bluuebunny/crossref_metadata_2025 huggingface dataset:
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

Deep 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

Admixture 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

High-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

Gliomas 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

We 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

In 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

Advanced 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

Due 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

Heart 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

Extrapulmonary 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.
Serum microRNA profile of rhesus macaques following ionizing radiation exposure and treatment with a medical countermeasure, Ex-Rad
Authors: Russ Eric , Fatanmi Oluseyi O. , Wise Stephen Y. , Carpenter Alana D. , Maniar Manoj , Iordanskiy Sergey , Singh Vijay K.
Published: February 2024

Exposure to ionizing radiation (IR) presents a formidable clinical challenge. Total-body or significant partial-body exposure at a high dose and dose rate leads to acute radiation syndrome (ARS), the complex pathologic effects that arise following IR exposure over a short period of time. Early and accurate diagnosis of ARS is critical for assessing the exposure dose and determining the proper treatment. Serum microRNAs (miRNAs) may effectively predict the impact of irradiation and assess cell viability/senescence changes and inflammation. We used a nonhuman primate (NHP) model—rhesus macaques (Macaca mulatta)—to identify the serum miRNA landscape 96 h prior to and following 7.2 Gy total-body irradiation (TBI) at four timepoints: 24, 36, 48, and 96 h. To assess whether the miRNA profile reflects the therapeutic effect of a small molecule ON01210, commonly known as Ex-Rad, that has demonstrated radioprotective efficacy in a rodent model, we administered Ex-Rad at two different schedules of NHPs; either 36 and 48 h post-irradiation or 48 and 60 h post-irradiation. Results of this study corroborated our previous findings obtained using a qPCR array for several miRNAs and their modulation in response to irradiation: some miRNAs demonstrated a temporary increased serum concentration within the first 24–36 h (miR-375, miR-185-5p), whereas others displayed either a prolonged decline (miR-423-5p) or a long-term increase (miR-30a-5p, miR-27b-3p). In agreement with these time-dependent changes, hierarchical clustering of differentially expressed miRNAs showed that the profiles of the top six miRNA that most strongly correlated with radiation exposure were inconsistent between the 24 and 96 h timepoints following exposure, suggesting that different biodosimetry miRNA markers might be required depending on the time that has elapsed. Finally, Ex-Rad treatment restored the level of several miRNAs whose expression was significantly changed after radiation exposure, including miR-16-2, an miRNA previously associated with radiation survival. Taken together, our findings support the use of miRNA expression as an indicator of radiation exposure and the use of Ex-Rad as a potential radioprotectant.
The effect of arterial spin labeling MR angiography (ASL-MRA) in visualizing the branches of external carotid artery
Authors: Yogi Akira , Ito Junji , Ishikawa Kazuki , Heianna Joichi , Sakugawa Satoshi , Aguni Narihisa , Obara Makoto , Maeda Hiroyuki , Nishie Akihiro
Published: February 2024

This study aimed to assess the performance of arterial-spin labeling MRA (ASL-MRA) for visualizing the external carotid artery (ECA) branches in comparison with time-of-flight MRA (TOF-MRA) and CT angiography (CTA). We retrospectively selected 31 consecutive patients, who underwent both MRAs and CTA, prior to the intra-arterial chemoradiotherapy (IACRT) for head and neck cancer. Four patients underwent IACRT bilaterally, so we analyzed 35 ECAs. Pseudo-continuous, three-dimensional ASL using a turbo field echo sequence was acquired. For the TOF-MRA and CTA, clinically used parameters were applied. Two observers evaluated each ECA branch with reference to the angiogram at the IACRT, using five-point scale, in consensus. Friedman test for multiple comparisons was applied. ASL-MRA and CTA better visualized the superior thyroid, lingual, facial, submental, transverse facial, and internal maxillary arteries (IMAs) better than TOF-MRA (p < 0.05). In addition, CTA was superior to ASL-MRA in visualizing only submental artery among these arteries (p = 0.0005). Alternatively, the ASL-MRA was superior for visualizing the middle meningeal artery (MMA) and IMA, compared to the CTA (p = 0.0001 and 0.0007, respectively). ASL-MRA was superior to the TOF-MRA and similar to the CTA in visualizing most of ECA branches. Furthermore, ASL-MRA can better visualize the periphery of MMA and IMA than CTA.
The dynamics of fishing villages along the South Atlantic Coast of North America (ca. 5000–3000 years BP)
Authors: Thompson Victor D. , Smith Karen Y. , Sanger Matthew , Garland Carey J. , Pluckhahn Thomas J. , Napora Katharine , Bedell Jennifer Dodd , Hadden Carla , Cherkinsky Alex , Cajigas Rachel , Blair Elliot H. , Semon Anna M. , Thomas David Hurst
Published: February 2024

