bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of CAF-related genes in immunotherapy and drug sensitivity in hepatocellular carcinoma: a multi-database analysis
Authors: Yao Yi , Yang KaiQing , Wang Qiang , Zhu Zeming , Li Sheng , Li Bin , Feng Bin , Tang Caixi 
Published: January 2024

AbstractThis study aims to identify the cancer-associated fibroblasts (CAF)-related genes that can affect immunotherapy and drug sensitivity in hepatocellular carcinoma (HCC). Expression data and survival data associated with HCC were obtained in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted correlation network analysis (WGCNA) analysis was performed to obtain CAF-related genes. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for regression analysis and risk models. Subsequently, Gene Set Enrichment Analysis (GSEA) analysis, Gene Set Enrichment Analysis (ssGSEA) analysis, Tumor Immune Dysfunction and Exclusion (TIDE) analysis and drug sensitivity analysis were performed on the risk models. Survival analysis of CAF scores showed that the survival rate was lower in samples with high CAF scores than those with low scores. However, this difference was not significant, suggesting CAF may not directly influence the prognosis of HCC patients. Further screening of CAF-related genes yielded 33 CAF-related genes. Seven risk models constructed based on CDR2L, SPRED1, PFKP, ENG, KLF2, FSCN1 and VCAN, showed significant differences in immunotherapy and partial drug sensitivity in HCC. Seven CAF-related genes may have important roles in immunotherapy, drug sensitivity and prognostic survival in HCC patients.
Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly
Authors: Evans Constantine Glen , O’Brien Jackson , Winfree Erik , Murugan Arvind 
Published: January 2024

AbstractInspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles1–3. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks4–7. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems.
A small and vigorous black hole in the early Universe
Authors: Maiolino Roberto , Scholtz Jan , Witstok Joris , Carniani Stefano , D’Eugenio Francesco , de Graaff Anna , Übler Hannah , Tacchella Sandro , Curtis-Lake Emma , Arribas Santiago , Bunker Andrew , Charlot Stéphane , Chevallard Jacopo , Curti Mirko , Looser Tobias J. , Maseda Michael V. , Rawle Timothy D. , Rodríguez del Pino Bruno , Willott Chris J. , Egami Eiichi , Eisenstein Daniel J. , Hainline Kevin N. , Robertson Brant , Williams Christina C. , Willmer Christopher N. A. , Baker William M. , Boyett Kristan , DeCoursey Christa , Fabian Andrew C. , Helton Jakob M. , Ji Zhiyuan , Jones Gareth C. , Kumari Nimisha , Laporte Nicolas , Nelson Erica J. , Perna Michele , Sandles Lester , Shivaei Irene , Sun Fengwu 
Published: January 2024

AbstractSeveral theories have been proposed to describe the formation of black hole seeds in the early Universe and to explain the emergence of very massive black holes observed in the first thousand million years after the Big Bang1–3. Models consider different seeding and accretion scenarios4–7, which require the detection and characterization of black holes in the first few hundred million years after the Big Bang to be validated. Here we present an extensive analysis of the JWST-NIRSpec spectrum of GN-z11, an exceptionally luminous galaxy at z = 10.6, revealing the detection of the [Neiv]λ2423 and CII*λ1335 transitions (typical of active galactic nuclei), as well as semi-forbidden nebular lines tracing gas densities higher than 109 cm−3, typical of the broad line region of active galactic nuclei. These spectral features indicate that GN-z11 hosts an accreting black hole. The spectrum also reveals a deep and blueshifted CIVλ1549 absorption trough, tracing an outflow with velocity 800−1,000 km s−1, probably driven by the active galactic nucleus. Assuming local virial relations, we derive a black hole mass of $$\log ({M}_{{\rm{BH}}}/{M}_{\odot })=6.2\pm 0.3$$

log

(

M

BH

/

M

⊙

)

