced spin-polarized charge transfer from WTe2 to CrI3. We demonstrate that the bilayer is topologically trivial by showing that its Chern number is zero. Lastly, we show that interfacial scattering at the boundary between the terraced materials does not remove WTe2’s edge conductance. Altogether, this evidence indicates that BL 1T’-WTe2/CrI3 is gapless, magnetic, and topologically trivial, meaning that a terraced WTe2/CrI3 bilayer heterostructure in which only a portion of a WTe2 monolayer is topped with CrI3 is a promising candidate for a 2D topological spin filter. Our results further suggest that 1D chiral edge states may be realized by stacking strongly ferromagnetic monolayers, like CrI3, atop 2D nonmagnetic Weyl semimetals like 1T’-WTe2.
We Have to Talk About Something: Why NOT Talk About the Curriculum? A Guide to Embedding Language Interventions in Curricular Content
Authors: Owen Van Horne Amanda J. , Curran Maura , Weatherford Samantha , McGregor Karla K. 
Published: April 2024

Purpose:
Children with developmental language disorder frequently have difficulty with both academic success and language learning and use. This clinical focus article describes core principles derived from a larger program of research (National Science Foundation 1748298) on language intervention combined with science instruction for preschoolers. It serves as an illustration of a model for integrating language intervention with curricular content delivery.

Method:
We present a five-step model for a speech-language pathologist and other school professionals to follow to (a) understand the grade-level core curriculum objectives; (b) align intervention targets with the curriculum; (c) select a therapy approach that aligns with both goals and curricular content, and (d) methods for implementing the intervention; and (e) verify that both the intervention and the curriculum have been provided in accordance with best practices. We apply this model to the Next Generation Science Standards, a science curriculum popular in the United States, and to grammar and vocabulary interventions, two areas of difficulty for children with developmental language disorders, though it would be possible to extend the steps to other curricular areas and intervention targets.

Conclusions:
We conclude by discussing the barriers and benefits to adopting this model. We recognize that both speech-language pathologists and teachers may have limited time to implement language intervention within a general education curriculum, but we suggest that the long-term benefits outweigh the barriers.
Role of perceived ease of use, usefulness, and financial strength on the adoption of health information systems: the moderating role of hospital size
Authors: Luo Ji , Ahmad Sayed Fayaz , Alyaemeni Asma , Ou Yuhan , Irshad Muhammad , Alyafi-Alzahri Randah , Alsanie Ghadeer , Unnisa Syeda Taj 
Published: April 2024

AbstractAdoption of a health information system is always a challenge for hospitals. It is because most of the medical staff do not have enough skills to use the new technology and due to the sensitivity of medical data. These factors pose a challenge for the successful adoption of health information system in hospitals. The aim of this research is to find out the factors which influence the adoption of information systems in hospitals. The study investigated the impact of the Financial status of the Hospital; Perceived Usefulness and Perceived Ease of Use on the adoption of health information systems through a questionnaire survey. Data was collected from 602 healthcare workers from 20 hospitals through close-ended questionnaire in Pakistan, where the adoption of health information systems is very slow. PLS-SEM was used for the analysis. The findings show that the Financial status of the Hospital; Perceived Usefulness and Perceived Ease of Use have positive and significant role in the adoption of Health Information Systems. The finding also shows that hospital size moderates the relationship of Perceived ease of use and the adoption of health information systems and interestingly it does not moderate the relationship among perceived usefulness and financial strength toward the adoption of health information systems. The study concludes that perceived ease of use, perceived usefulness and financial strength are the main factors, necessary for the adoption of health information systems. The findings of the study have useful implications for policy makers, medical professionals to successfully adopt health information systems in hospitals. It also provides new avenues for researchers to explore other factors and test this framework in other countries.
Cyclical dynamics and co-movement of business, credit, and investment cycles: empirical evidence from India
Authors: Garg Ridhima , Sah A. N. 
Published: April 2024

AbstractThe paper aimed to investigate the cyclical dynamics of the business, credit, and investment sectors in India. This was achieved by utilizing annual data from 1980 to 2021 and investigating the impact of domestic and global financial cycles on the business cycle. The cycles were derived using the Hodrick–Prescott filter, and structural vector autoregression (SVAR) and Granger causality tests were employed to establish the dynamic interactions among these cycles. The results of the study revealed a clear divergence between domestic and global financial cycles. Additionally, the SVAR analysis confirmed the presence of a long-run relationship between business, investment, and credit cycles. Notably, the findings suggest that credit cycles can provide valuable insights to manage business cycles in India. Finally, robustness checks were conducted to confirm the reliability of SVAR findings.
Design and simulation optimization of phase-locked loop structure for phase-shifting power supply
Authors: Liu Qingchan , Li Tengbin , Zhong Yao , Lin Cong , Zhang Junlin , Zhao Zhengang 
Published: April 2024

A phase-locked loop (PLL) of a phase-shifting power supply for the power system is designed in this paper. Taking the industrial frequency current as the reference signal, the input and output characteristics of the phase-frequency detector with charge pump are analyzed. In addition, the transfer function of the PLL system is analyzed by combining the passive second-order low-pass filter and the voltage-controlled oscillator. As a result, the influence of parameters such as charge pump current, phase margin, and loop bandwidth on the performance of the PLL is investigated, and the optimal parameters of each section are obtained. The results show that the optimized PLL has a locking time of 0.75 s, a pull-out range of 63.16 Hz, and a locking range of 43.82 Hz, with high stability and noise immunity.