bluuebunny/crossref_metadata_2025_split huggingface dataset:
The metabolic overdrive hypothesis: hyperglycolysis and glutaminolysis in bipolar mania
Authors: Campbell Iain H. , Campbell Harry 
Published: January 2024

AbstractEvidence from diverse areas of research including chronobiology, metabolomics and magnetic resonance spectroscopy indicate that energy dysregulation is a central feature of bipolar disorder pathophysiology. In this paper, we propose that mania represents a condition of heightened cerebral energy metabolism facilitated by hyperglycolysis and glutaminolysis. When oxidative glucose metabolism becomes impaired in the brain, neurons can utilize glutamate as an alternative substrate to generate energy through oxidative phosphorylation. Glycolysis in astrocytes fuels the formation of denovo glutamate, which can be used as a mitochondrial fuel source in neurons via transamination to alpha-ketoglutarate and subsequent reductive carboxylation to replenish tricarboxylic acid cycle intermediates. Upregulation of glycolysis and glutaminolysis in this manner causes the brain to enter a state of heightened metabolism and excitatory activity which we propose to underlie the subjective experience of mania. Under normal conditions, this mechanism serves an adaptive function to transiently upregulate brain metabolism in response to acute energy demand. However, when recruited in the long term to counteract impaired oxidative metabolism it may become a pathological process. In this article, we develop these ideas in detail, present supporting evidence and propose this as a novel avenue of investigation to understand the biological basis for mania.
Novel PUF60 variant suggesting an interaction between Verheij and Cornelia de Lange syndrome: phenotype description and review of the literature
Authors: Hoogenboom Amarens , Falix Farah A. , van der Laan Liselot , Kerkhof Jennifer , Alders Mariëlle , Sadikovic Bekim , van Haelst Mieke M. 
Published: January 2024

AbstractVerheij syndrome [VRJS; OMIM 615583] is a rare autosomal dominant neurodevelopmental disorder characterized by distinct clinical features, including growth retardation, intellectual disability, cardiac, and renal anomalies. VRJS is caused by deletions of chromosome 8q24.3 or pathogenic variants in the PUF60 gene. Recently, pathogenic PUF60 variants have been reported in some individuals with VRJS, contributing to the variability in the clinical presentation and severity of the condition. PUF60 encodes a protein involved in regulating gene expression and cellular growth. In this report, we describe a new case of VRJS with developmental delay, cardiac-, and renal abnormalities, caused by a heterozygous pathogenic PUF60 variant. Surprisingly, DNA methylation analysis revealed a pattern resembling the Cornelia de Lange syndrome (CdLS) episignature, suggesting a potential connection between PUF60 and CdLS-related genes. This case report further delineates the clinical and molecular spectrum of VRJS and supports further research to validate the interaction between VRJS and CdLS.
Epigenetic regulation of the nuclear genome associated with mitochondrial dysfunction in Leber’s hereditary optic neuropathy (LHON)
Authors: Nair Aswathy P. , Selvakumar Ambika , Gopalarethinam Janani , Abishek Kumar B. , Vellingiri Balachandar , Subramaniam Mohana Devi 
Published: January 2024

Abstract
Leber’s hereditary optic neuropathy (LHON) is a mitochondrial hereditary disease in which visual loss affects complex 1 activity of the electron transport chain of mitochondria. It first manifests as painless dulling or blurry in one or even both eyes, and as it develops, sharpness and color perception are lost. In addition to primary mitochondrial DNA (mtDNA) mutations, there are also other environmental and epigenetic factors involved in the pathogenesis of LHON. One of the most common locations for deadly pathogenic mutations in humans is the human complex I accessory NDUFS4 subunit gene. The iron-sulfur clusters of the electron input domain were distorted in the absence of NDUFS4, which reduced complex I function and elevated the production of reactive oxygen species. Therefore, here, we studied the epigenetic alterations of NDUFS4 by focusing on histone activation and repressive markers. We isolated peripheral blood mononuclear cells (PBMCs) from LHON patients and healthy individuals and examined epigenetic modifications in ND4 mutant cells and control cells. Chromatin immunoprecipitation-qRT PCR (ChIP-qRT PCR) assays were performed to investigate the modifications of histones. In comparison to their controls, both LHON patients and ND4 mutant cells exhibited a significant enrichment in activation and repressive markers. This finding indicates that these modifications might mitigate the impact of LHON mutations on complex 1 and aid in elucidating the mechanism underlying the progression of LHON disease.
Corrosion behavior of laser powder bed fusion additive manufacturing produced TiNi alloy by micro-arc oxidation
Authors: Zhao Xin , Liu Yicheng , Jia Chenfan , Chang Hao , Zhang Wei , Bai Yun , Li Shujun , Zhang Lai-Chang , Yuan Wei 
Published: January 2024