=
6.2
±
0.3

, accreting at about five times the Eddington rate. These properties are consistent with both heavy seeds scenarios and scenarios considering intermediate and light seeds experiencing episodic super-Eddington phases. Our finding explains the high luminosity of GN-z11 and can also provide an explanation for its exceptionally high nitrogen abundance.
Artificial intelligence predicts normal summer monsoon rainfall for India in 2023
Authors: Narang Udit , Juneja Kushal , Upadhyaya Pankaj , Salunke Popat , Chakraborty Tanmoy , Behera Swadhin Kumar , Mishra Saroj Kanta , Suresh Akhil Dev 
Published: January 2024

AbstractInaccuracy in the All Indian Summer Monsoon Rainfall (AISMR) forecast has major repercussions for India's economy and people's daily lives. Improving the accuracy of AISMR forecasts remains a challenge. An attempt is made here to address this problem by taking advantage of recent advances in machine learning techniques. The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.
Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems
Authors: Biazar Seyed Mostafa , Shehadeh Hisham A. , Ghorbani Mohammad Ali , Golmohammadi Golmar , Saha Amartya 
Published: January 2024

AbstractSoil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models—artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.
On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods
Authors: Moura Felipe Arruda , Pelegrinelli Alexandre R. M. , Catelli Danilo S. , Kowalski Erik , Lamontagne Mario , da Silva Torres Ricardo 
Published: January 2024

AbstractKnee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3–5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.
Peripheral oxytocin levels are linked to hypothalamic gray matter volume in autistic adults: a cross-sectional secondary data analysis
Authors: Haaf Raoul , Brandi Marie-Luise , Albantakis Laura , Lahnakoski Juha M. , Henco Lara , Schilbach Leonhard 
Published: January 2024

AbstractOxytocin (OXT) is known to modulate social behavior and cognition and has been discussed as pathophysiological and therapeutic factor for autism spectrum disorder (ASD). An accumulating body of evidence indicates the hypothalamus to be of particular importance with regard to the underlying neurobiology. Here we used a region of interest voxel-based morphometry (VBM) approach to investigate hypothalamic gray matter volume (GMV) in autistic (n = 29, age 36.03 ± 11.0) and non-autistic adults (n = 27, age 30.96 ± 11.2). Peripheral plasma OXT levels and the autism spectrum quotient (AQ) were used for correlation analyses. Results showed no differences in hypothalamic GMV in autistic compared to non-autistic adults but suggested a differential association between hypothalamic GMV and OXT levels, such that a positive association was found for the ASD group. In addition, hypothalamic GMV showed a positive association with autistic traits in the ASD group. Bearing in mind the limitations such as a relatively small sample size, a wide age range and a high rate of psychopharmacological treatment in the ASD sample, these results provide new preliminary evidence for a potentially important role of the HTH in ASD and its relationship to the OXT system, but also point towards the importance of interindividual differences.
Microbic flow analysis of nano fluid with chemical reaction in microchannel with flexural walls under the effects of thermophoretic diffusion
Authors: Akbar Noreen Sher , Rafiq Maimona , Muhammad Taseer , Alghamdi Metib 
Published: January 2024

AbstractThe current investigation examines the peristaltic flow, in curved conduit, having complaint boundaries for nanofluid. The effects of curvature are taken into account when developing the governing equations for the nano fluid model for curved channels. Nonlinear & coupled differential equations are then simplified by incorporating the long wavelength assumption along with smaller Reynolds number. The homotopy perturbation approach is used to analytically solve the reduced coupled differential equations. The entropy generation can be estimated through examining the contributions of heat and fluid viscosities. The results of velocity, temperature, concentration, entropy number, and stream functions have been plotted graphically in order to discuss the physical attributes of the essential quantities. Increase in fluid velocity within the curved conduit is noticed for higher values of thermophoresis parameter and Brownian motion parameter further entropy generation number is boosted by increasing values of Grashof number.
Finite element analysis of cross fluid model over a vertical disk suspended to a tetra hybrid nanoparticles mixture
Authors: Sohail Muhammad , Nazir Umar , Singh Abha , Tulu Ayele , Khan Muhammad Jahangir 
Published: January 2024

