bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expermental investigation on adsorption of methylene blue dye from waste water using corncob cellulose-based hydrogel
Authors: Majamo Samuel Latebo , Amibo Temesgen Abeto , Mekonnen Dereje Tadesse 
Published: February 2024

AbstractHydrogel from corncob cellulose was synthesized in this investigation. The synthesized Hydrogel was characterized by SEM, XRD, and FTIR instruments. As the results indicate the synthesized hydrogel has required and important features, these suggest the suitability of hydrogel for the adsorption of methylene blue dye (MBD). Three important process variables (dosage, contact time, and initial concentration) with three levels were studied during the adsorption process at 30 °C and neutral pH. The efficiency of hydrogel for adsorption of MBD was determined in each experiment. The experimental results were statistically analyzed and interpreted. The maximum removal efficiency was achieved at 2.22 g/L of dosage, 80.36 min of contact time, and 74.54 mg/L of initial concentration. At this condition, 98.25% of MBD was achieved through experimental tests. Kinetics, isotherm, and thermodynamics studies were performed. Langmuir isotherm is more suitable to describe the adsorption process and the Pseudo second-order kinetic model fits this process. From the thermodynamics studies, all negative values of change in Gibbs free energy (ΔG°), and positive value of change in enthalpy (ΔH°), and change in entropy (ΔS°) indicate that the carried out experimental process is a spontaneous and endothermic. Moreover, the regeneration experiment for adsorbent was performed. The treatment of real textile industry waste water was conducted and the removal efficiency of hydrogel was 64.76%. This removal percentage reduction from sythetic aqueous solution is due to involvement of other pollutants in the real waste water. The synthesized hydrogel adsorbent is suitable up to the third cycle without significant loss in removal efficiency.
Foraging behavior and age affect maternal transfer of mercury to northern elephant seal pups
Authors: Peterson Sarah H. , Peterson Michael G. , Ackerman Joshua T. , Debier Cathy , Goetsch Chandra , Holser Rachel R. , Hückstädt Luis A. , Johnson Jennifer C. , Keates Theresa R. , McDonald Birgitte I. , McHuron Elizabeth A. , Costa Daniel P. 
Published: February 2024

AbstractDeep ocean foraging northern elephant seals (Mirounga angustirostris) consume fish and squid in remote depths of the North Pacific Ocean. Contaminants bioaccumulated from prey are subsequently transferred by adult females to pups during gestation and lactation, linking pups to mercury contamination in mesopelagic food webs (200–1000 m depths). Maternal transfer of mercury to developing seal pups was related to maternal mercury contamination and was strongly correlated with maternal foraging behavior (biotelemetry and isotopes). Mercury concentrations in lanugo (hair grown in utero) were among the highest observed worldwide for young pinnipeds (geometric mean 23.01 μg/g dw, range 8.03–63.09 μg/g dw; n = 373); thus, some pups may be at an elevated risk of sub-lethal adverse health effects. Fetal mercury exposure was affected by maternal foraging geographic location and depth; mercury concentrations were highest in pups of the deepest diving, pelagic females. Moreover, pup lanugo mercury concentrations were strongly repeatable among successive pups of individual females, demonstrating relative consistency in pup mercury exposure based on maternal foraging strategies. Northern elephant seals are biosentinels of a remote deep-sea ecosystem. Our results suggest that mercury within North Pacific mesopelagic food webs may also pose an elevated risk to other mesopelagic-foraging predators and their offspring.
Ethnic and functional differentiation of copy number polymorphisms in Tunisian and HapMap population unveils insights on genome organizational plasticity
Authors: Romdhane Lilia , Kefi Sameh , Mezzi Nessrine , Abassi Najla , Jmel Haifa , Romdhane Safa , Shan Jingxuan , Chouchane Lotfi , Abdelhak Sonia 
Published: February 2024