AbstractTo improve the corrosion resistance of TiNi alloy fabricated by laser powder bed fusion (LPBF), a porous oxidation layer was synthesized by micro-arc oxidation in a sodium aluminate and sodium silicate electrolyte. The influences of the applied voltage and the processing time on the morphology of oxidation layer were investigated, and the corrosion behavior of the oxidation layer in artificial saliva was evaluated and compared with that of the as-fabricated LPBF alloy. The results indicate that, as increasing the applied voltage and the processing time, the oxidation layer becomes uniform and integrated. The optimum parameters are with an applied voltage of 450 V and processing time of 40 min. The oxidation layer primarily contains α-Al2O3 and consists of two layers, i.e., a thin, compact and uniform inner layer and a porous outer layer. The formation of stable α-Al2O3 phase in the coating and its almost non-porous dense structure reduce the channels for corrosion ions to penetrate into the substrate through coating, thereby improving the corrosion resistance of TiNi alloy.
Buffer induced ionically crosslinked polyelectrolyte treatment for self-extinguishing polyester
Authors: Smith Dallin L. , Vest Natalie A. , Convento Miguel O. , Montemayor Maya D. , Grunlan Jaime C. 
Published: January 2024

AbstractOver 60 million tons of polyethylene terephthalate (PET) fibers are produced annually for clothing, upholstery, linens, and carpeting. Despite its widespread use, the versatility of PET is constrained by its flammability, which poses a particular fire hazard to homes with synthetic furnishings. To mitigate this fire risk, a polyelectrolyte complex (PEC) solution composed of polyallylamine hydrochloride and poly(sodium phosphate) is deposited onto the surface of 100% polyester fabric to render it self-extinguishing and eliminate melt dripping. A buffered solution of acetic acid, citric acid, or formic acid is used to initiate ionic complexation, rendering the PEC water resistant. Buffer identity affects deposition, but does not significantly influence the intumescent mechanism. This rapidly deposited aqueous coating primarily operates by facilitating production of an insulating char layer that limits the heat release and degradation of polyester into volatile byproducts.
A chromosome-level genome assembly of Sesamia inferens
Authors: Li Hongran , Peng Yan , Wu Chao , Vigan Chess-Kadouste , Mao Kaikai , Zhu Jingyun , Zou Luming , Jin Minghui , Zhang Lei , Xiao Yutao 
Published: January 2024

AbstractThe pink stem borer, Sesamia inferens (Walker), is a significant polyphagous pest historically restricted to regions south of N34° latitude. However, with changes in global climate and farming practices, the distribution of this moth has progressively exceeded its traditional limit of 34° N and encompassed most regions in North China. The genetic adaptations of S. inferens remain incompletely understood due to the lack of high-quality genome resources. Here, we sequenced the genome of S. inferens using PacBio and Hi-C technology, yielding a genome assembly of 865.04 Mb with contig N50 of 1.23 Mb. BUSCO analysis demonstrated this genome assembly has a high-level completeness of 96.1% gene coverage. In total, 459.72 Mb repeat sequences (53.14% of the assembled genome) and 20858 protein-coding genes were identified. We used the Hi-C technique to anchor 1135 contigs to 31 chromosomes, yielding a chromosome-level genome assembly with a scaffold N50 of 29.99 Mb. In conclusion, our high-quality genome assembly provided valuable resource that exploring the genetic characteristics of local adaptation and developing an efficient control strategy.
Global seasonal prediction of fire danger
Authors: Di Giuseppe Francesca , Vitolo Claudia , Barnard Christopher , Libertá Giorgio , Maciel Pedro , San-Miguel-Ayanz Jesus , Villaume Sebastien , Wetterhall Fredrik 
Published: January 2024

