bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process optimization for gold nanoparticles biosynthesis by Streptomyces albogriseolus using artificial neural network, characterization and antitumor activities
Authors: El-Naggar Noura El-Ahmady , El-Sawah Asmaa A. , Elmansy Mohamed F. , Elmessiry Omar T. , El-Saidy Mohanad E. , El-Sherbeny Mostafa K. , Sarhan Mohamed T. , Elhefnawy Aya Amin , Dalal Shimaa R. 
Published: February 2024

AbstractGold nanoparticles (GNPs) are highly promising in cancer therapy, wound healing, drug delivery, biosensing, and biomedical imaging. Furthermore, GNPs have anti-inflammatory, anti-angiogenic, antioxidants, anti-proliferative and anti-diabetic effects. The present study presents an eco-friendly approach for GNPs biosynthesis using the cell-free supernatant of Streptomyces albogriseolus as a reducing and stabilizing agent. The biosynthesized GNPs have a maximum absorption peak at 540 nm. The TEM images showed that GNPs ranged in size from 5.42 to 13.34 nm and had a spherical shape. GNPs have a negatively charged surface with a Zeta potential of − 24.8 mV. FTIR analysis identified several functional groups including C–H, –OH, C–N, amines and amide groups. The crystalline structure of GNPs was verified by X-ray diffraction and the well-defined and distinct diffraction rings observed by the selected area electron diffraction analysis. To optimize the biosynthesis of GNPs using the cell-free supernatant of S. albogriseolus, 30 experimental runs were conducted using central composite design (CCD). The artificial neural network (ANN) was employed to analyze, validate, and predict GNPs biosynthesis compared to CCD. The maximum experimental yield of GNPs (778.74 μg/mL) was obtained with a cell-free supernatant concentration of 70%, a HAuCl4 concentration of 800 μg/mL, an initial pH of 7, and a 96-h incubation time. The theoretically predicted yields of GNPs by CCD and ANN were 809.89 and 777.32 μg/mL, respectively, which indicates that ANN has stronger prediction potential compared to the CCD. The anticancer activity of GNPs was compared to that of doxorubicin (Dox) in vitro against the HeP-G2 human cancer cell line. The IC50 values of Dox and GNPs-based treatments were 7.26 ± 0.4 and 22.13 ± 1.3 µg/mL, respectively. Interestingly, treatments combining Dox and GNPs together showed an IC50 value of 3.52 ± 0.1 µg/mL, indicating that they targeted cancer cells more efficiently.
Insights into the Dead Sea Transform Activity through the study of fracture-induced electromagnetic radiation (FEMR) signals before the Syrian-Turkey earthquake (Mw-6.3) on 20.2.2023
Authors: Das Shreeja , Frid Vladimir , Rabinovitch Avinoam , Bahat Dov , Kushnir Uri 
Published: February 2024

AbstractObservations of fracture-induced electromagnetic radiation (FEMR) were conducted along the Dead Sea Transform (DST) from Sodom to Jericho, coinciding with a magnitude (Mw) 6.3 aftershock earthquake (EQ) in the Turkey-Syrian region on February 20, 2023. The FEMR parameters (“hits,” Benioff strain release, frequency, rise-time, energy) and associated crack dimensions were analyzed, focusing on trends leading up to the EQ. This study investigated the Benioff Strain plot and other parameters in three consecutive earthquake nucleation stages leading to the catastrophe. The first stage showed increased FEMR hits and frequency, decreased rise time (T′), and crack dimensions. In the second stage, FEMR hits and crack width decreased while other parameters continued to rise, accumulating the second-highest energy, likely due to high-stress drop. The third stage exhibited steadily increasing FEMR hits and energy and a notable increase in crack dimensions, suggesting an imminent macro failure event. The cyclic trend in FEMR hits indicates alternating periods of high activity and silence, potentially linked to stress changes during crack propagation. Taken shortly before the earthquake, these measurements offer valuable insights into how FEMR parameters vary before seismic events, bridging the gap between lab-scale studies of rock collapses under stress and large-scale failure phenomena.
Distribution and elimination kinetics of midazolam and metabolites after post-resuscitation care: a prospective observational study
Authors: Jeong Wonjoon , Sunwoo Jung , You Yeonho , Park Jung Soo , Min Jin Hong , In Yong Nam , Ahn Hong Joon , Jeon So Young , Hong Jang Hee , Song Ji Hye , Kang Hyein , Nguyen My Tuyen Thi , Kim Jaehan , Kang Changshin 
Published: February 2024

