bluuebunny/crossref_metadata_2025_split huggingface dataset:
YAP silencing by RB1 mutation is essential for small-cell lung cancer metastasis
Authors: Wu Zhengming , Su Junhui , Li Fu-long , Chen Tao , Mayner Jaimie , Engler Adam , Ma Shenghong , Li Qingquan , Guan Kun-Liang 
Published: September 2023

AbstractSmall cell lung cancer (SCLC) is highly lethal due to its prevalent metastasis. Most SCLCs have inactivating mutations in TP53 and RB1. We find that loss of YAP expression is key for SCLC cells to acquire rapid ameboid migration and high metastatic potential. YAP functions through its target genes CCN1/CCN2 to inhibit SCLC ameboid migration. RB1 mutation contributes to YAP transcriptional silencing via E2F7, which recruits the RCOR co-repressor complex to YAP promoter. We discover that benzamide family HDAC inhibitors stimulate YAP expression by inhibiting the RCOR-HDAC complex, thereby suppressing SCLC metastasis and improving survival in a mouse model. Our study unveils the molecular and cellular basis underlying SCLC’s high metastatic potential, the previously unrecognized role of YAP in suppressing ameboid migration and tumor metastasis, and the mechanism of YAP transcription regulation involving E2F7, RCOR, and Sin3 HDAC. This study reveals a therapeutic potential of benzamides for SCLC treatment.
SCARB2 drives hepatocellular carcinoma tumor initiating cells via enhanced MYC transcriptional activity
Authors: Wang Feng , Gao Yang , Xue Situ , Zhao Luyao , Jiang Huimin , Zhang Tingting , Li Yunxuan , Zhao Chenxi , Wu Fan , Siqin Tana , Liu Ying , Wu Jie , Yan Yechao , Yuan Jian , Jiang Jian-dong , Li Ke 
Published: September 2023

AbstractCSCs (Cancer stem cells) with distinct metabolic features are considered to cause HCC (hepatocellular carcinoma) initiation, metastasis and therapeutic resistance. Here, we perform a metabolic gene CRISPR/Cas9 knockout library screen in tumorspheres derived from HCC cells and find that deletion of SCARB2 suppresses the cancer stem cell-like properties of HCC cells. Knockout of Scarb2 in hepatocytes attenuates HCC initiation and progression in both MYC-driven and DEN (diethylnitrosamine)-induced HCC mouse models. Mechanistically, binding of SCARB2 with MYC promotes MYC acetylation by interfering with HDCA3-mediated MYC deacetylation on lysine 148 and subsequently enhances MYC transcriptional activity. Screening of a database of FDA (Food and Drug Administration)-approved drugs shows Polymyxin B displays high binding affinity for SCARB2 protein, disrupts the SCARB2-MYC interaction, decreases MYC activity, and reduces the tumor burden. Our study identifies SCARB2 as a functional driver of HCC and suggests Polymyxin B-based treatment as a targeted therapeutic option for HCC.
High-resolution single-photon imaging with physics-informed deep learning
Authors: Bian Liheng , Song Haoze , Peng Lintao , Chang Xuyang , Yang Xi , Horstmeyer Roarke , Ye Lin , Zhu Chunli , Qin Tong , Zheng Dezhi , Zhang Jun 
Published: September 2023

AbstractHigh-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique’s state-of-the-art super-resolution SPAD imaging performance.
Structural insights into FSP1 catalysis and ferroptosis inhibition
Authors: Lv Yun , Liang Chunhui , Sun Qichao , Zhu Jing , Xu Haiyan , Li Xiaoqing , Li Yao-yao , Wang Qihai , Yuan Huiqing , Chu Bo , Zhu Deyu 
Published: September 2023

