bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metronomic and single high-dose paclitaxel treatments produce distinct heterogenous chemoresistant cancer cell populations
Authors: Mejia Peña Carolina , Skipper Thomas A. , Hsu Jeffrey , Schechter Ilexa , Ghosh Deepraj , Dawson Michelle R. 
Published: November 2023

AbstractMore than 75% of epithelial ovarian cancer (EOC) patients experience disease recurrence after initial treatment, highlighting our incomplete understanding of how chemoresistant populations evolve over the course of EOC progression post chemotherapy treatment. Here, we show how two paclitaxel (PTX) treatment methods- a single high dose and a weekly metronomic dose for four weeks, generate unique chemoresistant populations. Using mechanically relevant alginate microspheres and a combination of transcript profiling and heterogeneity analyses, we found that these PTX-treatment regimens produce distinct and resilient subpopulations that differ in metabolic reprogramming signatures, acquisition of resistance to PTX and anoikis, and the enrichment for cancer stem cells (CSCs) and polyploid giant cancer cells (PGCCs) with the ability to replenish bulk populations. We investigated the longevity of these metabolic reprogramming events using untargeted metabolomics and found that metabolites associated with stemness and therapy-induced senescence were uniquely abundant in populations enriched for CSCs and PGCCs. Predictive network analysis revealed that antioxidative mechanisms were likely to be differentially active dependent on both time and exposure to PTX. Our results illustrate how current standard chemotherapies contribute to the development of chemoresistant EOC subpopulations by either selecting for intrinsically resistant subpopulations or promoting the evolution of resistance mechanisms. Additionally, our work describes the unique phenotypic signatures in each of these distinct resistant subpopulations and thus highlights potential vulnerabilities that can be exploited for more effective treatment.
A new look at Hsp70 activity in phosphatidylserine-enriched membranes: chaperone-induced quasi-interdigitated lipid phase
Authors: Tagaeva Ruslana , Efimova Svetlana , Ischenko Alexander , Zhakhov Alexander , Shevtsov Maxim , Ostroumova Olga 
Published: November 2023

Abstract70 kDa heat shock protein Hsp70 (also termed HSP70A1A) is the major stress-inducible member of the HSP70 chaperone family, which is present on the plasma membranes of various tumor cells, but not on the membranes of the corresponding normal cells. The exact mechanisms of Hsp70 anchoring in the membrane and its membrane-related functions are still under debate, since the protein does not contain consensus signal sequence responsible for translocation from the cytosol to the lipid bilayer. The present study was focused on the analysis of the interaction of recombinant human Hsp70 with the model phospholipid membranes. We have confirmed that Hsp70 has strong specificity toward membranes composed of negatively charged phosphatidylserine (PS), compared to neutral phosphatidylcholine membranes. Using differential scanning calorimetry, we have shown for the first time that Hsp70 affects the thermotropic behavior of saturated PS and leads to the interdigitation that controls membrane thickness and rigidity. Hsp70-PS interaction depended on the lipid phase state; the protein stabilized ordered domains enriched with high-melting PS, increasing their area, probably due to formation of quasi-interdigitated phase. Moreover, the ability of Hsp70 to form ion-permeable pores in PS membranes may also be determined by the bilayer thickness. These observations contribute to a better understanding of Hsp70-PS interaction and biological functions of membrane-bound Hsp70 in cancer cells.
Image-based crop disease detection with federated learning
Authors: Mamba Kabala Denis , Hafiane Adel , Bobelin Laurent , Canals Raphaël 
Published: November 2023

