bluuebunny/crossref_metadata_2025_split huggingface dataset:
Customized three-dimensional printed ceramic bone grafts for osseous defects: a prospective randomized study
Authors: Kim Na-hyun , Yang Byoung-Eun , On Sung-Woon , Kwon Ik-Jae , Ahn Kang-Min , Lee Jong-Ho , Byun Soo-Hwan 
Published: February 2024

AbstractRidge resorption can result in insufficient bone volume for implant surgery, necessitating bone substitutes to restore the resorption area. Recent advances in computer-aided design and manufacturing enable the use of alloplastic bone graft materials with customizable compositions or shapes. This randomized study evaluated the clinical effectiveness of a customized three-dimensional (3D) printed alloplastic bone material. Sixty patients requiring guided bone regeneration for implant installation following tooth extraction due to alveolar bone resorption were recruited at two institutions. The participants were randomly allocated to either a group that received 3D-printed patient-customized bone graft material or a group that received conventional block bone graft material. Implant installation with bone harvesting was performed approximately 5 months after bone grafting. Histological and radiological assessments of the harvested bone area were performed. The experimental group had a significantly higher percent bone volume and a smaller tissue surface than the control group. Bone volume, bone surface, bone surface/volume ratio, bone surface density (bone surface/total volume), and bone mineral density did not differ significantly between groups. Patient-customized bone graft materials offer convenience and reduce patient discomfort. The findings suggest 3D-printed patient-customized bone graft materials could be used as an alternative for simpler bone grafting procedures.
Exploring the various effects of Cu doping in hydroxyapatite nanoparticle
Authors: Noori Alireza , Hoseinpour Mahdieh , Kolivand Sedighe , Lotfibakhshaiesh Nasrin , Ebrahimi‐Barough Somayeh , Ai Jafar , Azami Mahmoud 
Published: February 2024

AbstractAdding foreign ions to hydroxyapatite (HAp) is a popular approach for improving its properties. This study focuses on the effects of calcium substitution with copper in HAp. Instead of calcium, copper ions were doped into the structure of hydroxyapatite nanoparticles at 1%, 3%, and 5% concentrations. XRD analysis showed that the amount of substituted copper was less than needed to generate a distinct phase, yet its lattice parameters and crystallinity slightly decreased. Further, the results of degradation tests revealed that copper doping in hydroxyapatite doubled calcium ion release in water. The incorporation of copper into the apatite structure also boosted the HAp zeta potential and FBS protein adsorption onto powders. According to antibacterial investigations, a concentration of 200 mg/ml of hydroxyapatite containing 5% copper was sufficient to effectively eradicate E. coli and S. aureus bacteria. Furthermore, copper improved hydroxyapatite biocompatibility. Alkaline phosphatase activity and alizarin red tests showed that copper in hydroxyapatite did not inhibit stem cell differentiation into osteoblasts. Also, the scratch test demonstrated that copper-containing hydroxyapatite extract increased HUVEC cell migration. Overall, our findings demonstrated the utility of incorporating copper into the structure of hydroxyapatite from several perspectives, including the induction of antibacterial characteristics, biocompatibility, and angiogenesis.
Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs
Authors: Balewski Jan , Amankwah Mercy G. , Van Beeumen Roel , Bethel E. Wes , Perciano Talita , Camps Daan 
Published: February 2024

AbstractCompact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.
Toward an application of automatic evaluation system for central facial palsy using two simple evaluation indices in emergency medicine
Authors: Ikezawa Naoki , Okamoto Takayuki , Yoshida Yoichi , Kurihara Satoru , Takahashi Nozomi , Nakada Taka-aki , Haneishi Hideaki 
Published: February 2024

AbstractA stroke is a medical emergency and thus requires immediate treatment. Paramedics should accurately assess suspected stroke patients and promptly transport them to a hospital with stroke care facilities; however, current assessment procedures rely on subjective visual assessment. We aim to develop an automatic evaluation system for central facial palsy (CFP) that uses RGB cameras installed in an ambulance. This paper presents two evaluation indices, namely the symmetry of mouth movement and the difference in mouth shape, respectively, extracted from video frames. These evaluation indices allow us to quantitatively evaluate the degree of facial palsy. A classification model based on these indices can discriminate patients with CFP. The results of experiments using our dataset show that the values of the two evaluation indices are significantly different between healthy subjects and CFP patients. Furthermore, our classification model achieved an area under the curve of 0.847. This study demonstrates that the proposed automatic evaluation system has great potential for quantitatively assessing CFP patients based on two evaluation indices.
Full-length 16S rDNA sequencing based on Oxford Nanopore Technologies revealed the association between gut-pharyngeal microbiota and tuberculosis in cynomolgus macaques
Authors: Sawaswong Vorthon , Chanchaem Prangwalai , Klomkliew Pavit , Rotcheewaphan Suwatchareeporn , Meesawat Suthirote , Kemthong Taratorn , Kaewparuehaschai Mutchamon , Noradechanon Kirana , Ekatat Monya , Kanitpun Reka , Srilohasin Prapaporn , Warit Saradee , Chaiprasert Angkana , Malaivijitnond Suchinda , Payungporn Sunchai 
Published: February 2024

