bluuebunny/crossref_metadata_2025_split huggingface dataset:
Polyoxometalate Induced Assembly Into Surface Functionalized Multidimensional Heterostructures with Enhanced Catalytic Activity
Authors: Akram Bilal , Ali Mudussar , Liu Qingda 
Published: January 2024

AbstractThe self‐assembly of inorganic nanocrystals offers an efficient way for the fabrication of functional materials. However, it is still challenging for the construction of multidimensional nanostructures with controllable shapes, compositions and functions. Here, a series of heterostructures in different dimensions by surface modification of polyoxometalate (POM) clusters is developed. Three kinds of POM clusters (phosphomolybdic acid (PMA), phosphotungstic acid (PTA) and silicotungstic acid (STA) and five kinds of metal oxides (TiO2, VOx, La2O3, In2O3 and Gd2O3) can be used as building blocks, and a class of 1D, 2D and 3D heterostructures can be achieved by the control of surface ligand coverage. Compared with individual building blocks and other cluster‐based superstructures, TiO2‐PMA superstructures exhibit enhanced catalytic activity toward thioether oxidations, which is attributed to the electron transfer between TiO2 and POM clusters.
Bioanalytical method optimization for simultaneous quantification of structurally related probe drugs in a phenotyping cocktail using liquid chromatography‐tandem mass spectrometry
Authors: Leuschner Machel , Cromarty Duncan 
Published: January 2024

AbstractThe physicochemical diversity of the structurally related aromatic probe drugs, used together in a drug cocktail to assess metabolic and transport phenotypes, require optimized analytical procedures for simultaneous quantification. The analytical conditions can greatly influence the analyte selectivity, retention, stability, and ultimately the robustness of the method. The aim of this study was to assess the selectivity of the structurally related ionizable analytes between the commonly used C18 column chemistry and an alternative biphenyl column chemistry as well as the influence of changes in the analytical conditions on method robustness using liquid chromatography‐tandem mass spectrometry. A repeated measure two‐factor analysis of variance with Geisser‐Greenhouse correction was used to determine statistical significance. The results showed that a biphenyl stationary phase in combination with methanol as the organic eluent, could provide improved resolution and analyte selectivity. Changes in analytical conditions caused statistically significant variation in the retention behavior, selectivity, column efficiency, and sensitivity of the analytes of interest The robustness experiment confirmed the importance of controlling analytical conditions to ensure the reproducibility and reliability of the quantitative method.
Enhancing mechanical properties and surface quality of FDM-printed ABS: A comprehensive study on cold acetone vapor treatment
Authors: Demircali Ali Anil , Yilmaz Durmus , Yilmaz Abdurrahim , Keskin Onur , Keshavarz Meysam , Uvet Huseyin 
Published: January 2024

AbstractThe development of additive manufacturing (AM) technologies has significantly advanced fabrication capabilities, yet achieving optimal surface quality and mechanical properties in end-use products is challenging. The primary objective of this study is to improve specific characteristics of 3D-printed components by employing a chemical post-processing technique including acetone. This technique is specifically applied to acrylonitrile butadiene styrene (ABS) material, utilizing a customized mechanical cold-vapor system. A complete investigation was undertaken to assess the effects of treatment on many factors, such as temperature, solvent volume, and exposure duration, on the tensile strength, physical dimensions, and mass of the ABS samples. Acetone post-processing has notably improved tensile strength, influenced by treatment duration and temperature and has led to dimensional changes such as a slight length reduction and increases in width and thickness. Furthermore, the mass of the samples exhibited variability upon acetone treatment, which was shown to be dependent on both the ambient temperature and the duration of solvent exposure. The tensile strength was assessed under various conditions, showing a significant enhancement at higher temperatures and longer exposure times. These results, demonstrating smoother surfaces and a tensile strength increase of up to 20% at 65 °C, underscore the efficacy of our techniques in modifying the mechanical and physical properties of 3D-printed ABS components. This innovative approach provides valuable insights into the relationship between post-processing conditions and ABS properties, enriching the body of knowledge in AM technology.
Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images
Authors: Gende Mateo , Castelo Lúa , de Moura Joaquim , Novo Jorge , Ortega Marcos 
Published: January 2024

AbstractCentral Serous Chorioretinopathy (CSC) is a retinal disorder caused by the accumulation of fluid, resulting in vision distortion. The diagnosis of this disease is typically performed through Optical Coherence Tomography (OCT) imaging, which displays any fluid buildup between the retinal layers. Currently, these fluid regions are manually detected by visual inspection a time-consuming and subjective process that can be prone to errors. A series of six deep learning-based automatic segmentation architectural configurations of different levels of complexity were trained and compared in order to determine the best model intended for the automatic segmentation of CSC-related lesions in OCT images. The best performing models were then evaluated in an external validation study. Furthermore, an intra- and inter-expert analysis was conducted in order to compare the manual segmentation performed by expert ophthalmologists with the automatic segmentation provided by the models. Test results of the best performing configuration achieved a mean Dice of $$0.868 \mathop {\pm } 0.056$$