AbstractAdministration of sedatives for post-resuscitation care can complicate the determination of the optimal timing to avoid inappropriate, pessimistic prognostications. This prospective study aimed to investigate the distribution and elimination kinetics of midazolam (MDZ) and its metabolites, and their association with awakening time. The concentrations of MDZ and its seven metabolites were measured immediately and at 4, 8, 12, and 24 h after the discontinuation of MDZ infusion, using liquid chromatography-tandem mass spectrometry. The area under the time-plasma concentration curve from 0 to 24 h after MDZ discontinuation (AUClast) was calculated based on the trapezoidal rule. Of the 15 enrolled patients, seven awakened after the discontinuation of MDZ infusion. MDZ and three of its metabolites were major compounds and their elimination kinetics followed a first-order elimination profile. In the multivariable analysis, only MDZ was associated with awakening time (AUClast: R2 = 0.59, p = 0.03; AUCinf: R2 = 0.96, p < 0.001). Specifically, a 0.001% increase in MDZ AUC was associated with a 1% increase in awakening time. In the individual regression analysis between MDZ concentration and awakening time, the mean MDZ concentration at awakening time was 16.8 ng/mL. The AUC of MDZ is the only significant factor associated with the awakening time.
Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization
Authors: Adegboye Oluwatayomi Rereloluwa , Feda Afi Kekeli , Ojekemi Opeoluwa Seun , Agyekum Ephraim Bonah , Hussien Abdelazim G. , Kamel Salah 
Published: February 2024

AbstractThe effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it’s called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.
Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI
Authors: Shahzadi Iram , Seidlitz Annekatrin , Beuthien-Baumann Bettina , Zwanenburg Alex , Platzek Ivan , Kotzerke Jörg , Baumann Michael , Krause Mechthild , Troost Esther G. C. , Löck Steffen 
Published: February 2024

AbstractPersonalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.
Quantitative trait loci and genomic prediction for grain sugar and mineral concentrations of cowpea [Vigna unguiculata (L.) Walp.]
Authors: Huynh Bao-Lam , Stangoulis James C. R. , Vuong Tri D. , Shi Haiying , Nguyen Henry T. , Duong Tra , Boukar Ousmane , Kusi Francis , Batieno Benoit J. , Cisse Ndiaga , Diangar Mouhamadou Moussa , Awuku Frederick Justice , Attamah Patrick , Crossa José , Pérez-Rodríguez Paulino , Ehlers Jeffrey D. , Roberts Philip A. 
Published: February 2024

AbstractDevelopment of high yielding cowpea varieties coupled with good taste and rich in essential minerals can promote consumption and thus nutrition and profitability. The sweet taste of cowpea grain is determined by its sugar content, which comprises mainly sucrose and galacto-oligosaccharides (GOS) including raffinose and stachyose. However, GOS are indigestible and their fermentation in the colon can produce excess intestinal gas, causing undesirable bloating and flatulence. In this study, we aimed to examine variation in grain sugar and mineral concentrations, then map quantitative trait loci (QTLs) and estimate genomic-prediction (GP) accuracies for possible application in breeding. Grain samples were collected from a multi-parent advanced generation intercross (MAGIC) population grown in California during 2016–2017. Grain sugars were assayed using high-performance liquid chromatography. Grain minerals were determined by inductively coupled plasma–optical emission spectrometry and combustion. Considerable variation was observed for sucrose (0.6–6.9%) and stachyose (2.3–8.4%). Major QTLs for sucrose (QSuc.vu-1.1), stachyose (QSta.vu-7.1), copper (QCu.vu-1.1) and manganese (QMn.vu-5.1) were identified. Allelic effects of major sugar QTLs were validated using the MAGIC grain samples grown in West Africa in 2017. GP accuracies for minerals were moderate (0.4–0.58). These findings help guide future breeding efforts to develop mineral-rich cowpea varieties with desirable sugar content.
Longitudinal antibody dynamics after COVID-19 vaccine boosters based on prior infection status and booster doses
Authors: Matsumoto Naomi , Sasaki Ayako , Kadowaki Tomoka , Mitsuhashi Toshiharu , Takao Soshi , Yorifuji Takashi 
Published: February 2024