AbstractThe European Centre for Medium range weather forecast (ECMWF) on behalf of the Copernicus Emergency Management Service (CEMS) has recently widened the fire danger data offering in the Climate Data Store (CDS) to include a set of fire danger forecasts with lead times up to 7 months. The dataset incorporates fire danger indices for three different models developed in Canada, United States and Australia. The indices are calculated using ECMWF Seasonal Forecasting System 5 (SEAS5) and verified against the relevant reanalysis of fire danger based on the ECMWF Re-Analysis (ERA5). The data set is made openly available for the period 1981 to 2023 and will be updated regularly providing a resource to assess the predictability of fire weather at the seasonal time scale. The data set complements the availability of seasonal forecast provided by the Copernicus Emergency Management Service in real time.A preliminary analysis shows that globally anomalous conditions for fire weather can be predicted with confidence 1 month ahead. In some regions the prediction can extend to 2 months ahead. In most situations beyond this horizon, forecasts do not show more skill than climatology. However an extended predictability window, up to 6-7 months ahead is possible when anomalous fire weather is the results of large scale phenomena such as the El Niño Southern Oscillation and the Indian Ocean Dipole, often conducive of extensive fire burning in regions such as Indonesia and Australia.
AVDOS-VR: Affective Video Database with Physiological Signals and Continuous Ratings Collected Remotely in VR
Authors: Gnacek Michal , Quintero Luis , Mavridou Ifigeneia , Balaguer-Ballester Emili , Kostoulas Theodoros , Nduka Charles , Seiss Ellen 
Published: January 2024

AbstractInvestigating emotions relies on pre-validated stimuli to evaluate induced responses through subjective self-ratings and physiological changes. The creation of precise affect models necessitates extensive datasets. While datasets related to pictures, words, and sounds are abundant, those associated with videos are comparatively scarce. To overcome this challenge, we present the first virtual reality (VR) database with continuous self-ratings and physiological measures, including facial EMG. Videos were rated online using a head-mounted VR device (HMD) with attached emteqPRO mask and a cinema VR environment in remote home and laboratory settings with minimal setup requirements. This led to an affective video database with continuous valence and arousal self-rating measures and physiological responses (PPG, facial-EMG (7x), IMU). The AVDOS-VR database includes data from 37 participants who watched 30 randomly ordered videos (10 positive, neutral, and negative). Each 30-second video was assessed with two-minute relaxation between categories. Validation results suggest that remote data collection is ecologically valid, providing an effective strategy for future affective study designs. All data can be accessed via: www.gnacek.com/affective-video-database-online-study.
A processing-type active real-time traceable certification system
Authors: Wu Chia-Chun , Ling Chung-Huei , Hwang Min-Shiang 
Published: January 2024

AbstractIn addition to substituting low-price and low-quality materials for high-quality materials at the food processing stage, many dishonest businesses risk adulterating chemical materials in products to reduce production costs or increase product flavor with chemical synthesis spices. As a result, the risks to food safety are increased. Most safety management and certification regulations proceed with on-site examination or sampling inspection. As current certification systems lack complete tracking and real-time certification processes, they cannot comprehensively check foods’ processing and production processes and contents. Hence, food safety problems sway consumers’ trust and confidence in certification systems. This study intends to improve the agricultural processing end’s current food traceability certification system. Adding the design of raw material total quantity control provides a complete and sound real-time certification mechanism for citizens and businesses to assure consumer rights.
Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI—processed radiological parameter upon admission: a multicentre study
Authors: Tenda Eric Daniel , Henrina Joshua , Setiadharma Andry , Aristy Dahliana Jessica , Romadhon Pradana Zaky , Thahadian Harik Firman , Mahdi Bagus Aulia , Adhikara Imam Manggalya , Marfiani Erika , Suryantoro Satriyo Dwi , Yunus Reyhan Eddy , Yusuf Prasandhya Astagiri 
Published: January 2024

AbstractLimited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095–0.826), dyspnoea (OR: 1.684; 95%CI 1.049–2.705), loss of consciousness (OR: 4.593; 95%CI 1.702–12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900–0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967–0.996), neutrophil % (OR: 1.034; 95%CI 1.013–1.055), serum urea (OR: 1.018; 95%CI 1.010–1.026), affected lung area score (OR: 1.026; 95%CI 1.014–1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774–0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661–0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.
GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning
Authors: Zheng Shuhua , Rammohan Nikhil , Sita Timothy , Teo P. Troy , Wu Yilin , Lesniak Maciej , Sachdev Sean , Thomas Tarita O. 
Published: January 2024

AbstractIdentification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p < 0.001). Age > 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient’s age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients.
Aedes albopictus in a recently invaded area in Spain: effects of trap type, locality, and season on mosquito captures
Authors: Garrido Mario , Veiga Jesús , Garrigós Marta , Morales-Yuste Manuel , Recuero-Gil Jesús , Martínez-de la Puente Josué 
Published: January 2024