0.868
±
0.056

in the internal dataset. In the external validation set, these models achieved a level of agreement with human experts of up to 0.960 in terms of Kappa coefficient, contrasting with a value of 0.951 for agreement between human experts. Overall, the models reached a better agreement with either of the human experts than these experts with each other, suggesting that automatic segmentation models for the detection of CSC-related lesions in OCT imaging can be useful tools for assessing this disease, reducing the workload of manual inspection and leading to a more robust and objective diagnosis method.
Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety
Authors: Courtman Megan , Kim Daniel , Wit Huub , Wang Hongrui , Sun Lingfen , Ifeachor Emmanuel , Mullin Stephen , Thurston Mark 
Published: January 2024

AbstractFlagging the presence of metal devices before a head MRI scan is essential to allow appropriate safety checks. There is an unmet need for an automated system which can flag aneurysm clips prior to MRI appointments. We assess the accuracy with which a machine learning model can classify the presence or absence of an aneurysm clip on CT images. A total of 280 CT head scans were collected, 140 with aneurysm clips visible and 140 without. The data were used to retrain a pre-trained image classification neural network to classify CT localizer images. Models were developed using fivefold cross-validation and then tested on a holdout test set. A mean sensitivity of 100% and a mean accuracy of 82% were achieved. Predictions were explained using SHapley Additive exPlanations (SHAP), which highlighted that appropriate regions of interest were informing the models. Models were also trained from scratch to classify three-dimensional CT head scans. These did not exceed the sensitivity of the localizer models. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.
Multiple Kernel Synthesis of Head CT Using a Task-Based Loss Function
Authors: Nelson Brandon J. , Gomez-Cardona Daniel G. , Thorne Jamison E. , Huber Nathan R. , Yu Lifeng , Leng Shuai , McCollough Cynthia H. , Missert Andrew D. 
Published: January 2024

AbstractIn CT imaging of the head, multiple image series are routinely reconstructed with different kernels and slice thicknesses. Reviewing the redundant information is an inefficient process for radiologists. We address this issue with a convolutional neural network (CNN)-based technique, synthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON), that creates a single, thin, low-noise series that combines the favorable features from smooth and sharp head kernels. ZIRCON uses a CNN model with an autoencoder U-Net architecture that accepts two input channels (smooth- and sharp-kernel CT images) and combines their salient features to produce a single CT image. Image quality requirements are built into a task-based loss function with a smooth and sharp loss terms specific to anatomical regions. The model is trained using supervised learning with paired routine-dose clinical non-contrast head CT images as training targets and simulated low-dose (25%) images as training inputs. One hundred unique de-identified clinical exams were used for training, ten for validation, and ten for testing. Visual comparisons and contrast measurements of ZIRCON revealed that thinner slices and the smooth-kernel loss function improved gray-white matter contrast. Combined with lower noise, this increased visibility of small soft-tissue features that would be otherwise impaired by partial volume averaging or noise. Line profile analysis showed that ZIRCON images largely retained sharpness compared to the sharp-kernel input images. ZIRCON combined desirable image quality properties of both smooth and sharp input kernels into a single, thin, low-noise series suitable for both brain and skull imaging.
Development of polysaccharide-coated layered double hydroxide nanocomposites for enhanced oral insulin delivery
Authors: Pang Huiwen , Wu Youzhi , Chen Yang , Chen Chen , Nie Xuqiang , Li Peng , Huang Guojun , Xu Zhi Ping , Han Felicity Y. 
Published: January 2024

AbstractOral insulin (INS) is predicted to have the most therapeutic advantages in treating diabetes to repress hepatic glucose production through its potential to mimic the endogenous insulin pathway. Many oral insulin delivery systems have been investigated. Layered double hydroxide (LDH) as an inorganic material has been widely used in drug delivery thanks to its appealing features such as good biocompatibility, low toxicity, and excellent loading capability. However, when used in oral drug delivery, the effectiveness of LDH is limited due to the acidic degradation in the stomach. In this study, to overcome these challenges, chitosan (Chi) and alginate (Alg) dual-coated LDH nanocomposites with the loading of insulin (Alg-Chi-LDH@INS) were developed by the layered-by-layered method for oral insulin delivery with dynamic size of ~ 350.8 nm, negative charge of ~  − 13.0 mV, and dispersity index 0.228. The insulin release profile was evaluated by ultraviolet–visible spectroscopy. The drug release profiles evidenced that alginate and chitosan coating partially protect insulin release from a burst release in acidic conditions. The analysis using flow cytometry showed that chitosan coating significantly enhanced the uptake of LDH@INS by Caco-2 cells compared to unmodified LDH and free insulin. Further in the in vivo study in streptozocin-induced diabetic mice, a significant hypoglycemic effect was maintained following oral administration with great biocompatibility (~ 50% blood glucose level reduction at 4 h). This research has thus provided a potential nanocomposite system for oral delivery of insulin.
Graphical Abstract
Stable graphene oxide-based lyotropic liquid crystals for interfacial lubrication
Authors: Guo Yumei , Li Hanglin , Li Jiusheng , Zeng Xiangqiong 
Published: January 2024