AbstractGlobal concern over COVID-19 vaccine distribution disparities highlights the need for strategic booster shots. We explored longitudinal antibody responses post-booster during the Omicron wave in a Japanese cohort, emphasizing prior infection and booster doses. This prospective cohort study included 1763 participants aged 18 years and older with at least three vaccine doses (7376 datapoints). Antibody levels were measured every 2 months. We modeled temporal declines in antibody levels after COVID-19 vaccine boosters according to prior infection status and booster doses using a Bayesian linear mixed-effects interval-censored model, considering age, sex, underlying conditions, and lifestyle. Prior infection enhanced post-booster immunity (posterior median 0.346, 95% credible interval [CrI] 0.335–0.355), maintaining antibody levels (posterior median 0.021; 95% CrI 0.019–0.023) over 1 year, in contrast to uninfected individuals whose levels had waned by 8 months post-vaccination. Each additional booster was correlated with higher baseline antibody levels and slower declines, comparing after the third dose. Female sex, older age, immunosuppressive status, and smoking history were associated with lower baseline post-vaccination antibodies, but not associated with decline rates except for older age in the main model. Prior infection status and tailored, efficient, personalized booster strategies are crucial, considering sex, age, health conditions, and lifestyle.
Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules
Authors: Lu Hui , Liu Kaifang , Zhao Huan , Wang Yongqiang , Shi Bo 
Published: February 2024

AbstractThe benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients’ DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.
Optimizing grid-connected PV systems with novel super-twisting sliding mode controllers for real-time power management
Authors: Mohapatra Bhabasis , Sahu Binod Kumar , Pati Swagat , Bajaj Mohit , Blazek Vojtech , Prokop Lukas , Misak Stanislav 
Published: February 2024

AbstractOver the past years, the use of renewable energy sources (RESs) has grown significantly as a means of providing clean energy to counteract the devastating effects of climate change. Reducing energy costs and pollution have been the primary causes of the rise in solar photovoltaic (PV) system integrations with the grid in recent years. A load that is locally connected to a GCPV requires both active and reactive power control. In order to control both active and reactive power, MAs and advanced controllers are essential. Researchers have used one of the recently developed MAs, known as the CAOA, which is based on mathematical arithmetic operators to tackle a few real-world optimization problems. Some disadvantages of CAOA include its natural tendency to converge to a local optimum and its limited capacity for exploration. By merging the PSO and CAOA methodologies, this article suggests the IAOA. To show how applicable IAOA is, its performance has been evaluated using four benchmark functions. The implementation of an IAOA-based ST-SMC for active and reactive power control is addressed in this article, which offers an innovative approach of research. In comparison to PSO-based ST-SMC and CAOA-based ST-SMC, the proposed IAOA-based ST-SMC appears to be superior, with settling time for active and reactive power control at a minimum of 0.01012 s and 0.5075 s. A real-time OPAL-RT 4510 simulator is used to validate the performance results of a 40 kW GCPV system after it has been investigated in the MATLAB environment.
The role of farming and fishing in the rise of social complexity in the Central Andes: a stable isotope perspective
Authors: Pezo-Lanfranco Luis , Colonese André Carlo 
Published: February 2024

