bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxidative Stress and Inflammation in B-Cell Lymphomas
Authors: Sousa-Pimenta Mário , Estevinho Maria Manuela , Sousa Dias Miguel , Martins Ângelo , Estevinho Letícia M.
Published: April 2023

Mature lymphoid neoplasms arise de novo or by the transformation of more indolent lymphomas in a process that relies on the stepwise accumulation of genomic and transcriptomic alterations. The microenvironment and neoplastic precursor cells are heavily influenced by pro-inflammatory signaling, regulated in part by oxidative stress and inflammation. Reactive oxygen species (ROSs) are by-products of cellular metabolism able to modulate cell signaling and fate. Moreover, they play a crucial role in the phagocyte system, which is responsible for antigen presentation and the selection of mature B and T cells under normal conditions. Imbalances in pro-oxidant and antioxidant signaling can lead to physiological dysfunction and disease development by disrupting metabolic processes and cell signaling. This narrative review aims to analyze the impact of reactive oxygen species on lymphomagenesis, specifically examining the regulation of microenvironmental players, as well as the response to therapy for B-cell-derived non-Hodgkin lymphomas. Further research is needed to investigate the involvement of ROS and inflammation in the development of lymphomas, which may unravel disease mechanisms and identify innovative therapeutic targets.
Using Machine Learning to Explore the Risk Factors of Problematic Smartphone Use among Canadian Adolescents during COVID-19: The Important Role of Fear of Missing Out (FoMO)
Authors: Xiao Bowen , Parent Natasha , Rahal Louai , Shapka Jennifer
Published: April 2023

The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys; Mage = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use.
Oral Health and Use of Novel Transbuccal Drug Delivery Systems in Patients with Alzheimer’s and Parkinson’s Disease: A Review
Authors: Basilicata Michele , Grillo Piergiorgio , Tancredi Alvise , Di Fiore Adolfo , Bollero Patrizio , Stefani Alessandro , Schirinzi Tommaso
Published: April 2023

Neurodegenerative disorders, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are common age-related diseases responsible for high disability. Disease-modifying treatments for AD and PD are still lacking, but symptomatic therapies are available, although limited by difficult administration and patients’ scarce compliance at later disease stages. Transbuccal Drug Delivery Systems (TDDSs) include chemical-physics biotechnologies and mechatronic approaches, allowing drug delivery via the transbuccal route, a strategy that may theoretically overcome the limitations imposed by conventional oral administration. In this review, we provided a snapshot of TDDSs, their mechanism of action, the existing subtypes, and their potential application in PD and AD patients. We found a variety of TDDSs, including tablets, solutions, sprays, patches, and the more sophisticated “mechatronic” IntelliDrug and OraFuse devices using a system of pumps and valves for continuous drug release. Several trials have been conducted either on models or patients to test the safety and efficacy of the antidementia and antiparkinsonian agents delivered by TDDSs, which produced encouraging results that suggest future application on a larger scale. Moreover, oral health has emerged as a fundamental prerequisite for the successful use of TDDSs. Accordingly, greater attention to oral hygiene is now due in patients with neurodegenerative disease.
Smoothness and Efficiency Metrics Behavior after an Upper Extremity Training with Robic Humanoid Robot in Paediatric Spinal Cord Injured Patients
Authors: Salas-Monedero Miriam , Cereijo-Herranz Víctor , DelosReyes-Guzmán Ana , Pérez-Borrego Yolanda , Gil-Agudo Angel , García-Martín Fuensanta , Pulido-Pascual José-Carlos , López-Dolado Elisa
Published: April 2023

The upper extremity behavior in smoothness and efficiency metrics should be different between paraplegic and tetraplegic patients. The aim of this article was to analyze the behavior of these metrics after receiving upper extremity training with the humanoid robot Robic as a treatment. Ten pediatric patients participated in the study and completed ten experimental sessions with Robic. Patients were assessed at baseline and at ending the training using the Box and Block test and a non-immersive virtual application based on the Leap Motion Controller available in the RehabHand software. From this application, the smoothness metric was calculated as the number of peaks or units of movement detected in the velocity profile of the hand during the execution of the task, and the efficiency metric was assessed by calculating the length of the hand trajectory. Patients with tetraplegia had a significantly longer trajectory (286.01 ± 59.87 mm) than paraplegics (123.61 ± 17.14 mm) in the baseline situation. However, at the end of the training, there were no differences between them. In the Box and Block test, the paraplegic group passed more cubes than tetraplegics. In conclusion, the first experience with a Robic robot in SCI was very positive, with observed improvements in upper extremity dexterity in trained patients.
Development and Characterization of Ibrutinib-Loaded Ethylcellulose-Based Nanosponges: Cytotoxicity Assay against MCF-7 Cell Lines
Authors: Fatima Farhat , Anwer Md. Khalid
Published: April 2023