AbstractLyotropic liquid crystals have lubricating properties due to their ordered assembly and fluidity, whose mesogens are often characterized by amphiphilic properties. Despite the attention that graphene oxide (GO) has been studied as a novel amphiphilic lyotropic mesogen this decade, and GO applied as a lubrication additive has been demonstrated in both oil and water-based systems, little research reveals the interfacial lubrication of GO liquid crystals yet. This work reports that GO aqueous dispersion can form lyotropic liquid crystals above a specific critical concentration of 5.00 mg/mL, providing a form of stable water-based lubricant, which can keep stable for several months and can reduce friction by 37.3% and wear by 25.24%. The liquid crystal phase was verified by polarizing microscope and synchrotron radiation small-angle X-ray scattering, and its rheological properties and viscoelasticity were studied by interfacial rheometer. The formation of lyotropic liquid crystals can enhance the stability of GO aqueous dispersions at high density, simultaneously ensuring friction decrease and anti-wear effect. It is attributed to the stable nematic network by the ordered GO sheets. The ordered assembly structure bears vertical shear force, therefore, reducing the wear. It is also assumed that the wide lateral size of graphene oxide promotes the nematic phase thus smoothes the graphene oxide film composed spontaneously under the coincidence of lamellar liquid crystal and 2D layered material. Through this work, the interlayer lubrication of GO was optimized, and the problem of GO dispersion sedimentation was solved by self-assembly. The range of interfacial lubrication of GO aqueous dispersion has been expanded and the synergistic effect is conducive to the environmentally friendly lubricants.
Polarizable Additive with Intermediate Chelation Strength for Stable Aqueous Zinc-Ion Batteries
Authors: Xia Yuting , Tong Rongao , Zhang Jingxi , Xu Mingjie , Shao Gang , Wang Hailong , Dong Yanhao , Wang Chang-An 
Published: January 2024

AbstractAqueous zinc-ion batteries are promising due to inherent safety, low cost, low toxicity, and high volumetric capacity. However, issues of dendrites and side reactions between zinc metal anode and the electrolyte need to be solved for extended storage and cycle life. Here, we proposed that an electrolyte additive with an intermediate chelation strength of zinc ion—strong enough to exclude water molecules from the zinc metal-electrolyte interface and not too strong to cause a significant energy barrier for zinc ion dissociation—can benefit the electrochemical stability by suppressing hydrogen evolution reaction, overpotential growth, and dendrite formation. Penta-sodium diethylene-triaminepentaacetic acid salt was selected for such a purpose. It has a suitable chelating ability in aqueous solutions to adjust solvation sheath and can be readily polarized under electrical loading conditions to further improve the passivation. Zn||Zn symmetric cells can be stably operated over 3500 h at 1 mA cm−2. Zn||NH4V4O10 full cells with the additive show great cycling stability with 84.6% capacity retention after 500 cycles at 1 A g−1. Since the additive not only reduces H2 evolution and corrosion but also modifies Zn2+ diffusion and deposition, highlyreversible Zn electrodes can be achieved as verified by the experimental results. Our work offers a practical approach to the logical design of reliable electrolytes for high-performance aqueous batteries.
Macro-determinants of current account balance performance in selected African countries
Authors: Abille Adamu Braimah , Meçik Oytun 
Published: January 2024

AbstractMost African countries are open to international trade as shown by their signatories to various trade agreements such as the Economic Partnership Agreement (EPA) with the EU, and the Forum on China-Africa Cooperation (FOCAC). However, these countries face persistent current account deficits and institutional underdevelopment. The existing literature investigates the underlying factors at country-specific levels while ignoring the role of institutions. Meanwhile, African countries are also rapidly transitioning towards economic integration with the establishment of the African Continental Free Trade Area Agreement (AFCFTA) to deepen intra-Africa trade. Therefore, for unionized policy proposition, this study controls for the effects of institutions and investigates the macro-determinants of the current account balance (CAB) performance using panel data from 2002 to 2020 for 20 African countries. To account for cross-country correlation and country-level heteroscedasticity, the study employed panel-corrected standard errors (PCSE) and feasible generalized least squares (FGLS) techniques for the governance indicators-related step-wise estimations. The results show that after controlling for the effects of institutions, income, trade openness, the price level, the interplay between manufacturing and financial sector development, and the money supply are the significant macro-determinants of the CAB performance in Africa. Further, the marginal info-graphics show that the predictive effects of value addition and financial development on the CAB increase positively with good governance and that improved governance could reverse any negative effects of trade openness on the CAB performance. Based on the findings, the study recommends inter alia that African countries pursue value-addition-oriented trade, zero Central Bank budgetary financing, and institutional quality improvement to enhance the performance of their CABs.
A Local Explainability Technique for Graph Neural Topic Models
Authors: Rajendran Bharathwajan , Vidya Chandran G. , Sanil J. , Asharaf S. 
Published: January 2024