We present new chronologies that inform the timing and tempo of shell ring and shell mound construction on the South Atlantic Bight. Our project combines recently acquired dates with legacy radiocarbon dates from over 25 rings and mounds to provide a higher-resolution chronology regarding the occupation and formation of this larger landscape of the earliest fishing villages along the East Coast of the United States. We resolve the ordering and timing of occupation of these rings and mounds through Bayesian statistical modeling. These new models historicize and contextualize these shell rings in ways previously impossible. Specifically, our new chronologies of these villages indicate that the earliest villages were established prior to the invention of pottery. The early period of village establishment evidences isolated village rings, whereas later periods seem to have more villages, but these appear to have been relocated to other areas and/or islands over time. Shell mounds are fewer in number, are spread throughout the time period, and may represent special purpose sites compared to shell-rings. Once villages spread, they quickly adopted new technologies (i.e., pottery) and created new institutions and practiced village relocation, which allowed this way of life to persist for more than a thousand years.
Designing and psychometric assessment of the moral intelligence scale for healthcare professionals
Authors: Mohammadi Fateme , Borzou Seyed Reza , Khazaei Salman , Bijani Mostafa , Masoumi Seyedeh Zahra , Hosseini Seyed Kianoosh
Published: February 2024

The moral intelligence of healthcare professionals in the cardiac operating room is one of the most important aspects of professional competence. However, moral intelligence is an abstract and multidimensional concept that needs to be clarified and described based on organizational culture and environment. Therefore, there is a need to design a specific scale for measuring the moral intelligence of healthcare professionals in the cardiac operating room. This study aims to design and assess the psychometric properties of a moral intelligence scale for healthcare professionals in the cardiac operating room. The present study was a mixed method study with a sequential exploratory approach. The research was conducted in 2023–2024 in Iran. The first phase data were collected from 20 healthcare professionals and were analyzed by conventional content analysis method. In the second phase, the validity and reliability of the instrument were evaluated by involving 300 healthcare professionals in the cardiac operating room. The moral intelligence of health care professionals in the cardiac operating room was defined as moral sensitivity combined with moral commitment and moral courage for the provision of quality care that respects the principles of medical ethics. After deducing the conceptual framework, the moral intelligence scale for healthcare professionals in the cardiac operating room was developed with three dimensions: “moral sensitivity,” “moral commitment,” and “moral courage.” 11 items were removed during testing to ensure content validity. Face validity was confirmed with impact scores > 1.5 for all items. A scale was developed through factor analysis with three factors that accounted for 73.04% of the observed variance. The instrument’s reliability using Cronbach’s alpha coefficient calculation was reported as 0.94 for the entire instrument. The testretest showed no statistically significant difference between the pre and post-test scores of moral intelligence (p = 0.51). The moral intelligence scale demonstrated acceptable psychometric properties. The moral intelligence scale for health care professionals in the cardiac operating room demonstrated acceptable psychometric properties. This instrument may serve to assess the moral intelligence of healthcare professionals and determine the need for educational interventions to reduce the ethical challenges and improve the moral intelligence of this healthcare.
Evaluative contexts facilitate implicit mentalizing: relation to the broader autism phenotype and mental health
Authors: Wu Ruihan , Leow Karen , Yu Nicole , Rafter Ciara , Rosenbaum Katia , F. de C. Hamilton Antonia , White Sarah J.
Published: February 2024

One promising account for autism is implicit mentalizing difficulties. However, this account and even the existence of implicit mentalizing have been challenged because the replication results are mixed. Those unsuccessful replications may be due to the task contexts not being sufficiently evaluative. Therefore, the current study developed a more evaluative paradigm by implementing a prompt question. This was assessed in 60 non-autistic adults and compared with a non-prompt version. Additionally, parents of autistic children are thought to show a genetic liability to autistic traits and cognition and often report mental health problems, but the broader autism phenotype (BAP) is an under-researched area. Thus, we also aimed to compare 33 BAP and 26 non-BAP mothers on mentalizing abilities, autistic traits, compensation and mental health. Our results revealed that more evaluative contexts can facilitate implicit mentalizing in BAP and non-BAP populations, and thus improve task reliability and replicability. Surprisingly, BAP mothers showed better implicit mentalizing but worse mental health than non-BAP mothers, which indicates the heterogeneity in the broader autism phenotype and the need to promote BAP mothers’ psychological resilience. The findings underscore the importance of contexts for implicit mentalizing and the need to profile mentalizing and mental health in BAP parents.
A hybrid machine learning-based model for predicting flight delay through aviation big data
Authors: Dai Min
Published: February 2024