Ibrutinib (ITB) is a specific and novel irreversible inhibitor of Bruton’s tyrosine kinase enzyme, for which reason it exhibits potential chemotherapeutic effects against a few types of B-cell cancers. The objective of this study was to design and characterize the targeted anti-cancer moiety of ITB encapsulated in polymeric nanosponges (IBNS 1-5). The IBNSs were fabricated using the ultrasonication-assisted solvent evaporation technique. They were optimized for robust nanocarriers by varying the ratio of ethylcellulose (50–200 mg), using a constant amount 50 mg of polyvinyl alcohol ((PVA) stabilizer), and drug ITB. Optimized INBS4 containing 50 mg of ITB, PVA, and 162.5 mg of EC was prepared and was studied for anti-cancer potential. Particle analysis and EE and DL calculation of optimized IBNS4 were 640.9 nm, 0.35, −30.2 mV in size, PDI, and ζp, respectively. Physicochemical characterization (FTIR and DSC) studies of IBNS4 showed that the drug was compatible with excipients, and was encapsulated properly within the core of nanosponges. In vitro drug release studies revealed that IBNS4 followed the Higuchi matrix model with anomalous non-Fickian release kinetics. The in vitro diffusion study of I-NS4 exhibited sustained release for 24 h. Enhanced cytotoxicity effects against the MCF-7 observed with the developed NSs (IBNS4) showed 1.96 times more cytotoxic potential compared to the pure drug (ITB).
Implementation of the EPIC Earthquake Early Warning System in the Bursa Province (Türkiye) and Its Surroundings
Authors: Tunç Süleyman , Tunç Berna , Budakoğlu Emrah , Çaka Deniz , Nof Ran Novitsky , Barış Şerif
Published: April 2023

Earthquake early warning systems aim to reduce the potential danger by providing a warning in the seconds before strong ground shaking occurs. In this study, we implemented EPIC, an early warning algorithm for Bursa province and its surroundings, which is a seismically active region. We replayed 104 earthquakes of M ≥ 3.5, which occurred in and around the study area between 2012 and 2021. We derived period and amplitude-based magnitude-scaling relationships using peak displacement amplitude (Pd) and predominant period (Tpmax) parameters of the first 4 s of P-wave arrivals. We investigated the performance of magnitude-scaling relationships through testing with real-time data. We observed an improvement when comparing the magnitude estimates made with the newly developed equations with the default equations used for California. We have also found that magnitude estimation with Pd gives better results than Tpmax for estimating the accurate final magnitude. We aim to adapt the EPIC early warning system, implemented for Bursa province and its surroundings, specifically for each region of Türkiye where the earthquake risk is high.
Analysis of Phonetic Segments of Oesophageal Speech in People Following Total Laryngectomy
Authors: Tyburek Krzysztof , Mikołajewski Dariusz , Rojek Izabela
Published: April 2023

This paper presents an approach to extraction techniques for speaker recognition following total laryngectomy surgery. The aim of the research was to develop a pattern of physical features describing the oesophageal speech in people after experiencing laryngeal cancer. Research results may support the speech rehabilitation of laryngectomised patients by improving the quality of oesophageal speech. The main goal of the research was to isolate the physical features of oesophageal speech and to compare their values with the descriptors of physiological speech. Words (in Polish) used during speech rehabilitation were analyzed. Each of these words was divided into phonetic segments from which the physical features of speech were extracted. The values of the acquired speech descriptors were then used to create a vector of the physical features of oesophageal speech. A set of these features will determine a model that should allow us to recognize whether the speech-rehabilitation process is proceeding correctly and also provide a selection of bespoke procedures that we could introduce to each patient. This research is a continuation of the analysis of oesophageal speech published previously. This time, the effectiveness of parameterization was tested using methodologies for analyzing the phonetic segments of each word.
Investigation on the Application of Artificial Intelligence in Prosthodontics
Authors: Alshadidi Abdulkhaliq Ali F. , Alshahrani Ahid Amer , Aldosari Lujain Ibrahim N. , Chaturvedi Saurabh , Saini Ravinder S. , Hassan Saeed Awod Bin , Cicciù Marco , Minervini Giuseppe
Published: April 2023