AbstractTuberculosis (TB) is an infectious disease caused by the Mycobacterium tuberculosis complex (Mtbc), which develops from asymptomatic latent TB to active stages. The microbiome was purposed as a potential factor affecting TB pathogenesis, but the study was limited. The present study explored the association between gut-pharyngeal microbiome and TB stages in cynomolgus macaques using the full-length 16S rDNA amplicon sequencing based on Oxford Nanopore Technologies. The total of 71 macaques was divided into TB (−) control, TB (+) latent and TB (+) active groups. The differential abundance analysis showed that Haemophilus hemolyticus was decreased, while Prevotella species were increased in the pharyngeal microbiome of TB (+) macaques. In addition, Eubacterium coprostanoligenes in the gut was enriched in TB (+) macaques. Alteration of these bacteria might affect immune regulation and TB severity, but details of mechanisms should be further explored and validated. In summary, microbiota may be associated with host immune regulation and affect TB progression. The findings suggested the potential mechanisms of host-microbes interaction, which may improve the understanding of the role of microbiota and help develop therapeutics for TB in the future.
WHOOPH: whale optimization-based optimal placement of hub node within a WBAN
Authors: Shukla Shubham , Sachan Vibhav Kumar , Sinha Anurag , Pandey Saroj Kumar , Rao G. Madhukar , Shah Mohd Asif , Choudhary Amit , Tamrakar Balram 
Published: February 2024

AbstractBiosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.
Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria
Authors: Ibrahim Ali , Senthilkumar Kalimuthu , Saito Kazuki 
Published: February 2024

AbstractThe limited number of agricultural extension agents (EAs) in sub-Saharan Africa limits farmers’ access to extension services. Artificial intelligence (AI) assistants could potentially aid in providing answers to farmers’ questions. The objective of this study was to evaluate the ability of an AI chatbot assistant (ChatGPT) to provide quality responses to farmers’ questions. We compiled a list of 32 questions related to irrigated rice cultivation from farmers in Kano State, Nigeria. Six EAs from the state were randomly selected to answer these questions. Their answers, along with those of ChatGPT, were assessed by four evaluators in terms of quality and local relevancy. Overall, chatbot responses were rated significantly higher quality than EAs’ responses. Chatbot responses received the best score nearly six times as often as the EAs’ (40% vs. 7%). The evaluators preferred chatbot responses to EAs in 78% of cases. The topics for which the chatbot responses received poorer scores than those by EAs included planting time, seed rate, and fertilizer application rate and timing. In conclusion, while the chatbot could offer an alternative source for providing agricultural advisory services to farmers, incorporating site-specific input rate-and-timing agronomic practices into AI assistants is critical for their direct use by farmers.
Synthesis, molecular docking and DFT analysis of novel bis-Schiff base derivatives with thiobarbituric acid for α-glucosidase inhibition assessment
Authors: Gul Saba , Jan Faheem , Alam Aftab , Shakoor Abdul , Khan Ajmal , AlAsmari Abdullah F. , Alasmari Fawaz , Khan Momin , Bo Li 
Published: February 2024

AbstractA library of novel bis-Schiff base derivatives based on thiobarbituric acid has been effectively synthesized by multi-step reactions as part of our ongoing pursuit of novel anti-diabetic agents. All these derivatives were subjected to in vitro α-glucosidase inhibitory potential testing after structural confirmation by modern spectroscopic techniques. Among them, compound 8 (IC50 = 0.10 ± 0.05 µM), and 9 (IC50 = 0.13 ± 0.03 µM) exhibited promising inhibitory activity better than the standard drug acarbose (IC50 = 0.27 ± 0.04 µM). Similarly, derivatives (5, 6, 7, 10 and 4) showed significant to good inhibitory activity in the range of IC50 values from 0.32 ± 0.03 to 0.52 ± 0.02 µM. These derivatives were docked with the target protein to elucidate their binding affinities and key interactions, providing additional insights into their inhibitory mechanisms. The chemical nature of these compounds were reveal by performing the density functional theory (DFT) calculation using hybrid B3LYP functional with 6-311++G(d,p) basis set. The presence of intramolecular H-bonding was explored by DFT-d3 and reduced density gradient (RGD) analysis. Furthermore, various reactivity parameters were explored by performing TD-DFT at CAM-B3LYP/6-311++G(d,p) method.
Pb(ΙΙ), Cd(ΙΙ), and Mn(ΙΙ) adsorption onto pruning-derived biochar: physicochemical characterization, modeling and application in real landfill leachate
Authors: Rabiee Abyaneh Maryam , Nabi Bidhendi Gholamreza , Daryabeigi Zand Ali 
Published: February 2024