The prediction of flight delays is one of the important and challenging issues in the field of scheduling and planning flights by airports and airlines. Therefore, in recent years, we have witnessed various methods to solve this problem using machine learning techniques. In this article, a new method is proposed to address these issues. In the proposed method, a group of potential indicators related to flight delay is introduced, and a combination of ANOVA and the Forward Sequential Feature Selection (FSFS) algorithm is used to determine the most influential indicators on flight delays. To overcome the challenges related to large flight data volumes, a clustering strategy based on the DBSCAN algorithm is employed. In this approach, samples are clustered into similar groups, and a separate learning model is used to predict flight delays for each group. This strategy allows the problem to be decomposed into smaller sub-problems, leading to improved prediction system performance in terms of accuracy (by 2.49%) and processing speed (by 39.17%). The learning model used in each cluster is a novel structure based on a random forest, where each tree component is optimized and weighted using the Coyote Optimization Algorithm (COA). Optimizing the structure of each tree component and assigning weighted values to them results in a minimum 5.3% increase in accuracy compared to the conventional random forest model. The performance of the proposed method in predicting flight delays is tested and compared with previous research. The findings demonstrate that the proposed approach achieves an average accuracy of 97.2% which indicates a 4.7% improvement compared to previous efforts.
Mathematical modeling of cholera dynamics with intrinsic growth considering constant interventions
Authors: Brhane Kewani Welay , Ahmad Abdulaziz Garba , Hina Hina , Emadifar Homan
Published: February 2024

A mathematical model that describes the dynamics of bacterium vibrio cholera within a fixed population considering intrinsic bacteria growth, therapeutic treatment, sanitation and vaccination rates is developed. The developed mathematical model is validated against real cholera data. A sensitivity analysis of some of the model parameters is also conducted. The intervention rates are found to be very important parameters in reducing the values of the basic reproduction number. The existence and stability of equilibrium solutions to the mathematical model are also carried out using analytical methods. The effect of some model parameters on the stability of equilibrium solutions, number of infected individuals, number of susceptible individuals and bacteria density is rigorously analyzed. One very important finding of this research work is that keeping the vaccination rate fixed and varying the treatment and sanitation rates provide a rapid decline of infection. The fourth order Runge–Kutta numerical scheme is implemented in MATLAB to generate the numerical solutions.
Dietary polyamines promote intestinal adaptation in an experimental model of short bowel syndrome
Authors: Kasahara Naoya , Teratani Takumi , Yokota Shinichiro , Sakuma Yasunaru , Sasanuma Hideki , Fujimoto Yasuhiro , Ijichi Tetsuo , Urahashi Taizen , Yoshitomi Hideyuki , Kitayama Joji , Sata Naohiro
Published: February 2024

Intestinal adaptation does not necessarily recover absorptive capacity in short bowel syndrome (SBS), sometimes resulting in intestinal failure-associated liver disease (IFALD). Additionally, its therapeutic options remain limited. Polyamines (spermidine and spermine) are known as one of the autophagy inducers and play important roles in promoting the weaning process; however, their impact on intestinal adaptation is unknown. The aim of this study was to investigate the impact of polyamines ingestion on adaptation and hepatic lipid metabolism in SBS. We performed resection of two-thirds of the small intestine in male Lewis rats as an SBS model. They were allocated into three groups and fed different polyamine content diets (0%, 0.01%, 0.1%) for 30 days. Polyamines were confirmed to distribute to remnant intestine, whole blood, and liver. Villous height and number of Ki-67-positive cells in the crypt area increased with the high polyamine diet. Polyamines increased secretory IgA and mucin content in feces, and enhanced tissue Claudin-3 expression. In contrast, polyamines augmented albumin synthesis, mitochondrial DNA copy number, and ATP storage in the liver. Moreover, polyamines promoted autophagy flux and activated AMP-activated protein kinase with suppression of lipogenic gene expression. Polyamines ingestion may provide a new therapeutic option for SBS with IFALD.
Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome
Authors: Smith Andrew H. , Gray Geoffrey M. , Ashfaq Awais , Asante-Korang Alfred , Rehman Mohamed A. , Ahumada Luis M.
Published: February 2024