Artificial intelligence (AI) is a contemporary, information-driven innovative technology. Prosthetic dentistry, also known as prosthodontics, is the restoration and reconstruction of missing teeth utilizing implants for permanent and removable prostheses. It enhances healthy soft and hard tissues, promoting oral health. This study examined the use of artificial intelligence in prosthodontics to diagnose abnormalities and create patient-specific prostheses. Two researchers searched Google Scholar, Scopus, PubMed/MEDLINE, EBSCO host, Science Direct, and Web of Science (MEDLINE, WOS, and KJD). Articles on AI in English were reviewed. We also collected the following broad article aspects: research and control groups, assessment methodology, outcomes, and quality rankings. This methodological study examined AI use in prosthodontics using the latest scientific findings. The findings were statistically evaluated using ANOVA. Titles and abstracts revealed 172 AI-related dentistry studies, which were analyzed in this research. Thirty-eight papers were eliminated. According to the evaluation, AI was found to have significantly increased in prosthodontics. Despite the vast number of studies documenting AI applications, the description of the data illustrated the latest breakthroughs in AI in prosthodontics, highlighting its use in automatically produced diagnostics, predicting analytics, and classification or verification tools.
New Trends in Biosciences II
Authors: Ballini Andrea , Di Stasio Dario , Boccaccio Antonio , Contaldo Maria
Published: April 2023

Biosciences encompass an area of international scientific research that focuses on the mechanisms underpinning and regulating biological processes in organisms across all branches of life [...]
Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection
Authors: Chauhan Alok Singh , Lilhore Umesh Kumar , Gupta Amit Kumar , Manoharan Poongodi , Garg Ruchi Rani , Hajjej Fahima , Keshta Ismail , Raahemifar Kaamran
Published: April 2023

Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is figuring out, based on the patient’s traits, if the Kyphosis condition will continue after the treatment. There have been numerous models employed in the past to predict the Kyphosis disease, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Deep Neural Network (DNN), and others. Unfortunately, the precision was overestimated. Based on the dataset received from Kaggle, we investigated how to predict Kyphosis disorders more accurately by using these models with Hyperparameter tuning. While the calculations were being performed, certain variables were modified. The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN models’ accuracy was 87.72% with 5-fold cross-validation and 87.64% with 10-fold cross-validation. It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foresee Kyphosis disease. Medical experts can use this study’s findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that deep learning should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries.
An Efficient Spring Model for an Integrated Orthodontic Tooth Movement: A Verified Mathematical Model
Authors: Yona Shai , Medina Oded , Sarig Rachel , Shvalb Nir
Published: April 2023

Orthodontic tooth movement is of interest to both the medical and the engineering communities. Recent studies focused their attention mainly on the stress distribution within the periodontal ligament and the surrounding alveolar bone prior to the remodeling stage. Yet, although motion is indeed triggered by the exerted stress distribution, these remodeling processes are the main driver for significant (and permanent) tooth movements. Other studies attempted to provide such a holistic mechanical model for both the stress distribution and the remodeling processes to describe the movement of the tooth along an orthodontic treatment. Nevertheless, these methods are cumbersome and slow to run, and therefore, are unlikely to provide a clinical decision support platform. This paper aims to bridge this gap by providing a relaxed, simplified numerical model. The scheme is described, and its limitations and main assumptions are stated. The model is then optimized to accommodate clinical accuracy needs. Lastly, validation is provided by comparing the model to a recent study, which demonstrates the good agreement between the model and actual real-world clinical cases.
Transformative Technology for FLASH Radiation Therapy
Authors: Schulte Reinhard , Johnstone Carol , Boucher Salime , Esarey Eric , Geddes Cameron G. R. , Kravchenko Maksim , Kutsaev Sergey , Loo Billy W. , Méot François , Mustapha Brahim , Nakamura Kei , Nanni Emilio A. , Obst-Huebl Lieselotte , Sampayan Stephen E. , Schroeder Carl B. , Sheng Ke , Snijders Antoine M. , Snively Emma , Tantawi Sami G. , Van Tilborg Jeroen
Published: April 2023

The general concept of radiation therapy used in conventional cancer treatment is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and radiation beams that deliver a radiation dose with high conformality, e.g., protons and ions. However, the treatment and cure are still limited by normal tissue radiation toxicity, with the corresponding side effects. A fundamentally different paradigm for increasing the therapeutic index of radiation therapy has emerged recently, supported by preclinical research, and based on the FLASH radiation effect. FLASH radiation therapy (FLASH-RT) is an ultra-high-dose-rate delivery of a therapeutic radiation dose within a fraction of a second. Experimental studies have shown that normal tissues seem to be universally spared at these high dose rates, whereas tumors are not. While dose delivery conditions to achieve a FLASH effect are not yet fully characterized, it is currently estimated that doses delivered in less than 200 ms produce normal-tissue-sparing effects, yet effectively kill tumor cells. Despite a great opportunity, there are many technical challenges for the accelerator community to create the required dose rates with novel compact accelerators to ensure the safe delivery of FLASH radiation beams.
A Compound Class of Inverse-Power Muth and Power Series Distributions
Authors: Barrios-Blanco Leonardo , Gallardo Diego I. , Gómez Héctor J. , Bourguignon Marcelo
Published: April 2023