AbstractNanoparticles have numerous applications and are used frequently in different cooling, heating, treatment of cancer cells and manufacturing processes. The current investigation covers the utilization of tetra hybrid nanofluid (aluminum oxide, iron dioxide, titanium dioxide and copper) for Crossflow model over a vertical disk by considering the shape effects (bricks, cylindrical and platelet) of nanoparticles, electro-magneto-hydrodynamic effect and quadratic thermal radiation. In the current inspection model is first derived given PD-equations and then altered into a system of OD-equations by including similarity variables. The converted ordinary differential equations are solved by using the finite element procedure and the impact of the solution against numerous involved parameters is displayed through tables and graphs. It is observed that tetra-hybrid nanoparticles are recommended better in industrial applications where the highest production of thermal energy. Moreover, an enhancement of thermal production can be achieved utilizing different values of the magnetic parameter, time relaxation number, variable thermal radiation number and magnetic induction number but the opposite trend has been noticed with the effects of radiation number.
Functional and anatomical outcomes of brolucizumab for nAMD in a real-life setting
Authors: Pastore Marco Rocco , Milan Serena , Cirigliano Gabriella , Tognetto Daniele 
Published: January 2024

AbstractTo report long-term outcomes of brolucizumab in neovascular age-related macular degeneration (nAMD) treatment. Records from 74 patients were retrospectively reviewed. Both naïve eyes and those previously treated with other antiVEGF agents were included. Primary outcomes included variation in best corrected visual acuity (BCVA), central subfield thickness (CST), intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) dimensions. Outcomes were reviewed after the loading phase, at week 24, and at last follow-up. IOI occurrence represented the secondary outcome. BCVA improved significantly in both groups. In switched eyes, IRF and SRF were significantly reduced at every timepoint, with CST reduction from week 24 (p = 0.005). In naïve group, CST decreased from the loading phase (p = 0.006) and all patients showed dry macula from week 24. A significant reduction in PED maximum high was demonstrated in both groups. In seven naïve eyes, PED completely reabsorbed; a slight increase in PED horizontal maximal diameter was also observed from week 24. IOI occurred in 5.4% of cases. In conclusion, brolucizumab showed a strong drying effect, permitting functional improvement together with fluid reabsorption and an encouraging modification of PED dimension, especially on naïve patients. These results together with the extension of treatment intervals make brolucizumab an efficient therapeutic strategy for nAMD.
Regulation of mitochondrial metabolism by autophagy supports leptin-induced cell migration
Authors: García-Miranda Alin , Montes-Alvarado José Benito , Sarmiento-Salinas Fabiola Lilí , Vallejo-Ruiz Verónica , Castañeda-Saucedo Eduardo , Navarro-Tito Napoleón , Maycotte Paola 
Published: January 2024

AbstractLeptin is an adipokine secreted by adipose tissue, which promotes tumor progression by activating canonical signaling pathways such as MAPK/ERK. Recent studies have shown that leptin induces autophagy, and this process is involved in leptin-induced characteristics of malignancy. Autophagy is an intracellular degradation process associated with different hallmarks of cancer, such as cell survival, migration, and metabolic reprogramming. However, its relationship with metabolic reprogramming has not been clearly described. The purpose of this study was to determine the role of leptin-induced autophagy in cancer cell metabolism and its association with cellular proliferation and migration in breast cancer cells. We used ER+/PR+ and triple-negative breast cancer cell lines treated with leptin, autophagy inhibition, or mitochondrial metabolism inhibitors. Our results show that leptin induces autophagy, increases proliferation, mitochondrial ATP production and mitochondrial function in ER+/PR+ cells. Importantly, autophagy was required to maintain metabolic changes and cell proliferation driven by leptin. In triple-negative cells, leptin did not induce autophagy or cell proliferation but increased glycolytic and mitochondrial ATP production, mitochondrial function, and cell migration. In triple negative cells, autophagy was required to support metabolic changes and cell migration, and autophagy inhibition decreased cellular migration similar to mitochondrial inhibitors. In conclusion, leptin-induced autophagy supports mitochondrial metabolism in breast cancer cells as well as glycolysis in triple negative cells. Importantly, leptin-induced mitochondrial metabolism promoted cancer cell migration.
Life-history of Palaeoloxodon antiquus reveals Middle Pleistocene glacial refugium in the Megalopolis basin, Greece
Authors: Roditi Effrosyni , Bocherens Hervé , Konidaris George E. , Athanassiou Athanassios , Tourloukis Vangelis , Karkanas Panagiotis , Panagopoulou Eleni , Harvati Katerina 
Published: January 2024