AbstractMosquitoes are primary vectors of pathogens impacting humans, wildlife, and livestock. Among them, the Asian tiger mosquito, Aedes albopictus, stands out as an invasive species with a global distribution, having established populations on every continent except Antarctica. Recent findings incriminate Ae. albopictus in the local transmission of several pathogens causing human diseases, including dengue, chikungunya, and Zika viruses and worm parasites as Dirofilaria. In Spain, the establishment of Ae. albopictus occurred in 2004 and it rapidly expanded, currently reaching southern provinces and creating novel epidemiological scenarios in recently invaded areas. In this study, we conducted captures of Ae. albopictus from May to November 2022 in two provinces, Granada and Malaga, situated near the current edge of the species' expanding range in Spain. The objective was to identify the primary factors influencing their captures in these regions. Mosquitoes were captured using BG-Sentinel traps baited with CO2 and BG-Lure, and miniature CDC-UV traps in five different localities. Our findings underscore the influence of both extrinsic factors, such as locality, and intrinsic factors, including mosquito sex, on the abundance of captured Ae. albopictus. A higher abundance of Ae. albopictus was observed in the Malaga province compared to localities in the Granada province. Furthermore, similar numbers of Ae. albopictus mosquitoes were captured in more urbanized areas of Granada, while the lowest counts were recorded in the less urbanized area. These results were compared to captures of another common species in the area, specifically Culex pipiens. Overall, these results represent the first monitoring of invasive Ae. albopictus in the area and are discussed in the light of the potential importance of the species as a nuisance for humans and vectors of pathogens of public health relevance.
Green synthesis of zinc oxide nanoparticles using aqueous extract of shilajit and their anticancer activity against HeLa cells
Authors: Perumal Parthasarathi , Sathakkathulla Nazeer Ahamed , Kumaran Kalaivani , Ravikumar Ramaladevi , Selvaraj Justin Jayaraj , Nagendran Vijayakumar , Gurusamy Mariappan , Shaik Naazneen , Gnanavadivel Prabhakaran Senthilkumar , Suruli Palanichamy Vinothkumar , Ganesan Vellaichamy , Thiraviam Purushoth Prabhu , Gunalan Seshan , Rathinasamy Suresh 
Published: January 2024

AbstractIn the present study, ZnO nanoparticles have been synthesized using an aqueous extract of shilajit. The nanoparticles were characterized using different techniques such as UV (ultraviolet–visible spectrophotometer), FTIR (Fourier transform infrared), XRD (X-ray diffraction), particle size analysis, SEM (scanning electron microscope) and EDAX (Energy-dispersive X-ray) analysis. The UV absorption peak at 422.40 nm was observed for ZnO nanoparticles. SEM analysis showed the shape of nanoparticles to be spherical, FTIR spectrum confirmed the presence of zinc atoms, particle size analysis showed the nanoparticle size, EDAX confirmed the purity of ZnO nanoparticles whereas XRD pattern similar to that of JCPDS card for ZnO confirmed the presence of pure ZnO nanoparticles. The in vitro anticancer activity of ZnO nanoparticles against the HeLa cell line showed the IC50 value of 38.60 μg/mL compared to reference standard cisplatin. This finding confirms that ZnO nanoparticles from shilajit extract have potent cytotoxic effect on human cervical cancer cell lines.
Real-time prediction of ROP based on GRU-Informer
Authors: Tu Bingrui , Bai Kai , Zhan Ce , Zhang Wanxing 
Published: January 2024

AbstractAccurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs. ROP prediction is influenced by multiple factors, making accurate prediction challenging. Current research primarily relies on historical data for training and modeling, lacking methods for real-time ROP prediction. This paper introduces a GRU-Informer model for real-time ROP prediction. The model employs GRU (Gated Recurrent Unit) neural networks at the lower level to capture short-term correlations in drilling parameters and uses the Informer model at the top to address long-term dependencies among drilling parameters. Thus, the GRU-Informer can capture both short-term and long-term time dependencies, providing better ROP predictions. This paper constructs a dataset using historical data from a southwestern Chinese oil field for experimentation. RMSE (Root Mean Square Error), MAE (mean absolute error) and $${R}^{2}$$

R

2

(Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural networks and Informer in real-time ROP prediction, indicating its practical value.