This paper introduces the inverse-power Muth power series model, which is a composition of the inverse-power Muth and the class of power series distributions. The use of the Bell distribution in this context is emphasized for the first time in the literature. Probability density, survival and hazard functions are studied, as well as their moments. Using the stochastic representation of the model, the maximum-likelihood estimators are implemented by the use of the expectation-maximization algorithm, while standard errors are calculated using Oakes’ method. Monte Carlo simulation studies are conducted to show the performance of the maximum-likelihood estimators in finite samples. Two applications to real datasets are shown, where our proposal is compared with some models based on power series compositions.
Barrier Options and Greeks: Modeling with Neural Networks
Authors: Umeorah Nneka , Mashele Phillip , Agbaeze Onyecherelam , Mba Jules Clement
Published: April 2023

This paper proposes a non-parametric technique of option valuation and hedging. Here, we replicate the extended Black–Scholes pricing model for the exotic barrier options and their corresponding Greeks using the fully connected feed-forward neural network. Our methodology involves some benchmarking experiments, which result in an optimal neural network hyperparameter that effectively prices the barrier options and facilitates their option Greeks extraction. We compare the results from the optimal NN model to those produced by other machine learning models, such as the random forest and the polynomial regression; the output highlights the accuracy and the efficiency of our proposed methodology in this option pricing problem. The results equally show that the artificial neural network can effectively and accurately learn the extended Black–Scholes model from a given simulated dataset, and this concept can similarly be applied in the valuation of complex financial derivatives without analytical solutions.
Developing a Deep Learning-Based Defect Detection System for Ski Goggles Lenses
Authors: Dang Dinh-Thuan , Wang Jing-Wein
Published: April 2023

Ski goggles help protect the eyes and enhance eyesight. The most important part of ski goggles is their lenses. The quality of the lenses has leaped with technological advances, but there are still defects on their surface during manufacturing. This study develops a deep learning-based defect detection system for ski goggles lenses. The first step is to design the image acquisition model that combines cameras and light sources. This step aims to capture clear and high-resolution images on the entire surface of the lenses. Next, defect categories are identified, including scratches, watermarks, spotlight, stains, dust-line, and dust-spot. They are labeled to create the ski goggles lenses defect dataset. Finally, the defects are automatically detected by fine-tuning the mobile-friendly object detection model. The mentioned defect detection model is the MobileNetV3 backbone used in a feature pyramid network (FPN) along with the Faster-RCNN detector. The fine-tuning includes: replacing the default ResNet50 backbone with a combination of MobileNetV3 and FPN; adjusting the hyper-parameter of the region proposal network (RPN) to suit the tiny defects; and reducing the number of the output channel in FPN to increase computational performance. Our experiments demonstrate the effectiveness of defect detection; additionally, the inference speed is fast. The defect detection accuracy achieves a mean average precision (mAP) of 55%. The work automatically integrates all steps, from capturing images to defect detection. Furthermore, the lens defect dataset is publicly available to the research community on GitHub. The repository address can be found in the Data Availability Statement section.
Pallidal Recordings in Chronically Implanted Dystonic Patients: Mitigation of Tremor-Related Artifacts
Authors: Del Vecchio Del Vecchio Jasmin , Hanafi Ibrahem , Pozzi Nicoló Gabriele , Capetian Philipp , Isaias Ioannis U. , Haufe Stefan , Palmisano Chiara
Published: April 2023

Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the PerceptTM PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.
Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques
Authors: Alizargar Azadeh , Chang Yang-Lang , Tan Tan-Hsu
Published: April 2023

Hepatitis C is a liver infection caused by the hepatitis C virus (HCV). Due to the late onset of symptoms, early diagnosis is difficult in this disease. Efficient prediction can save patients before permeant liver damage. The main objective of this study is to employ various machine learning techniques to predict this disease based on common and affordable blood test data to diagnose and treat patients in the early stages. In this study, six machine learning algorithms (Support Vector Machine (SVM), K-nearest Neighbors (KNN), Logistic Regression, decision tree, extreme gradient boosting (XGBoost), artificial neural networks (ANN)) were utilized on two datasets. The performances of these techniques were compared in terms of confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), and the area under the curve (AUC) to identify a method that is appropriate for predicting this disease. The analysis, on NHANES and UCI datasets, revealed that SVM and XGBoost (with the highest accuracy and AUC among the test models, >80%) can be effective tools for medical professionals using routine and affordable blood test data to predict hepatitis C.
Standardization of Animal Models and Techniques for Platelet-Rich Fibrin Production: A Narrative Review and Guideline
Authors: Mourão Carlos Fernando , Lowenstein Adam , Mello-Machado Rafael Coutinho , Ghanaati Shahram , Pinto Nelson , Kawase Tomoyuki , Alves Gutemberg Gomes , Messora Michel Reis
Published: April 2023