AbstractThe aim of this study was to systemically evaluate how different pyrolysis temperatures (400, 550, and 700 °C) and particle sizes (1–2 mm and 63–75 µm) were influenced biochar evolution, made from urban pruning waste, during pyrolysis process and to establish their relationships with biochar potential for removal of lead (Pb), cadmium (Cd), and manganese (Mn) from real municipal solid waste landfill leachate. The effects of pH (2–7), contact time (30–300 min) and adsorbent dosage (0.1–5 g L−1) on heavy metals removal were also examined. The results showed that physicochemical properties of biochar were greatly influenced by pyrolysis temperature. Particle size, however, showed little influence on biochar characteristics (p > 0.05). The yield, volatile matter, hydrogen and oxygen contents, and surface functional groups decreased consistently with increasing pyrolysis temperature. An increase in the pH, electrical conductivity, ash, fixed carbon, and specific surface area values was also found. In biochar samples formed at high temperatures (i.e., 550 and 700 °C), Fourier transform infrared spectroscopy-FTIR studies confirmed the increase in aromaticity. Field emission scanning electron microscopy-FESEM images showed differences in the microporous structure and lower size pores at higher temperatures. Biochar pyrolyzed at 700 °C with a particle size of 63–75 µm (i.e., Lv700-63) showed the highest removal efficiency performance. Pb and Cd ions were completely removed (100%) by 0.2 g L−1 Lv700-63 at 7.0 pH and contact times of 120 and 90 min, respectively. The maximum percentage removal of Mn was 86.20% at optimum conditions of 0.2 g L−1 Lv700-63 dosage, 7.0 pH, and 180 min contact time. The findings suggests that the surface complexation, π-electron coordination, and cation exchange were the dominant mechanisms for the Pb, Cd, and Mn removal onto Lv700-63.
A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department
Authors: Rahmatinejad Zahra , Dehghani Toktam , Hoseini Benyamin , Rahmatinejad Fatemeh , Lotfata Aynaz , Reihani Hamidreza , Eslami Saeid 
Published: February 2024

AbstractThis study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision–recall curve (AUCPR), Hosmer–Lemeshow (H–L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital’s ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802–0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787–0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.
Targeting M2-like tumor-associated macrophages is a potential therapeutic approach to overcome antitumor drug resistance
Authors: Wang Shujing , Wang Jingrui , Chen Zhiqiang , Luo Jiamin , Guo Wei , Sun Lingling , Lin Lizhu 
Published: February 2024

AbstractTumor drug resistance emerges from the interaction of two critical factors: tumor cellular heterogeneity and the immunosuppressive nature of the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) constitute essential components of the TME. M2-like TAMs are essential in facilitating tumor metastasis as well as augmenting the drug resistance of tumors. This review encapsulates the mechanisms that M2-like TAMs use to promote tumor drug resistance. We also describe the emerging therapeutic strategies that are currently targeting M2-like TAMs in combination with other antitumor drugs, with some still undergoing clinical trial evaluation. Furthermore, we summarize and analyze various existing approaches for developing novel drugs that target M2-like TAMs to overcome tumor resistance, highlighting how targeting M2-like TAMs can effectively stop tumor growth, metastasis, and overcome tumor drug resistance.
Room temperature nonlocal detection of charge-spin interconversion in a topological insulator
Authors: Hoque Md. Anamul , Sjöström Lars , Khokhriakov Dmitrii , Zhao Bing , Dash Saroj Prasad 
Published: February 2024

AbstractTopological insulators (TIs) are emerging materials for next-generation low-power nanoelectronic and spintronic device applications. TIs possess non-trivial spin-momentum locking features in the topological surface states in addition to the spin-Hall effect (SHE), and Rashba states due to high spin-orbit coupling (SOC) properties. These phenomena are vital for observing the charge-spin conversion (CSC) processes for spin-based memory, logic and quantum technologies. Although CSC has been observed in TIs by potentiometric measurements, reliable nonlocal detection has so far been limited to cryogenic temperatures up to T = 15 K. Here, we report nonlocal detection of CSC and its inverse effect in the TI compound Bi1.5Sb0.5Te1.7Se1.3 at room temperature using a van der Waals heterostructure with a graphene spin-valve device. The lateral nonlocal device design with graphene allows observation of both spin-switch and Hanle spin precession signals for generation, injection and detection of spin currents by the TI. Detailed bias- and gate-dependent measurements in different geometries prove the robustness of the CSC effects in the TI. These findings demonstrate the possibility of using topological materials to make all-electrical room-temperature spintronic devices.
Optimal design and operation of a wind farm/battery energy storage considering demand side management
Authors: Tao Siyu , Zhang Chaohai , Feijóo‐Lorenzo Andrés E. , Kim Victor 
Published: February 2024

AbstractBalancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) on the consumer side, should be considered during its planning and operation stages. An optimization framework with two levels to simultaneously decide the layout and operation of the WF/BES is put forward in this paper. The first‐level model consists of determining the WF/BES capacities, the WF configuration, and the connection buses. It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. The MOOP model in the second level is transformed to a single‐objective optimization problem via the maximum fuzzy satisfaction method, and is then solved by the genetic algorithm. The proposed model and the strategy are verified by the Barrow offshore WF test case, which is integrated into the IEEE‐118 system. Simulation results indicate that the wind and load patterns, the DSM and the BES price are the three key factors influencing the WF/BES design optimization.