AbstractFerroptosis suppressor protein 1 (FSP1, also known as AIMF2, AMID or PRG3) is a recently identified glutathione-independent ferroptosis suppressor1–3, but its underlying structural mechanism remains unknown. Here we report the crystal structures of Gallus gallus FSP1 in its substrate-free and ubiquinone-bound forms. The structures reveal a FAD-binding domain and a NAD(P)H-binding domain, both of which are shared with AIF and NADH oxidoreductases4–9, and a characteristic carboxy-terminal domain as well. We demonstrate that the carboxy-terminal domain is crucial for the catalytic activity and ferroptosis inhibition of FSP1 by mediating the functional dimerization of FSP1, and the formation of two active sites located on two sides of FAD, which are responsible for ubiquinone reduction and a unique FAD hydroxylation respectively. We also identify that FSP1 can catalyze the production of H2O2 and the conversion of FAD to 6-hydroxy-FAD in the presence of oxygen and NAD(P)H in vitro, and 6-hydroxy-FAD directly inhibits ferroptosis in cells. Together, these findings further our understanding on the catalytic and ferroptosis suppression mechanisms of FSP1 and establish 6-hydroxy-FAD as an active cofactor in FSP1 and a potent radical-trapping antioxidant in ferroptosis inhibition.
Molecular characterization of the N-terminal half of TasA during amyloid-like assembly and its contribution to Bacillus subtilis biofilm formation
Authors: Cámara-Almirón Jesús , Domínguez-García Laura , El Mammeri Nadia , Lends Alons , Habenstein Birgit , de Vicente Antonio , Loquet Antoine , Romero Diego 
Published: September 2023

AbstractBiofilms are bacterial communities that result from a cell differentiation process leading to the secretion of an extracellular matrix (ECM) by part of the population. In Bacillus subtilis, the main protein component of the ECM is TasA, which forms a fiber-based scaffold that confers structure to the ECM. The N-terminal half of TasA is strongly conserved among Bacillus species and contains a protein domain, the rigid core (RcTasA), which is critical for the structural and functional properties of the recombinant protein. In this study, we demonstrate that recombinantly purified RcTasA in vitro retains biochemical properties previously observed for the entire protein. Further analysis of the RcTasA amino acid sequence revealed two aggregation-prone stretches and a region of imperfect amino acid repeats, which are known to contribute to functional amyloid assembly. Biochemical characterization of these stretches found in RcTasA revealed their amyloid-like capacity in vitro, contributing to the amyloid nature of RcTasA. Moreover, the study of the imperfect amino acid repeats revealed the critical role of residues D64, K68 and D69 in the structural function of TasA. Experiments with versions of TasA carrying the substitutions D64A and K68AD69A demonstrated a partial loss of function of the protein either in the assembly of the ECM or in the stability of the core and amyloid-like properties. Taken together, our findings allow us to better understand the polymerization process of TasA during biofilm formation and provide knowledge into the sequence determinants that promote the molecular behavior of protein filaments in bacteria.
BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells
Authors: Wu John , Stewart William C. L. , Jayaprakash Ciriyam , Das Jayajit 
Published: September 2023

AbstractMechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.
A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States
Authors: Zhang Qiwei , Li Fangxing 
Published: September 2023

AbstractEfficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis.
Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques
Authors: Vijithananda Sahan M. , Jayatilake Mohan L. , Gonçalves Teresa C. , Rato Luis M. , Weerakoon Bimali S. , Kalupahana Tharindu D. , Silva Anil D. , Dissanayake Karuna , Hewavithana P. B. 
Published: September 2023

AbstractApparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.
Molecular dynamics simulations of methane adsorption and displacement from graphenylene shale reservoir nanochannels
Authors: Hajianzadeh Maryam , Mahmoudi Jafar , Sadeghzadeh Sadegh 
Published: September 2023