Experimental research is critical for advancing medical knowledge and enhancing patient outcomes, including in vitro and in vivo preclinical assessments. Platelet-rich fibrin (PRF) is a blood by-product that has garnered attention in the medical and dental fields due to its potential for tissue regeneration and wound healing. Animal models, such as rabbits and rats, have been used to produce PRF and examine its properties and applications. PRF has demonstrated potential in the dental and medical fields for reducing inflammation, promoting tissue repair, and accelerating wound healing. This narrative review aims to compare existing evidence and provide guidelines for PRF animal research, emphasizing the importance of standardizing animal models, following ethical considerations, and maintaining transparency and accountability. The authors highlight the necessity to use the correct relative centrifugal force (RCF), standardize centrifugal calibration, and report detailed information about blood collection and centrifuge parameters for reproducible results. Standardizing animal models and techniques is crucial for narrowing the gap between laboratory research and clinical applications, ultimately enhancing the translation of findings from bench to bedside.
Potential Molecular Mechanisms of Alzheimer’s Disease from Genetic Studies
Authors: Nwadiugwu Martin , Shen Hui , Deng Hong-Wen
Published: April 2023

The devastating effects of Alzheimer’s disease (AD) are yet to be ameliorated due to the absence of curative treatment options. AD is an aging-related disease that affects cognition, and molecular imbalance is one of its hallmarks. There is a need to identify common causes of molecular imbalance in AD and their potential mechanisms for continuing research. A narrative synthesis of molecular mechanisms in AD from primary studies that employed single-cell sequencing (scRNA-seq) or spatial genomics was conducted using Embase and PubMed databases. We found that differences in molecular mechanisms in AD could be grouped into four key categories: sex-specific features, early-onset features, aging, and immune system pathways. The reported causes of molecular imbalance were alterations in bile acid (BA) synthesis, PITRM1, TREM2, olfactory mucosa (OM) cells, cholesterol catabolism, NFkB, double-strand break (DSB) neuronal damage, P65KD silencing, tau and APOE expression. What changed from previous findings in contrast to results obtained were explored to find potential factors for AD-modifying investigations.
Spatial–Temporal Distribution of Fish Larvae in the Pearl River Estuary Based on Habitat Suitability Index Model
Authors: Wang Dongliang , Yu Jing , Lin Zhaojin , Chen Pimao
Published: April 2023

The spawning grounds are important areas for the survival and reproduction of aquatic organisms and play an important role in the replenishment of fishery resources. The density of fish larvae in the Pearl River Estuary (PRE) was analyzed to establish Habitat Suitability Index (HSI) based on marine environmental factors. Survey data and satellite remote sensing data, including sea surface temperature, sea surface salinity and chlorophyll a concentration, from 2014 to 2017 during April–September were analyzed. Results showed that the accuracy of the HSI model based on the larval density and environmental factors was more than 60%, and the distribution trend of HSI was consistent with the distribution trend of larval density. The HSI models constructed based on Arithmetic Mean Model (AMM), Geometric Mean Model (GMM) and Minimum Model (MINM) methods can better predict the spatial–temporal distribution of larvae in the PRE. Among them, the accuracy of the HSI model constructed by the AMM and GMM methods was the highest in April (71%) and September (93%); the accuracy of the HSI model constructed by the MINM method was the highest in June (70%), July (84%) and August (64%). In general, the areas with high HSI values are mainly distributed in the offshore waters of the PRE. The spatial–temporal distribution of larvae in the PRE was influenced by monsoon, Pearl River runoff, Guangdong coastal currents and the invasion of high-salinity seawater from the outer sea.
Adaptability of Wild-Growing Tulips of Greece: Uncovering Relationships between Soil Properties, Rhizosphere Fungal Morphotypes and Nutrient Content Profiles
Authors: Bilias Fotis , Karagianni Anastasia-Garyfallia , Ipsilantis Ioannis , Samartza Ioulietta , Krigas Nikos , Tsoktouridis Georgios , Matsi Theodora
Published: April 2023