AbstractTopic modelling is a Natural Language Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence patterns inside a document corpus to reveal hidden topics. Graph Neural Topic Model (GNTM) is a topic modelling technique that uses Graph Neural Networks (GNNs) to learn document representations effectively. It provides high-precision documents-topics and topics-words probability distributions. Such models find immense application in many sectors, including healthcare, financial services, and safety-critical systems like autonomous cars. This model is not explainable. As a matter of fact, the user cannot comprehend the underlying decision-making process. The paper introduces a technique to explain the documents-topics probability distributions output of GNTM. The explanation is achieved by building a local explainable model such as a probabilistic Naïve Bayes classifier. The experimental results using various benchmark NLP datasets show a fidelity of 88.39% between the predictions of GNTM and the local explainable model. This similarity implies that the proposed technique can effectively explain the documents-topics probability distribution output of GNTM.
Using whole genome sequencing to characterize Clostridioides difficile isolates at a tertiary center in Melbourne, Australia
Authors: Liu Alice , Chan Eddie , Madigan Victoria , Leung Vivian , Dosvaldo Lucille , Sherry Norelle , Howden Benjamin , Bond Katherine , Marshall Caroline 
Published: January 2024

Abstract

Objective:
Clostridioides difficile infection (CDI) is the commonest cause of healthcare-associated diarrhea and undergoes standardized surveillance and mandatory reporting in most Australian states and territories. Historically attributed to nosocomial spread, local and international whole genome sequencing (WGS) data suggest varied sources of acquisition. This study describes C. difficile genotypes isolated at a tertiary center in Melbourne, Australia, their likely source of acquisition, and common risk factors.

Design:
Retrospective observational study.

Setting:
The Royal Melbourne Hospital (RMH), a 570-bed tertiary center in Victoria, Australia.

Methods:
Short-read whole genome sequencing was performed on 75 out of 137 C. difficile isolates obtained from 1/5/2021 to 28/2/2022 and compared to previous data from 8/11/2015 to 1/11/2016. Existing data from infection control surveillance and electronic medical records were used for epidemiological and risk factor analysis.

Results:
Eighty-five (62.1%) of the 137 cases were defined as healthcare-associated from epidemiological data. On genome sequencing, 33 different multi-locus sequence type (MLST) subtypes were identified, with changes in population structure compared to the 2015–16 period. Risk factors for CDI were present in 130 (94.9%) cases, including 108 (78.8%) on antibiotics, 86 (62.8%) on acid suppression therapy, and 25 (18.2) on chemotherapy.

Conclusion:
In both study periods, most C. difficile isolates were not closely related, suggesting varied sources of acquisition and that spread of C. difficile within the hospital was unlikely. Current infection control precautions may therefore warrant review. Underlying risk factors for CDI were common and may contribute to the proportion of healthcare-associated infections in the absence of proven hospital transmission.
Cultivating space for contemporary resistance in Brazil’s Amazon and Cerrado
Authors: Garvey Brian , Mendonça Maria Luisa , Torres Maurício , Stefano Daniela , Pitta Fábio 
Published: January 2024

AbstractThis paper investigates how contemporary labour-capital conflicts in the Cerrado and Amazon biomes of Brazil are centred on the expansion of value from land via dispossession and land titling, and the extraction of value through financial mechanisms that enhance the current and future rent from landholdings. Understanding the consequent territorial struggles between traditional collective ownership on the one hand, and private individual and corporate value capture on the other requires a departure from incumbent capital-(salaried) labour analyses in value chain studies. Resistance to further land capture for speculation reveals inter-and intra-class class tensions, and the facilitative role of the state in validating illicitly grabbed resources. Despite the adverse political conditions in Brazil, there are modest, but significant gains by autonomous land occupations and demarcations in confronting capital expansion. In the face of intensified land grabbing, violent threats, and the laundering of illicit resource extraction, the two cases presented open up new dimensions of, and possibilities for, capital-labour struggles linked to commodity expansion and extraction on resource-rich frontiers.