AbstractMethane is the main component of shale gas and is adsorbed in shale pores. Methane adsorption not only affects the estimation of shale gas reserves but also reduces extraction efficiency. Therefore, investigating the behavior of methane adsorption in shale reservoirs is important for evaluating shale gas resources, as well as understanding its desorption and displacement from the nanochannels of shale gas reservoirs. In this research, molecular dynamics simulations were used to investigate the adsorption behavior of methane gas in organic shale pores made of graphenylene, followed by its displacement by CO2 and N2 injection gases. The effects of pore size, pressure, and temperature on adsorption were examined. It was observed that increasing the pore size at a constant pressure led to a decrease in the density of adsorbed methane molecules near the pore surface, while a stable free phase with constant density formed in the central region of the nanopore. Moreover, adsorption increased with increasing pressure, and at pressures ranging from 0 to 3 MPa, 15 and 20 Å pores exhibited lower methane adsorption compared to other pores. The amount of adsorption decreased with increasing temperature, and the observed adsorption isotherm followed the Langmuir adsorption isotherm. The mechanism of methane displacement by the two injected gases differed. Carbon dioxide filled both vacant adsorption sites and directly replaced the adsorbed methane. On the other hand, nitrogen only adsorbed onto the vacant sites and, by reducing the partial pressure of methane, facilitated the displacement of methane.
Autonomic nervous system modulation during self-induced non-ordinary states of consciousness
Authors: Oswald Victor , Vanhaudenhuyse Audrey , Annen Jitka , Martial Charlotte , Bicego Aminata , Rousseaux Floriane , Sombrun Corine , Harel Yann , Faymonville Marie-Elisabeth , Laureys Steven , Jerbi Karim , Gosseries Olivia 
Published: September 2023

AbstractSelf-induced cognitive trance (SICT) is a voluntary non-ordinary state of consciousness characterized by a lucid yet narrowed awareness of the external surroundings. It involves a hyper-focused immersive experience of flow, expanded inner imagery, modified somatosensory processing, and an altered perception of self and time. SICT is gaining attention due to its potential clinical applications. Similar states of non-ordinary state of consciousness, such as meditation, hypnosis, and psychedelic experiences, have been reported to induce changes in the autonomic nervous system. However, the functioning of the autonomic nervous system during SICT remains poorly understood. In this study, we aimed to investigate the impact of SICT on the cardiac and respiratory signals of 25 participants proficient in SICT. To accomplish this, we measured various metrics of heart rate variability (HRV) and respiration rate variability (RRV) in three conditions: resting state, SICT, and a mental imagery task. Subsequently, we employed a machine learning framework utilizing a linear discriminant analysis classifier and a cross-validation scheme to identify the features that exhibited the best discrimination between these three conditions. The results revealed that during SICT, participants experienced an increased heart rate and a decreased level of high-frequency (HF) HRV compared to the control conditions. Additionally, specific increases in respiratory amplitude, phase ratio, and RRV were observed during SICT in comparison to the other conditions. These findings suggest that SICT is associated with a reduction in parasympathetic activity, indicative of a hyperarousal state of the autonomic nervous system during SICT.
The sourcil roundness index is a useful measure for quantifying acetabular concavity asphericity
Authors: Kitamura Kenji , Fujii Masanori , Motomura Goro , Hamai Satoshi , Kawahara Shinya , Sato Taishi , Yamaguchi Ryosuke , Hara Daisuke , Utsunomiya Takeshi , Kokubu Yasuhiko , Nakashima Yasuharu 
Published: September 2023