AbstractFor many years, the rise of stratified societies along the Central Andean coast, known as the birthplace of Andean civilization, has been closely linked to a marine-oriented economy. This hypothesis has recently been challenged by increasing evidence of plant management and cultivation among Andean populations long before the emergence of complex societies and monumental architecture. The extent to which marine and plant-based economies were integrated and their contributions to early sedentism, population growth, and intra-community stratification, however, remain subjects of ongoing and contentious debate. Using Bayesian Mixing Models we reanalyze the previously published stable isotopes (δ15Ncollagen, δ13Ccollagen, δ13Capatite) values of 572 human individuals from 39 archaeological sites in the Central Andes dated between ca. 7000 BCE and 200 CE to reconstruct dietary regimes in probabilistic terms. Our results reveal that fish, terrestrial fauna, and cultivated plants variably contributed to the diet of prehistoric Andean populations; in coastal and middle valley settlements plant cultivation, not fishing, fueled the development of the earliest complex societies during the Formative Period (from 3000 BCE). Similarly, in the highlands the societies that built ceremonial centers show a plant-based economy. Our findings also show that maize only became a staple food (> 25% dietary contribution) in more recent phases of Andean prehistory, around 500 BCE.
Surface active functional groups enhanced PET non‐woven fabric for the filtering of harmful substances in smoke
Authors: Ning Lu , Tang Pengfei , Lu Xiong , Yang Guangyuan , Jiang Faxing , Zhang Hongping 
Published: February 2024

AbstractHarmful components in cigarette smoke, such as nicotine, tar, and organic particulate matter, are the primary culprits behind lung diseases. While conventional filter materials based on cellulose, carbon, and molecular sieves exhibit commendable filtration capabilities, their high cost restricts their widespread applications. Based on this, the authors aim to prepare PET‐based filter materials with good adsorption properties through a simple surface functionalisation strategy. The adsorption performance of the PET‐based non‐woven fabric was enhanced by the introduction of sodium alginate (SA). The gas adsorption experiments results reveal that SA‐modified PET (SA‐PET) exhibits significantly improved filtration efficiency for nicotine, tar, and total particulate matter—increasing by 27.1%, 26.2%, and 21.3%, respectively. Moreover, SA‐PET exhibits more odour control ability than traditional activated carbon‐based filter materials. These results prove that surface‐functionalised SA‐PET has better filtration performance for harmful substances in smoke and provides a new strategy for the design of high‐performance filtration materials.
Knowledge graph reasoning for cyber attack detection
Authors: Gilliard Ezekia , Liu Jinshuo , Aliyu Ahmed Abubakar 
Published: February 2024

AbstractIn today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.
Towards efficient traffic crash detection based on macro and micro data fusion on expressways: A digital twin framework
Authors: Qu Qikai , Shen Yongjun , Yang Miaomiao , Zhang Rui 
Published: February 2024

AbstractEfficient detection of traffic crashes has been a significant matter of concern with regards to expressway safety management. The current challenge is that, despite collecting vast amounts of data, expressway detection equipment is plagued by low data utilization rates, unreliable crash detection models, and inadequate real‐time updating capabilities. This study is to develop an effective digital twin framework for the detection of traffic crashes on expressways. Firstly, the digital twin technology is used to create a virtual entity of the real expressway. A fusion method for macro and micro traffic data is proposed based on the location of multi‐source detectors on a digital twin platform. Then, a traffic crash detection model is developed using the ThunderGBM algorithm and interpreted by the SHAP method. Furthermore, a distributed strategy for detecting traffic crashes is suggested, where various models are employed concurrently on the digital twin platform to enhance the general detection ability and reliability of the models. Finally, the efficacy of the digital twin framework is confirmed through a case study of certain sections of the Nanjing Ring expressway. This study is expected to lay the groundwork for expressway digital twin studies and offer technical assistance for expressway traffic management.
Crocus sativus updated
Authors: Chrubasik-Hausmann Sigrun 
Published: February 2024

ZusammenfassungDie goldgelben Stigmen des Safrans werden seit der Antike nicht nur für
Kultzwecke und zum Färben, sondern auch als Heilmittel genutzt. Der
Beitrag stellt relevante Forschungsergebnisse der letzten Jahre bei
verschiedenen Indikationen vor.