Wild-growing Greek tulips are protected plants but almost nothing is known about their natural nutrient status and rhizosphere fungal morphotypes in the wild, thus no insight is currently available into their growth and adaptation to their natural environment or artificial settings. To this end, several botanical expeditions were conducted with a special collection permit, and 34 tulip and soil samples were collected, representing 13 species from two phytogeographical regions of Greece (North Aegean Islands, Crete Island) and seven regions of mainland Greece. The tulips’ content in essential macro- and micro-nutrients, respective physicochemical soil properties, and rhizosphere fungal morphotypes were assessed across samples, and all parameters were subjected to appropriate statistical analysis to determine their interrelationships. The results showed that soil variables played a significant role in shaping tulips’ nutrient content, explaining up to 67% of the detected variability as in the case of phosphorus (P) in the above-ground plant tissue. In addition, significant correlations were observed (with an r value of up to 0.65, p < 0.001) between essential nutrients in the tulips, such as calcium (Ca) and boron (B). The principal component analysis (PCA) revealed that between the three spatial units examined, the total variability of tulips’ nutrient content produced a clear distinction among sampled species, while the first two PCA axes managed to explain 44.3% of it. This was further confirmed by the analysis of variance (ANOVA) results which showed corresponding significant differences (at p < 0.05) in both the tulips’ nutrient content and the studied soil properties as well (mean values of N, P, and K in the North Aegean Islands tulips’ nutrient content, up to 53%, 119%, and 54% higher compared to those of the Crete Island, respectively). Our study sheds light on Greek tulips’ adaptability and resilience in their original habitats, facilitating at the same time the undertaken efforts regarding their conservation and potential domestication in artificial settings.
Lunar Cycle, Climate, and Onset of Parturition in Domestic Dromedary Camels: Implications of Species-Specific Metabolic Economy and Social Ecology
Authors: Iglesias Pastrana Carlos , Navas González Francisco Javier , Delgado Bermejo Juan Vicente , Ciani Elena
Published: April 2023

Given energy costs for gestating and caring for male offspring are higher than those of female newborns, external environmental conditions might be regarded as likely to affect the timing of delivery processes differentially depending on the sex of the newborn calf to be delivered. The aim of the present paper is to evaluate the association between environmental stressors such as the moon phase and weather-related factors and the onset of labor in female dromedaries. A binary logistic regression model was developed to find the most parsimonious set of variables that are most effective in predicting the probability for a gravid female dromedary to give birth to a male or a female calf, assuming that higher gestational costs and longer labor times are ascribed to the production of a male offspring. Although the differences in the quantitative distribution of spontaneous onset of labor across lunar phases and the mean climate per onset event along the whole study period were deemed nonsignificant (p > 0.05), a non-negligible prediction effect of a new moon, mean wind speed and maximum wind gust was present. At slightly brighter nights and lower mean wind speeds, a calf is more likely to be male. This microevolutionary response to the external environment may have been driven by physiological and behavioral adaptation of metabolic economy and social ecology to give birth to cooperative groups with the best possible reduction of thermoregulatory demands. Model performance indexes then highlighted the heterothermic character of camels to greatly minimize the impact of the external environment. The overall results will also enrich the general knowledge of the interplay between homeostasis and arid and semi-arid environments.
Does Enhanced Structural Maturity of hiPSC-Cardiomyocytes Better for the Detection of Drug-Induced Cardiotoxicity?
Authors: Van de Sande Dieter , Ghasemi Mohammadreza , Watters Taylor , Burton Francis , Pham Ly , Altrocchi Cristina , Gallacher David J. , Lu Huarong , Smith Godfrey
Published: April 2023

Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are currently used following the Comprehensive in vitro Proarrhythmic Assay (CiPA) initiative and subsequent recommendations in the International Council for Harmonization (ICH) guidelines S7B and E14 Q&A, to detect drug-induced cardiotoxicity. Monocultures of hiPSC-CMs are immature compared to adult ventricular cardiomyocytes and might lack the native heterogeneous nature. We investigated whether hiPSC-CMs, treated to enhance structural maturity, are superior in detecting drug-induced changes in electrophysiology and contraction. This was achieved by comparing hiPSC-CMs cultured in 2D monolayers on the current standard (fibronectin matrix, FM), to monolayers on a coating known to promote structural maturity (CELLvo™ Matrix Plus, MM). Functional assessment of electrophysiology and contractility was made using a high-throughput screening approach involving the use of both voltage-sensitive fluorescent dyes for electrophysiology and video technology for contractility. Using 11 reference drugs, the response of the monolayer of hiPSC-CMs was comparable in the two experimental settings (FM and MM). The data showed no functionally relevant differences in electrophysiology between hiPSC-CMs in standard FM and MM, while contractility read-outs indicated an altered amplitude of contraction but not changes in time course. RNA profiling for cardiac proteins shows similarity of the RNA expression across the two forms of 2D culture, suggesting that cell-to-matrix adhesion differences may explain account for differences in contraction amplitude. The results support the view that hiPSC-CMs in both 2D monolayer FM and MM that promote structural maturity are equally effective in detecting drug-induced electrophysiological effects in functional safety studies.
Phytochemical Interactions with Calmodulin and Critical Calmodulin Binding Proteins Involved in Amyloidogenesis in Alzheimer’s Disease
Authors: O'Day Danton H.
Published: April 2023