AbstractAdmixture mapping has been useful in identifying genetic variations linked to phenotypes, adaptation and diseases. Copy number variations (CNVs) represents genomic structural variants spanning large regions of chromosomes reaching several megabases. In this investigation, the “Canary” algorithm was applied to 102 Tunisian samples and 991 individuals from eleven HapMap III populations to genotype 1279 copy number polymorphisms (CNPs). In this present work, we investigate the Tunisian population structure using the CNP makers previously identified among Tunisian. The study revealed that Sub-Saharan African populations exhibited the highest diversity with the highest proportions of allelic CNPs. Among all the African populations, Tunisia showed the least diversity. Individual ancestry proportions computed using STRUCTURE analysis revealed a major European component among Tunisians with lesser contribution from Sub-Saharan Africa and Asia. Population structure analysis indicated the genetic proximity with Europeans and noticeable distance from the Sub-Saharan African and East Asian clusters. Seven genes harbouring Tunisian high-frequent CNPs were identified known to be associated with 9 Mendelian diseases and/or phenotypes. Functional annotation of genes under selection highlighted a noteworthy enrichment of biological processes to receptor pathway and activity as well as glutathione metabolism. Additionally, pathways of potential concern for health such as drug metabolism, infectious diseases and cancers exhibited significant enrichment. The distinctive genetic makeup of the Tunisians might have been influenced by various factors including natural selection and genetic drift, resulting in the development of distinct genetic variations playing roles in specific biological processes. Our research provides a justification for focusing on the exclusive genome organization of this population and uncovers previously overlooked elements of the genome.
Metal-insulator transition effect on Graphene/VO$$_\text {2}$$ heterostructure via temperature-dependent Raman spectroscopy and resistivity measurement
Authors: Lerttraikul Kittitat , Rattanasakuldilok Wirunchana , Pakornchote Teerachote , Bovornratanaraks Thiti , Klanurak Illias , Taychatanapat Thiti , Srathongsian Ladda , Seriwatanachai Chaowaphat , Kanjanaboos Pongsakorn , Chatraphorn Sojiphong , Kittiwatanakul Salinporn 
Published: February 2024

AbstractHigh-quality VO$$_2$$

2

films were fabricated on top of c-Al$$_2$$

2

O$$_3$$

3

substrates using Reactive Bias Target Ion Beam Deposition (RBTIBD) and the studies of graphene/VO$$_2$$

2

heterostructure were conducted. Graphene layers were placed on top of $$\sim$$
∼
50 and $$\sim$$
∼
100 nm VO$$_2$$

2

. The graphene layers were introduced using mechanical exfoliate and CVD graphene wet-transfer method to prevent the worsening crystallinity of VO$$_2$$

2

, to avoid the strain effect from lattice mismatch and to study how VO$$_2$$

2

can affect the graphene layer. Slight increases in graphene/VO$$_2$$

2

T$$_\text {MIT}$$

MIT

compared to pure VO$$_2$$

2

by $$\sim$$
∼
1.9 $$^{\circ }$$

∘

C and $$\sim$$
∼
3.8 $$^{\circ }$$

∘

C for CVD graphene on 100 and 50 nm VO$$_2$$

2

, respectively, were observed in temperature-dependent resistivity measurements. As the strain effect from lattice mismatch was minimized in our samples, the increase in T$$_\text {MIT}$$

MIT

may originate from a large difference in the thermal conductivity between graphene and VO$$_2$$

2

. Temperature-dependent Raman spectroscopy measurements were also performed on all samples, and the G-peak splitting into two peaks, G$$^{+}$$

+

and G$$^{-}$$

-

, were observed on graphene/VO$$_2$$

2

(100 nm) samples. The G-peak splitting is a reversible process and may originates from in-plane asymmetric tensile strain applied under the graphene layer due to the VO$$_2$$

2

phase transition mechanism. The 2D-peak measurements also show large blue-shifts around 13 cm$$^{-1}$$

-
1

at room temperature and slightly red-shifts trend as temperature increases for 100 nm VO$$_2$$

2

samples. Other electronic interactions between graphene and VO$$_2$$

2

are expected as evidenced by 2D-peak characteristic observed in Raman measurements. These findings may provide a better understanding of graphene/VO$$_2$$

2

and introduce some new applications that utilize the controllable structural properties of graphene via the VO$$_2$$

2

phase transition.
A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks
Authors: Elazab Naira , Gab-Allah Wael A. , Elmogy Mohammed 
Published: February 2024

AbstractGliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.
Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context
Authors: Morand Jules , Yip Shoichi , Velegrakis Yannis , Lattanzi Gianluca , Potestio Raffaello , Tubiana Luca 
Published: February 2024

AbstractWe discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users’ data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.
Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles
Authors: Battraw Marcus A. , Fitzgerald Justin , James Michelle A. , Bagley Anita M. , Joiner Wilsaan M. , Schofield Jonathon S. 
Published: February 2024

AbstractIn recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8–20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant’s affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand movements produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children.
A physics-informed Bayesian optimization method for rapid development of electrical machines
Authors: Asef Pedram , Vagg Christopher 
Published: February 2024

AbstractAdvanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.
Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN
Authors: Wang Qian , Hu Shuo , Wang Xinya 
Published: February 2024

AbstractDue to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.
Empirical exploration of whale optimisation algorithm for heart disease prediction
Authors: Atimbire Stephen Akatore , Appati Justice Kwame , Owusu Ebenezer 
Published: February 2024

AbstractHeart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting the need for comprehensive evaluation metrics and utilization of different datasets in the same domain (heart disease). This research introduces a heart disease risk prediction approach by harnessing the whale optimization algorithm (WOA) for feature selection and implementing a comprehensive evaluation framework. The study leverages five distinct datasets, including the combined dataset comprising the Cleveland, Long Beach VA, Switzerland, and Hungarian heart disease datasets. The others are the Z-AlizadehSani, Framingham, South African, and Cleveland heart datasets. The WOA-guided feature selection identifies optimal features, subsequently integrated into ten classification models. Comprehensive model evaluation reveals significant improvements across critical performance metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using the same dataset, validating the effectiveness of our methodology. The comprehensive evaluation framework provides a robust assessment of the model’s adaptability, underscoring the WOA’s effectiveness in identifying optimal features in multiple datasets in the same domain.
Mesenchymal stem cells-derived extracellular vesicles for therapeutics of renal tuberculosis
Authors: Yudintceva Natalia , Bobkov Danila , Sulatsky Maksim , Mikhailova Natalia , Oganesyan Elena , Vinogradova Tatiana , Muraviov Alexandr , Remezova Anna , Bogdanova Evdokia , Garapach Irina , Maslak Olga , Esmedlyaeva Dilyara , Dyakova Marina , Yablonskiy Petr , Ziganshin Rustam , Kovalchuk Sergey , Blum Natalya , Sonawane Shirish H. , Sonawane Avinash , Behl Ankita , Shailja Singh  , Shevtsov Maxim 
Published: February 2024

AbstractExtrapulmonary tuberculosis with a renal involvement can be a manifestation of a disseminated infection that requires therapeutic intervention, particularly with a decrease in efficacy of conventional regimens. In the present study, we investigated the therapeutic potency of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) in the complex anti-tuberculosis treatment (ATT). A rabbit model of renal tuberculosis (rTB) was constructed by injecting of the standard strain Mycobacterium tuberculosis H37Rv into the cortical layer of the kidney parenchyma. Isolated rabbit MSC-EVs were intravenously administered once as an addition to standard ATT (isoniazid, pyrazinamide, and ethambutol). The therapeutic efficacy was assessed by analyzing changes of blood biochemical biomarkers and levels of anti- and pro-inflammatory cytokines as well as by renal computed tomography with subsequent histological and morphometric examination. The therapeutic effect of therapy with MSC-EVs was shown by ELISA method that confirmed a statistically significant increase of the anti-inflammatory and decrease of pro-inflammatory cytokines as compared to conventional treatment. In addition, there is a positive trend in increase of ALP level, animal weigh, and normalization of ADA activity that can indicate an improvement of kidney state. A significant reduction of the area of specific and interstitial inflammation indicated positive affect of MSC-EVs that suggests a shorter duration of ATT. The number of MSC-EVs proteins (as identified by mass-spectometry analysis) with anti-microbial, anti-inflammatory and immunoregulatory functions reduced the level of the inflammatory response and the severity of kidney damage (further proved by morphometric analysis). In conclusion, MSC-EVs can be a promising tool for the complex treatment of various infectious diseases, in particularly rTB.