Hypoplastic left heart syndrome (HLHS) is a congenital malformation commonly treated with palliative surgery and is associated with significant morbidity and mortality. Risk stratification models have often relied upon traditional survival analyses or outcomes data failing to extend beyond infancy. Individualized prediction of transplant-free survival (TFS) employing machine learning (ML) based analyses of outcomes beyond infancy may provide further valuable insight for families and healthcare providers along the course of a staged palliation. Data from both the Pediatric Heart Network (PHN) Single Ventricle Reconstruction (SVR) trial and Extension study (SVR II), which extended cohort follow up for five years was used to develop ML-driven models predicting TFS. Models incrementally incorporated features corresponding to successive phases of care, from pre-Stage 1 palliation (S1P) through the stage 2 palliation (S2P) hospitalization. Models trained with features from Pre-S1P, S1P operation, and S1P hospitalization all demonstrated time-dependent area under the curves (td-AUC) beyond 0.70 through 5 years following S1P, with a model incorporating features through S1P hospitalization demonstrating particularly robust performance (td-AUC 0.838 (95% CI 0.836–0.840)). Machine learning may offer a clinically useful alternative means of providing individualized survival probability predictions, years following the staged surgical palliation of hypoplastic left heart syndrome.
Mixed methods research on satisfaction with athletes’ compensation
Authors: Li Qianqian , Li Shuo , Zhao Huan , Jiao Lu , Han Xiao
Published: February 2024

This study explores the factors influencing athletes’ compensation satisfaction and their configuration effects. A mixed research approach that combines regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) was applied to process the survey data of 352 athletes from six provinces. The regression analysis results demonstrate that economic compensation, the compensation system, the external environment, and compensation fairness all have a significant positive effect on athletes’ compensation satisfaction. Accordingly, the fsQCA approach was applied to explore the combined effects of the factors influencing athletes’ compensation satisfaction, which verifies the conclusions drawn from regression analysis and provides improvement paths for increasing athletes’ compensation satisfaction.
Exploring the neural underpinnings of chord prediction uncertainty: an electroencephalography (EEG) study
Authors: Ono Kentaro , Mizuochi Ryohei , Yamamoto Kazuki , Sasaoka Takafumi , Ymawaki Shigeto
Published: February 2024

Predictive processing in the brain, involving interaction between interoceptive (bodily signal) and exteroceptive (sensory) processing, is essential for understanding music as it encompasses musical temporality dynamics and affective responses. This study explores the relationship between neural correlates and subjective certainty of chord prediction, focusing on the alignment between predicted and actual chord progressions in both musically appropriate chord sequences and random chord sequences. Participants were asked to predict the final chord in sequences while their brain activity was measured using electroencephalography (EEG). We found that the stimulus preceding negativity (SPN), an EEG component associated with predictive processing of sensory stimuli, was larger for non-harmonic chord sequences than for harmonic chord progressions. Additionally, the heartbeat evoked potential (HEP), an EEG component related to interoceptive processing, was larger for random chord sequences and correlated with prediction certainty ratings. HEP also correlated with the N5 component, found while listening to the final chord. Our findings suggest that HEP more directly reflects the subjective prediction certainty than SPN. These findings offer new insights into the neural mechanisms underlying music perception and prediction, emphasizing the importance of considering auditory prediction certainty when examining the neural basis of music cognition.
Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment
Authors: Qi Yun , Xue Kailong , Wang Wei , Cui Xinchao , Liang Ran , Wu Zewei
Published: February 2024

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.
Intestinal metabolomics in premature infants with late-onset sepsis
Authors: Liu Jingfei , Zhang Li , Li Dong , Yu Xiaotong , Gao Ying , Zhou Ying
Published: February 2024