An increasing number of plant-based herbal treatments, dietary supplements, medical foods and nutraceuticals and their component phytochemicals are used as alternative treatments to prevent or slow the onset and progression of Alzheimer’s disease. Their appeal stems from the fact that no current pharmaceutical or medical treatment can accomplish this. While a handful of pharmaceuticals are approved to treat Alzheimer’s, none has been shown to prevent, significantly slow or stop the disease. As a result, many see the appeal of alternative plant-based treatments as an option. Here, we show that many phytochemicals proposed or used as Alzheimer’s treatments share a common theme: they work via a calmodulin-mediated mode of action. Some phytochemicals bind to and inhibit calmodulin directly while others bind to and regulate calmodulin-binding proteins, including Aβ monomers and BACE1. Phytochemical binding to Aβ monomers can prevent the formation of Aβ oligomers. A limited number of phytochemicals are also known to stimulate calmodulin gene expression. The significance of these interactions to amyloidogenesis in Alzheimer’s disease is reviewed.
The α-Synuclein Monomer May Have Different Misfolding Mechanisms in the Induction of α-Synuclein Fibrils with Different Polymorphs
Authors: Zhao Nannan , Zhang Qianqian , Yu Fansen , Yao Xiaojun , Liu Huanxiang
Published: April 2023

The aggregation of alpha-synuclein (α-Syn) is closely related to the occurrence of some neurodegenerative diseases such as Parkinson’s disease. The misfolding of α-Syn monomer plays a key role in the formation of aggregates and extension of fibril. However, the misfolding mechanism of α-Syn remains elusive. Here, three different α-Syn fibrils (isolated from a diseased human brain, generated by in vitro cofactor-tau induction, and obtained by in vitro cofactor-free induction) were selected for the study. The misfolding mechanisms of α-Syn were uncovered by studying the dissociation of the boundary chains based on the conventional molecular dynamics (MD) and Steered MD simulations. The results showed that the dissociation paths of the boundary chains in the three systems were different. According to the reverse process of dissociation, we concluded that in the human brain system, the binding of the monomer and template starts from the C-terminal and gradually misfolds toward the N-terminal. In the cofactor-tau system, the monomer binding starts from residues 58–66 (contain β3), followed by the C-terminal coil (residues 67–79). Then, the N-terminal coil (residues 36–41) and residues 50–57 (contain β2) bind to the template, followed by residues 42–49 (contain β1). In the cofactor-free system, two misfolding paths were found. One is that the monomer binds to the N/C-terminal (β1/β6) and then binds to the remaining residues. The other one is that the monomer binds sequentially from the C- to N-terminal, similar to the human brain system. Furthermore, in the human brain and cofactor-tau systems, electrostatic interactions (especially from residues 58–66) are the main driving force during the misfolding process, whereas in the cofactor-free system, the contributions of electrostatic and van der Waals interactions are comparable. These results may provide a deeper understanding for the misfolding and aggregation mechanism of α-Syn.
Navigating Value Co-Destruction in Open Innovation Communities: An Empirical Study of Expectancy Disconfirmation and Psychological Contracts in Business Analytics Communities
Authors: Daradkeh Mohammad
Published: April 2023

Enterprises seeking to enhance their innovation capabilities are increasingly turning to open innovation communities (OICs), which allow them to leverage the collective knowledge and collaborative potential of external users, providing a powerful source of new and innovative ideas. Despite their potential for value co-creation, recent research suggests that value co-destruction can also occur within OICs. However, the mechanisms underlying value co-destruction in OICs have not yet been fully explored or empirically examined. To address this gap, this study employs expectancy disconfirmation theory and psychological contract theory to investigate the relationship between user expectancy disconfirmation and value co-destruction in OICs. Drawing upon data collected from a questionnaire survey of business analytics OICs, this study reveals that self-interest expectancy disconfirmation has a positive effect on value co-destruction, which is mediated by the transactional psychological contract breach. In addition, social interaction expectancy disconfirmation is found to have a positive impact on value co-destruction, which is mediated by the relational psychological contract breach. The study further reveals that self-worth expectancy disconfirmation of community users positively influences value co-destruction, which is mediated by the ideological psychological contract breach. Moreover, the study demonstrates the crucial role of perceived organizational status in moderating the ideological psychological contract breach resulting from self-worth expectancy disconfirmation. Collectively, these findings contribute valuable insights into the phenomenon of value co-destruction in OICs, and provide practical guidance for enterprises seeking to enhance the development and performance of these innovation paradigms.
Ageing Suitability Evaluation of Residential Districts Based on Active Ageing Theory
Authors: Xu Yuxin , Liu Hui , Su Shu , Mao Peng
Published: April 2023