AbstractThe Balkans are considered a major glacial refugium where flora and fauna survived glacial periods and repopulated the rest of Europe during interglacials. While it is also thought to have harboured Pleistocene human populations, evidence linking human activity, paleoenvironmental indicators and a secure temporal placement to glacial periods is scant. Here, we present the first intra-tooth multi-isotope analysis for the European straight-tusked elephant Palaeoloxodon antiquus, on an adult male individual excavated in association with lithic artefacts at the MIS 12 site Marathousa 1 (Megalopolis basin, Greece). The studied find also exhibits anthropogenic modifications, providing direct evidence of hominin presence. We employed strontium, carbon and oxygen isotope analysis on enamel bioapatite to investigate its foraging and mobility behaviour, using a sequential sampling strategy along the tooth growth axis of the third upper molar, to assess ecological changes during the last decade of life. We found a geographically restricted range, in a C3-dominated open woodland environment, and relatively stable conditions over the examined timeframe. Our results show that, despite the severity of the MIS 12 glacial, the Megalopolis basin sustained a mesic habitat, sufficient plant cover and limited seasonal fluctuations in resource availability, pointing to its role as a glacial refugium for both fauna and hominins.
Parkinson’s disease detection based on features refinement through L1 regularized SVM and deep neural network
Authors: Ali Liaqat , Javeed Ashir , Noor Adeeb , Rauf Hafiz Tayyab , Kadry Seifedine , Gandomi Amir H. 
Published: January 2024

AbstractIn previous studies, replicated and multiple types of speech data have been used for Parkinson’s disease (PD) detection. However, two main problems in these studies are lower PD detection accuracy and inappropriate validation methodologies leading to unreliable results. This study discusses the effects of inappropriate validation methodologies used in previous studies and highlights the use of appropriate alternative validation methods that would ensure generalization. To enhance PD detection accuracy, we propose a two-stage diagnostic system that refines the extracted set of features through $$L_{1}$$

L
1

regularized linear support vector machine and classifies the refined subset of features through a deep neural network. To rigorously evaluate the effectiveness of the proposed diagnostic system, experiments are performed on two different voice recording-based benchmark datasets. For both datasets, the proposed diagnostic system achieves 100% accuracy under leave-one-subject-out (LOSO) cross-validation (CV) and 97.5% accuracy under k-fold CV. The results show that the proposed system outperforms the existing methods regarding PD detection accuracy. The results suggest that the proposed diagnostic system is essential to improving non-invasive diagnostic decision support in PD.
Prevalence and patterns of comorbidities in people with disabilities and their associated socio-demographic factors
Authors: Rana Md Shohel , Alam Md Badsha , Khanam Shimlin Jahan , Kabir Md Iqbal , Khandaker Gulam , Khan Md Nuruzzaman 
Published: January 2024

AbstractThe presence of comorbidities among individuals with disabilities worsens their already complex health and social circumstances. This study aims to explore prevalence and patterns of morbidities among persons with disabilities in Bangladesh and identify associated socio-demographic factors. Data from 4270 persons with disability was analysed extracted from the 2021 Bangladesh National Household Survey on Persons with Disability. Outcome variable considered was the occurrence of morbidity among persons with disabilities. Explanatory variables encompassed factors at the individual, household, and community levels. Adjusted and unadjusted multilevel mixed-effects logistic regression model was used to explore association of outcome variable with explanatory variables. We found that approximately half of individuals with disabilities experienced one or more morbidities, with chronic conditions being the most prevalent (44%). Around 42% of total persons with disability were unable to work. Specifically, hypertension (18.3%), diabetes (9.1%), and heart problems (17.1%) were prevalent chronic conditions. The likelihood of experiencing comorbidity was found to be higher among females (aOR 1.3, 95% CI 1.1, 1.7), increase year of education (aOR, 1.1, 95% CI 1.0–1.2), and those from wealthier households (aOR 1.6, 95% CI 1.2, 2.2). This underscores the need for targeted policies and interventions addressing their distinct healthcare needs.