We aimed to investigate the characteristics of intestinal metabolomics and non-invasive biomarkers for early diagnosis of late-onset sepsis (LOS) by analyzing gut metabolites in preterm infants with LOS. We collected stool samples from septic and healthy preterm infants for analysis by liquid chromatography–mass spectrometry. 123 different metabolites were identified and 13 pathways were mainly involved. Glycine, serine, and threonine metabolism; glyoxylate and dicarboxylic acid metabolism; glutathione metabolism; primary bile acid biosynthesis; steroid synthesis; pentose and glucuronic acid interconversion may be involved in the pathogenesis of LOS in preterm infants. The significant changes of N-Methyldopamine, cellulose, glycine, gamma-Glutamyltryptophan, N-Ribosylnicotinamide and 1alpha, 25-dihydroxycholecalciferol showed specific diagnostic values and as non-invasive biomarkers for LOS.
Dynamic contrast-enhanced magnetic resonance imaging assessment of residual tumor angiogenesis after insufficient microwave ablation and donafenib adjuvant therapy
Authors: Ren Ziwang , Feng Guiling , Li Bing , Zhang Chuan , Du Yong
Published: February 2024

To analyze the correlation between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) permeability parameters and serum vascular endothelial growth factor (VEGF) levels in a rabbit VX2 liver cancer model with insufficient microwave ablation (MWA), to observe the dynamic changes in residual tumor angiogenesis in the short term after MWA, and to assess the effectiveness of donafenib as adjuvant therapy. Forty rabbits with VX2 liver tumors were randomly divided into three groups: an insufficient MWA group (n = 15), a combined treatment group (n = 15) and a control group (n = 10). The dynamic changes in VEGF expression after MWA and the effectiveness of donafenib as adjuvant therapy were evaluated by DCE-MRI and serum VEGF levels before surgery and 1, 3, 7, and 14 days after surgery. The correlation between the volume translate constant (Ktrans) of DCE-MRI parameters and serum VEGF levels fluctuated after ablation, but the coefficient was always positive (all p < 0.001). Repeated-measures ANOVA revealed significant changes in the serum VEGF concentration (F = 40.905, p < 0.001; partial η2 = 0.689), Ktrans (F = 13.388, p < 0.001; partial η2 = 0.420), and tumor diameter in each group (F = 34.065, p < 0.001; partial η2 = 0.648) at all five time points. Pairwise comparisons showed that the serum VEGF level, Ktrans value and tumor diameter in the insufficient MWA group and combined treatment group were significantly lower at 1 d than in the control group, but these values gradually increased over time (all p < 0.05). Ktrans and tumor diameter were significantly greater in the insufficient MWA group than in the control group at 14 days (all p < 0.05). The serum VEGF concentration, Ktrans, and tumor diameter were significantly lower in the combined treatment group than in the other two groups at 3, 7, and 14 days (all p < 0.05). Ktrans is positively correlated with the serum VEGF concentration. Ktrans and the serum VEGF concentration changed significantly after treatment with insufficient ablation or in combination with donafenib, and Ktrans may change faster. Insufficient MWA promotes the progression of residual tumors. Adjuvant treatment with donafenib is effective.
Enhancing high-performance concrete sustainability: integration of waste tire rubber for innovation
Authors: Singaravel Dhipan Aravind , Veerapandian Pavalan , Rajendran Silambarasan , Dhairiyasamy Ratchagaraja
Published: February 2024

This study extensively explored the impact of integrating waste tire rubber into high-performance concrete (HPC) by substituting natural sand. Different fractions of rubber particles—5%, 10%, and 15% replacements of the fine aggregate—were rigorously investigated. Properties from fresh to hardened concrete were assessed, including compressive and tensile strength, modulus of elasticity, workability, and damping coefficient. Replacing up to 10% of sand with 0.6 mm rubber particles showed minimal strength compromise compared to standard HPC. However, at a 15% replacement rate, a noticeable decline in strength became evident, highlighting an optimal threshold for inclusion. Additionally, rubber incorporation notably enhanced concrete ductility and damping, marking a substantial improvement in dynamic properties. Efforts to offset strength reduction through increased fines content and mineral admixture could not counteract the decline at the 15% replacement level, suggesting limitations in compensatory measures. Methodological refinements enhanced data accuracy, including capping and surface treatments during compression testing. The study underlined the viability of controlled rubber substitution for bolstering HPC's dynamic attributes. Despite strength reductions at higher replacement rates, controlled waste tire rubber integration proves promising for enhancing HPC's dynamics without compromising structural integrity, advocating its suitability across diverse construction applications.
Global epistasis in plasmid-mediated antimicrobial resistance
Authors: DelaFuente Javier , Diaz-Colunga Juan , Sanchez Alvaro , San Millan Alvaro
Published: February 2024

Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology.