bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolic adaptations in prostate cancer
Authors: Pujana-Vaquerizo Mikel , Bozal-Basterra Laura , Carracedo Arkaitz 
Published: July 2024

AbstractProstate cancer is one of the most commonly diagnosed cancers in men and is a major cause of cancer-related deaths worldwide. Among the molecular processes that contribute to this disease, the weight of metabolism has been placed under the limelight in recent years. Tumours exhibit metabolic adaptations to comply with their biosynthetic needs. However, metabolites also play an important role in supporting cell survival in challenging environments or remodelling the tumour microenvironment, thus being recognized as a hallmark in cancer. Prostate cancer is uniquely driven by androgen receptor signalling, and this knowledge has also influenced the paths of cancer metabolism research. This review provides a comprehensive perspective on the metabolic adaptations that support prostate cancer progression beyond androgen signalling, with a particular focus on tumour cell intrinsic and extrinsic pathways.
Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering
Authors: Cheng Peng , Mao Cong , Tang Jin , Yang Sen , Cheng Yu , Wang Wuke , Gu Qiuxi , Han Wei , Chen Hao , Li Sihan , Chen Yaofeng , Zhou Jianglin , Li Wuju , Pan Aimin , Zhao Suwen , Huang Xingxu , Zhu Shiqiang , Zhang Jun , Shu Wenjie , Wang Shengqi 
Published: July 2024

AbstractMutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.
Functional multi-organelle units control inflammatory lipid metabolism of macrophages
Authors: Zimmermann Julia A. , Lucht Kerstin , Stecher Manuel , Badhan Chahat , Glaser Katharina M. , Epple Maximilian W. , Koch Lena R. , Deboutte Ward , Manke Thomas , Ebnet Klaus , Brinkmann Frauke , Fehler Olesja , Vogl Thomas , Schuster Ev-Marie , Bremser Anna , Buescher Joerg M. , Rambold Angelika S. 
Published: July 2024

AbstractEukaryotic cells contain several membrane-separated organelles to compartmentalize distinct metabolic reactions. However, it has remained unclear how these organelle systems are coordinated when cells adapt metabolic pathways to support their development, survival or effector functions. Here we present OrgaPlexing, a multi-spectral organelle imaging approach for the comprehensive mapping of six key metabolic organelles and their interactions. We use this analysis on macrophages, immune cells that undergo rapid metabolic switches upon sensing bacterial and inflammatory stimuli. Our results identify lipid droplets (LDs) as primary inflammatory responder organelle, which forms three- and four-way interactions with other organelles. While clusters with endoplasmic reticulum (ER) and mitochondria (mitochondria–ER–LD unit) help supply fatty acids for LD growth, the additional recruitment of peroxisomes (mitochondria–ER–peroxisome–LD unit) supports fatty acid efflux from LDs. Interference with individual components of these units has direct functional consequences for inflammatory lipid mediator synthesis. Together, we show that macrophages form functional multi-organellar units to support metabolic adaptation and provide an experimental strategy to identify organelle-metabolic signalling hubs.
Algal methylated compounds shorten the lag phase of Phaeobacter inhibens bacteria
Authors: Sperfeld Martin , Narváez-Barragán Delia A. , Malitsky Sergey , Frydman Veronica , Yuda Lilach , Rocha Jorge , Segev Einat 
Published: July 2024

AbstractThe lag phase is key in resuming bacterial growth, but it remains underexplored particularly in environmental bacteria. Here we use transcriptomics and 13C-labelled metabolomics to show that the lag phase of the model marine bacterium Phaeobacter inhibens is shortened by methylated compounds produced by the microalgal partner, Emiliania huxleyi. Methylated compounds are abundantly produced and released by microalgae, and we show that their methyl groups can be collected by bacteria and assimilated through the methionine cycle. Our findings underscore the significance of methyl groups as a limiting factor during the lag phase and highlight the adjustability of this growth phase. In addition, we show that methylated compounds, typical of photosynthetic organisms, prompt diverse reductions in lag times in bacteria associated with algae and plants, potentially favouring early growth in some bacteria. These findings suggest ways to accelerate bacterial growth and underscore the significance of studying bacteria within an environmental context.
Saturation genome editing of BAP1 functionally classifies somatic and germline variants
Authors: Waters Andrew J. , Brendler-Spaeth Timothy , Smith Danielle , Offord Victoria , Tan Hong Kee , Zhao Yajie , Obolenski Sofia , Nielsen Maartje , van Doorn Remco , Murphy Jo-Ellen , Gupta Prashant , Rowlands Charlie F. , Hanson Helen , Delage Erwan , Thomas Mark , Radford Elizabeth J. , Gerety Sebastian S. , Turnbull Clare , Perry John R. B. , Hurles Matthew E. , Adams David J. 
Published: July 2024

AbstractMany variants that we inherit from our parents or acquire de novo or somatically are rare, limiting the precision with which we can associate them with disease. We performed exhaustive saturation genome editing (SGE) of BAP1, the disruption of which is linked to tumorigenesis and altered neurodevelopment. We experimentally characterized 18,108 unique variants, of which 6,196 were found to have abnormal functions, and then used these data to evaluate phenotypic associations in the UK Biobank. We also characterized variants in a large population-ascertained tumor collection, in cancer pedigrees and ClinVar, and explored the behavior of cancer-associated variants compared to that of variants linked to neurodevelopmental phenotypes. Our analyses demonstrated that disruptive germline BAP1 variants were significantly associated with higher circulating levels of the mitogen IGF-1, suggesting a possible pathological mechanism and therapeutic target. Furthermore, we built a variant classifier with >98% sensitivity and specificity and quantify evidence strengths to aid precision variant interpretation.
Saturation genome editing maps the functional spectrum of pathogenic VHL alleles
Authors: Buckley Megan , Terwagne Chloé , Ganner Athina , Cubitt Laura , Brewer Reid , Kim Dong-Kyu , Kajba Christina M. , Forrester Nicole , Dace Phoebe , De Jonghe Joachim , Shepherd Scott T. C. , Sawyer Chelsea , McEwen Mairead , Diederichs Sven , Neumann-Haefelin Elke , Turajlic Samra , Ivakine Evgueni A. , Findlay Gregory M. 
Published: July 2024

AbstractTo maximize the impact of precision medicine approaches, it is critical to identify genetic variants underlying disease and to accurately quantify their functional effects. A gene exemplifying the challenge of variant interpretation is the von Hippel–Lindautumor suppressor (VHL). VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here we optimize and apply saturation genome editing to assay nearly all possible single-nucleotide variants (SNVs) across VHL’s coding sequence. To delineate mechanisms, we quantify mRNA dosage effects and compare functional effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types and reveal new mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered clinically and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype–phenotype relationships across complete genes.
Improvement of sand-washing performance and internal flow field analysis of a novel downhole sand removal device
Authors: Wang Zhiliang , Fang Zhigang , Wang Zhensong , Zhang Manlai , Liao Ruiquan 
Published: July 2024

AbstractWith the progression of many shale gas wells in the Sichuan-Chongqing region of China into the middle and late stages of exploitation, the problem of sand production in these wells is a primary factor influencing production. Failure to implement measures to remove sand from the gas wells will lead to a sharp decline in production after a certain period of exploitation. Moreover, As the amount of sand produced in the well increases, the production layer will be potentially buried by sand. To boost the production of shale gas wells in the Sichuan-Chongqing region and improve production efficiency, a novel downhole jet sand-washing device has been developed. Upon analyzing the device's overall structure, it is revealed that the device adopts a structural design integrating a jet pump with an efficient sand- washing nozzle, providing dual capabilities for jet sand- washing and sand conveying via negative pressure. To enhance the sand- washing and unblocking performance of the device, various sand- washing fluids and the structures of different sand- washing nozzles are compared for selection, aiming to elevate the device's sand- washing and unblocking performance from a macro perspective. Subsequently, drawing on simulation and internal flow field analysis of the device's sand- washing and unblocking process through CFD and the control variable method, it is ultimately found that the length diameter ratio of the cylindrical segment of the nozzle outlet, the outlet diameter, and the contraction angle of the nozzle greatly influence the device's sand- washing and unblocking performance. And the optimum ranges for the length-diameter ratio of the cylindrical segment of the nozzle outlet, the outlet diameter, the contraction angle of the nozzle, and the inlet diameter are 2 to 4, 6 mm to 10 mm, 12° to 16°, and 18 mm and 22 mm, respectively. The findings of the research not only provide new insights into existing sand removal processes but also offer a novel structure for current downhole sand removal devices and a specific range for the optimal size of the nozzle.
Paramixta manurensis gen. nov., sp. nov., a novel member of the family Erwiniaceae producing indole-3-acetic acid isolated from mushroom compost
Authors: Kim Jueun , Yun Hyosuk , Tahmasebi Aminallah , Nam Jiyoung , Pham Ha , Kim Yong-Hak , Min Hye Jung , Lee Chul Won 
Published: July 2024

AbstractThere are numerous species in the Erwiniaceae family that are important for agricultural and clinical purposes. Here we described the Erwiniaceae bacterium PD-1 isolated from mushroom (Pleurotus eryngii) compost. Comparative genomic and phylogenetic analyses showed that the strain PD-1 was assigned to a new genus and species, Paramixta manurensis gen. nov., sp. nov. in the family Erwiniaceae. From the average amino acid index, we identified the five AroBEKAC proteins in the shikimate pathway as a minimal set of molecular markers to reconstruct the phylogenetic tree of the Erwiniaceae species. The strain PD-1 containing annotated genes for ubiquinone and menaquinone produced a higher level of ubiquinone (Q8) than demethylmenaquinone (DMK8) and menaquinone (MK8) in anaerobic condition compared to aerobic condition, as similarly did the reference strains from the genera Mixta and Erwinia. Results from fatty acid methyl ester and numerical analyses of strain PD-1 showed a similarity to species of the genera Mixta and Winslowiella. This study revealed that the strain’s ability to utilize polyols, such as glycerol, erythritol, and d-arabitol, distinguished the strain PD-1 from the nearest relative and other type strains. The analyzed genetic markers and biochemical properties of the strain PD-1 suggest its potential role in the process of mushroom compost through the degradation of carbohydrates and polysaccharides derived from fungi and plants. Additionally, it can produce a high concentration of indole-3-acetic acid as a plant growth-promoting agent.
Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto’s thyroiditis through bioinformatics analysis and machine learning
Authors: Jiang Hui , He Yanbin , Lan Xiaofeng , Xie Xiang 
Published: July 2024

AbstractThere is a growing body of evidence suggesting that Hashimoto’s thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein–protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.
Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model
Authors: Alsulami Abdulaziz A. , Abu Al-Haija Qasem , Alturki Badraddin , Alqahtani Ali , Binzagr Faisal , Alghamdi Bandar , Alsemmeari Rayan A. 
Published: July 2024

AbstractMicrogrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.
Quadrotor with wheels: design and experimental evaluation
Authors: Aizelman Ilan , Magazinnik Dan , Feldman Dan , Klein Itzik 
Published: July 2024

AbstractQuadrotors have found widespread use in indoor applications, including tracking and mapping. In general, to carry out such tasks effectively, a navigation solution should provide both accuracy and battery efficiency. To achieve both, we propose a cost-effective and lightweight wheeled quadrotor that combines both driving and flying capabilities. Our design allows the quadrotor to perform both functions seamlessly. We provide a detailed description of the design and construction process, highlighting its advantages. Our focus was on the Tello quadrotor, which weighs 80 grams. Our design allowed driving capability with an increased weight of only fifteen grams, resulting in less than 20% of the added weight. Furthermore, we evaluate the quadrotor’s pure inertial navigation performance and corresponding battery consumption by employing various flying and driving patterns. Our results show that when only driving the battery consumption was the lowest with 10% and some flying scenarios improve the positioning error by more than 70%.
An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm
Authors: Srinivasan M. Nuthal , Chinnadurai M. , Senthilkumar S. , Dinesh E. 
Published: July 2024

AbstractIn recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique’s versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.
Nuclear ATP-citrate lyase regulates chromatin-dependent activation and maintenance of the myofibroblast gene program
Authors: Lazaropoulos Michael P. , Gibb Andrew A. , Chapski Douglas J. , Nair Abheya A. , Reiter Allison N. , Roy Rajika , Eaton Deborah M. , Bedi Kenneth C. , Margulies Kenneth B. , Wellen Kathryn E. , Estarás Conchi , Vondriska Thomas M. , Elrod John W. 
Published: July 2024

AbstractDifferentiation of cardiac fibroblasts to myofibroblasts is necessary for matrix remodeling and fibrosis in heart failure. We previously reported that mitochondrial calcium signaling drives α-ketoglutarate-dependent histone demethylation, promoting myofibroblast formation. Here we investigate the role of ATP-citrate lyase (ACLY), a key enzyme for acetyl-CoA biosynthesis, in histone acetylation regulating myofibroblast fate and persistence in cardiac fibrosis. We show that inactivation of ACLY prevents myofibroblast differentiation and reverses myofibroblasts towards quiescence. Genetic deletion of Acly in post-activated myofibroblasts prevents fibrosis and preserves cardiac function in pressure-overload heart failure. TGFβ stimulation enhances ACLY nuclear localization and ACLY–SMAD2/3 interaction, and increases H3K27ac at fibrotic gene loci. Pharmacological inhibition of ACLY or forced nuclear expression of a dominant-negative ACLY mutant prevents myofibroblast formation and H3K27ac. Our data indicate that nuclear ACLY activity is necessary for myofibroblast differentiation and persistence by maintaining histone acetylation at TGFβ-induced myofibroblast genes. These findings provide targets to prevent and reverse pathological fibrosis.
Operability-economics trade-offs in adsorption-based CO2 capture processes
Authors: Sachio Steven , Ward Adam , Pini Ronny , Papathanasiou Maria M. 
Published: July 2024

AbstractDispatchable low-carbon power underpins the transition to a sustainable energy system, providing balancing load for the integration of intermittent renewable power. In such load-following operation, the post-combustion carbon capture process must be capable of highly transient operation. Here we have developed a computational framework that integrates process design, operability and techno-economic assessment of a pressure-vacuum swing adsorption process for CO2 capture. We demonstrate that the cost-optimal design has limited process flexibility, challenging reactiveness to disturbances in the flue gas conditions. Flexibility can be introduced by relaxing the CO2 recovery constraint on the operation, albeit at the expense of the capture efficiency of the process. We discover that adsorption-based processes can be designed to enhance flexibility, while improving performance with respect to the operational constraints on CO2 recovery and purity. The results herein demonstrate a trade-off between process economics and process operability, which must be rationalised to integrate CO2 capture units in low-carbon energy systems.