AbstractThis study aimed to clarify the clinical utility of the sourcil roundness index (SRI), a novel index for quantifying the asphericity of the acetabular concavity, by determining (1) the difference in the SRI between dysplastic and normal hips and (2) the correlation between the SRI and radiographic parameters of hip dysplasia. We reviewed standing anteroposterior pelvic radiographs of 109 dysplastic and 40 normal hips. The SRI was determined as the ratio of the distance from the medial edge of the sourcil to the most concave point of the acetabular sourcil (A) to the distance from the medial to the lateral edge of the sourcil (B). The formula for SRI is (A/B) × 100–50 (%), with an SRI of 0% indicating a perfectly spherical acetabulum, and higher SRI values indicating a more aspherical shape. The median SRI was greater in patients with hip dysplasia than in normal hips (5.9% vs. − 1.4%; p < 0.001). Furthermore, the median SRI was greater in the severe dysplasia subgroup (18.9%) than in the moderate (3.5%) and borderline-to-mild (− 1.3%) dysplasia subgroups (p < 0.05). Quantification of acetabular concavity asphericity by the SRI showed that dysplastic hips had a more lateral acetabular concave point than normal hips, and that the severity of hip dysplasia had an effect on the acetabular concavity asphericity.
Domestic greywater treatment using electrocoagulation-electrooxidation process: optimisation and experimental approaches
Authors: Mousazadeh Milad , Khademi Nastaran , Kabdaşlı Işık , Rezaei Seyedahmadreza , Hajalifard Zeinab , Moosakhani Zohreh , Hashim Khalid 
Published: September 2023

AbstractA synergistic combination of electrocoagulation-electrooxidation (EC-EO) process was used in the current study to treat domestic greywater. The EC process consisted of an aluminium (Al) anode and an iron (Fe) cathode, and the EO process consisted of titanium with platinum coating mesh (Ti/Pt) as an anode and stainless steel as a cathode. The effect of operative variables, namely current density, pH, EC time and EO time, on the removal of chemical oxygen demand (COD), colour, turbidity, and total organic carbon (TOC) was studied and optimised using Response Surface Methodology (RSM). The results showed that although the pH affected the removal of all studied pollutants, it had more effect on turbidity removal with a contribution of 88.44%, while the current density had the main dominant effect on colour removal with a contribution of 73.59%. It was also found that at optimal operation conditions for a current density of 2.6 A, an initial pH of 4.67, an EC time of 31.67 min, and an EO time of 93.28 min led to a COD, colour, turbidity, and TOC removal rates of 96.1%, 97.5%, 90.9%, and 98%, respectively, which were close to the predicted results. The average operating cost and energy consumption for the removal of COD, colour, turbidity, and TOC were 0.014 $/m3 and 0.01 kWh/kg, 0.083 $/m3 and 0.008 kWh/kg, 0.075 $/m3 and 0.062 kWh/kg, and 0.105 $/m3 and 0.079 kWh/kg, respectively.
A reliable numerical investigation of an SEIR model of measles disease dynamics with fuzzy criteria
Authors: Dayan Fazal , Ahmed Nauman , Rafiq Muhammad , Raza Ali , Khan Ilyas , eldin Elsayed Mohamed Tag 
Published: September 2023

AbstractThe terms susceptibility, exposure, infectiousness, and recovered all have some inherent ambiguity because different population members have different susceptibility levels, exposure levels, infectiousness levels, and recovery patterns. This uncertainty becomes more pronounced when examining population subgroups characterized by distinct behaviors, cultural norms, and varying degrees of resilience across different age brackets, thereby introducing the possibility of fluctuations. There is a need for more accurate models that take into account the various levels of susceptibility, exposure, infectiousness, and recovery of the individuals. A fuzzy SEIR model of the dynamics of the measles disease is discussed in this article. The rates of disease transmission and recovery are treated as fuzzy sets. Three distinct numerical approaches, the forward Euler, fourth-order Runge-Kutta, and nonstandard finite difference (NSFD) are employed for the resolution of this fuzzy SEIR model. Next, the outcomes of the three methods are examined. The results of the simulation demonstrate that the NSFD method adeptly portrays convergent solutions across various time step sizes. Conversely, the conventional Euler and RK-4 methods only exhibit positivity and convergence solutions when handling smaller step sizes. Even when considering larger step sizes, the NSFD method maintains its consistency, showcasing its efficacy. This demonstrates the NSFD technique’s superior reliability when compared to the other two methods, while maintaining all essential aspects of a continuous dynamical system. Additionally, the results from numerical and simulation studies offer solid proof that the suggested NSFD technique is a reliable and effective tool for controlling these kinds of dynamical systems.The convergence and consistency analysis of the NSFD method are also studied.