Residential districts, the primary locations for older adults, struggle to satisfy the needs of the rising older population due to ageing infrastructure, etc. China has begun to focus on this issue through the popularization of approaches such as active ageing theory (AAT), and has implemented ageing suitability renovations. Meanwhile, recent evaluation models failed to validate their logic and standardization. The current evaluation models prioritize the indoor environment above the external environment. Therefore, this study attempts to provide a comprehensive evaluation model of the ageing suitability of residential districts. First, 23 indicators of ageing suitability were identified by a systematic literature review and expert interviews; next, the indicators were categorized using the active ageing theory. Second, the grey correlation analysis approach was used to calculate the weights of evaluation indicators, and the existing evaluation criteria were incorporated to formulate the evaluation criteria for the project. Eventually, the evaluation model was constructed. Finally, the feasibility of the model was validated by a case study. The findings of this study can help provide directions for the future renovation of old residential districts (for example, the activity area should be over 200 m2) and the construction of suitable houses for older adults, such as the installation of a continuous handrail, to alleviate the increasingly grave problem of ageing and promote the development of the pension industry.
Photocatalytic Activity and Mechanical Properties of Cement Slurries Containing Titanium Dioxide
Authors: Jabali Yasmine , Assaad Joseph , Aouad Georges
Published: April 2023

The effect of titanium dioxide (TiO2) on the mechanical properties of cement slurries including their benefits on air purification and abatement of pollutants is not well documented. Cementitious-based slurries are typically applied in thin layers as decorative coatings for existing facades, protection against an ingress of aggressive ions, or rainproof covers to minimize water penetration. Different parameters including the TiO2 concentration, dispersion time during batching, and applied thickness on top of existing mortar blocks are investigated in this paper. Tested properties included the flow, colorimetry, compressive/flexural strengths, bond to existing substrates, water absorption, and photocatalytic activity evaluated using an ISO 22197-1:2007 reactor. The results showed that the mechanical properties remarkably improved with TiO2 additions, up to 8% of the cement mass. This was attributed to two concomitant phenomena including a micro-filler effect that enhances the packing density and nucleation sites to promote strength development. The removal of nitrogen oxides from the atmosphere reached 92% when the TiO2 was added at a rate of 5% of the cement mass. Such data can be of particular interest to consultants and environmental activists searching for innovative materials capable of maintaining better ambient air quality in urban and modern cities.
Developing a Construction-Oriented DfMA Deployment Framework
Authors: Rankohi Sara , Bourgault Mario , Iordanova Ivanka , Carbone Carlo
Published: April 2023

Applying design for manufacture and assembly (DfMA) principles in the construction industry has gained attention in recent years. Studies convey that the application of DfMA in construction projects can significantly enhance overall productivity. However, the literature on construction-oriented DfMA is still limited, and its application in real-life projects has been stifled due to various constraints. Following a design science research method, a systematic literature review was conducted to identify the construction-oriented DfMA implementation challenges. To address these challenges, a construction-oriented DfMA framework was theorized, verified in a project-based context, and validated through focus group discussions with off-site construction industry experts. In this study, 45 challenges were identified and categorized into eight main constraint categories: contractual, technological, procedural, cultural, commercial, geographical, financial, and technical/cognitive. The foremost challenges to the adoption of DfMA in construction projects seems to relate to the contractual and operational aspects and their associated stakeholders. This study provides insight into the challenges of implementing DfMA in the construction industry. The investigated challenges contribute to the theoretical and practice-based checklists of limitations for implementing DfMA methods and can inform future research. Finally, this paper introduces a framework for implementing DfMA and provides supporting field-based evidence for its application.
Cyber Technology Implementation Barriers for Sustainable Buildings: A Novel Mathematical Partial Least Square Structural Equation Modelling
Authors: Kineber Ahmed Farouk , Oke Ayodeji Emmanuel , Qaralleh Thikryat Jibril Obied , Alaboud Naif Sultan , Alshahrani Adnan , Alaboud Mosaab , Daoud Ahmed Osama
Published: April 2023

To reap the most advantages while maintaining the functioning of residential building projects, sustainability concepts should be included at all stages of the construction decision-making process. This research identified and investigated the barriers to the employment of cyber technology in residential construction projects in order to ensure their long-term viability. Prior research identified cyber technology barriers, which were then contextually explored using a questionnaire survey in the Nigerian construction business. An exploratory factor analysis (EFA) revealed that cyber technology hurdles may be classified into five constructs: knowledge, government, culture, project nature, and regulations. The barriers model was also built using partial least square structural equation modelling (PLS-SEM). According to the findings, project-related constraints were key impediments to the implementation of cyber technology. The findings of this study might serve as a guide for decision-makers in Nigeria’s construction industry looking to decrease costs and boost sustainability via the use of cyber technology.