AbstractCrop disease detection and management is critical to improving productivity, reducing costs, and promoting environmentally friendly crop treatment methods. Modern technologies, such as data mining and machine learning algorithms, have been used to develop automated crop disease detection systems. However, centralized approach to data collection and model training induces challenges in terms of data privacy, availability, and transfer costs. To address these challenges, federated learning appears to be a promising solution. In this paper, we explored the application of federated learning for crop disease classification using image analysis. We developed and studied convolutional neural network (CNN) models and those based on attention mechanisms, in this case vision transformers (ViT), using federated learning, leveraging an open access image dataset from the “PlantVillage” platform. Experiments conducted concluded that the performance of models trained by federated learning is influenced by the number of learners involved, the number of communication rounds, the number of local iterations and the quality of the data. With the objective of highlighting the potential of federated learning in crop disease classification, among the CNN models tested, ResNet50 performed better in several experiments than the other models, and proved to be an optimal choice, but also the most suitable for a federated learning scenario. The ViT_B16 and ViT_B32 Vision Transformers require more computational time, making them less suitable in a federated learning scenario, where computational time and communication costs are key parameters. The paper provides a state-of-the-art analysis, presents our methodology and experimental results, and concludes with ideas and future directions for our research on using federated learning in the context of crop disease classification.
Metabolic engineering of Saccharomyces cerevisiae for second-generation ethanol production from xylo-oligosaccharides and acetate
Authors: Procópio Dielle Pierotti , Lee Jae Won , Shin Jonghyeok , Tramontina Robson , Ávila Patrícia Felix , Brenelli Lívia Beatriz , Squina Fabio Márcio , Damasio André , Rabelo Sarita Cândida , Goldbeck Rosana , Franco Telma Teixeira , Leak David , Jin Yong-Su , Basso Thiago Olitta 
Published: November 2023

AbstractSimultaneous intracellular depolymerization of xylo-oligosaccharides (XOS) and acetate fermentation by engineered Saccharomyces cerevisiae offers significant potential for more cost-effective second-generation (2G) ethanol production. In the present work, the previously engineered S. cerevisiae strain, SR8A6S3, expressing enzymes for xylose assimilation along with an optimized route for acetate reduction, was used as the host for expressing two β-xylosidases, GH43-2 and GH43-7, and a xylodextrin transporter, CDT-2, from Neurospora crassa, yielding the engineered SR8A6S3-CDT-2-GH34-2/7 strain. Both β-xylosidases and the transporter were introduced by replacing two endogenous genes, GRE3 and SOR1, that encode aldose reductase and sorbitol (xylitol) dehydrogenase, respectively, and catalyse steps in xylitol production. The engineered strain, SR8A6S3-CDT-2-GH34-2/7 (sor1Δ gre3Δ), produced ethanol through simultaneous XOS, xylose, and acetate co-utilization. The mutant strain produced 60% more ethanol and 12% less xylitol than the control strain when a hemicellulosic hydrolysate was used as a mono- and oligosaccharide source. Similarly, the ethanol yield was 84% higher for the engineered strain using hydrolysed xylan, compared with the parental strain. Xylan, a common polysaccharide in lignocellulosic residues, enables recombinant strains to outcompete contaminants in fermentation tanks, as XOS transport and breakdown occur intracellularly. Furthermore, acetic acid is a ubiquitous toxic component in lignocellulosic hydrolysates, deriving from hemicellulose and lignin breakdown. Therefore, the consumption of XOS, xylose, and acetate expands the capabilities of S. cerevisiae for utilization of all of the carbohydrate in lignocellulose, potentially increasing the efficiency of 2G biofuel production.
Impact of germline DNA repair gene variants on prognosis and treatment of men with advanced prostate cancer
Authors: Hansen Emma B. , Karlsson Questa , Merson Susan , Wakerell Sarah , Rageevakumar Reshma , Jensen Jørgen B. , Borre Michael , Kote-Jarai Zsofia , Eeles Rosalind A. , Sørensen Karina D. 
Published: November 2023

AbstractThe clinical importance of germline variants in DNA repair genes (DRGs) is becoming increasingly recognized, but their impact on advanced prostate cancer prognosis remains unclear. A cohort of 221 newly diagnosed metastatic castration-resistant prostate cancer (mCRPC) patients were screened for pathogenic germline variants in 114 DRGs. The primary endpoint was progression-free survival (PFS) on first-line androgen signaling inhibitor (ARSI) treatment for mCRPC. Secondary endpoints were time to mCRPC progression on initial androgen deprivation therapy (ADT) and overall survival (OS). Twenty-seven patients (12.2%) carried a germline DRG variant. DRG carrier status was independently associated with shorter PFS on first-line ARSI [HR 1.72 (1.06–2.81), P = 0.029]. At initiation of ADT, DRG carrier status was independently associated with shorter progression time to mCRPC [HR 1.56, (1.02–2.39), P = 0.04] and shorter OS [HR 1.99, (1.12–3.52), P = 0.02]. Investigating the contributions of individual germline DRG variants on PFS and OS revealed CHEK2 variants to have little effect. Furthermore, prior taxane treatment was associated with worse PFS on first-line ARSI for DRG carriers excluding CHEK2 (P = 0.0001), but not for noncarriers. In conclusion, germline DRG carrier status holds independent prognostic value for predicting advanced prostate cancer patient outcomes and may potentially inform on optimal treatment sequencing already at the hormone-sensitive stage.
A universal framework for accurate and efficient geometric deep learning of molecular systems
Authors: Zhang Shuo , Liu Yang , Xie Lei 
Published: November 2023

AbstractMolecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. As a result, PAMNet can reduce expensive operations, making it time and memory efficient. In extensive benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks: small molecule properties, RNA 3D structures, and protein-ligand binding affinities. Our results highlight the potential for PAMNet in a broad range of molecular science applications.
Hippocampal volume mediates the relationship of parental rejection in childhood with social cognition in healthy adults
Authors: Kawamoto Marino , Takagishi Haruto , Ishihara Toru , Takagi Shunsuke , Kanai Ryota , Sugihara Genichi , Takahashi Hidehiko , Matsuda Tetsuya 
Published: November 2023

AbstractChildhood abuse reduces hippocampal and amygdala volumes and impairs social cognition, including the ability to recognize facial expressions. However, these associations have been studied primarily in individuals with a history of severe abuse and psychiatric symptoms; researchers have not determined whether these associations can also be observed in healthy adults. In the present study, we analyzed data from 400 healthy adults (208 men and 192 women) at Tamagawa University. Parental rejection reflecting childhood abuse was assessed using the short form of Egna Minnen Beträffande Uppfostran, while social cognition was assessed using the “Fake Smile Detection Task.” Hippocampal and amygdala volumes were extracted from T1-weighted magnetic resonance imaging data using FreeSurfer. We found that greater parental rejection resulted in smaller hippocampal and amygdala volumes and poorer performance in the Fake Smile Detection Task. Structural equation modeling analysis supported the model that hippocampal volume mediates maternal rejection effect on performance on the Fake Smile Detection Task, with involvement of the amygdala. These findings are in line with the structural and functional connectivity found between the hippocampus and amygdala and their joint involvement in social cognition. Therefore, parental rejection may affect hippocampal and amygdala volumes and social cognitive function even in symptom-free adults.
Improved maximum likelihood method for P-S-N curve fitting method with small number specimens and application in T-welded joint
Authors: Liu Wenfei , Zhang Li , He Liwen , Liu Hailang 
Published: November 2023

AbstractIn fatigue data analysis, fitting accurate P-S-N curve is problematic if only a small number of specimen is available, especially to evaluate the relationship between the stress level and the standard deviation. This paper proposes a sample information reconstruction method that can effectively solve this problem. Based on this method and the life equivalent principle, a new maximum likelihood method (which is abbreviated to improved maximum likelihood method) is proposed for P-S-N curve fitting. T-joint specimens of Q450NQR1 steel were fabricated and tested, then the P-S-N curves was fitted by the improved maximum likelihood method, least square method, maximum likelihood method, standard BS7608 and standard IIW. Finally, P-S-N curves by three methods and two standards are compared and analyzed. The results show that the relevant parameters of the P-S-N curve with 99.9% survival probability fitted by the improved maximum likelihood method are similar to those in the two standards, and it is indicated that the improved maximum likelihood method is a better way for P-S-N curve fitting with the small number of fatigue test specimens.
Chondrocytes supplemented to bone graft-containing scaffolds expedite cranial defect repair
Authors: Carmon Idan , Zobrab Anna , Alterman Michael , Tabib Rami , Cohen Adir , Kandel Leonid , Greenberg Alexander , Reich Eli , Casap Nardi , Dvir-Ginzberg Mona 
Published: November 2023

AbstractCritical maxillofacial bone fractures do not heal spontaneously, thus, often there is a need to facilitate repair via surgical intervention. Gold standard approaches, include the use of autologous bone graft, or devices supplemented with osteogenic growth factors and bone substitutes. This research aimed to employ a critical size calvaria defect model, to determine if the addition of chondrocytes to collagen-containing bone graft substitute, may expedite bone repair. As such, using a critical size rat calvaria defect, we implanted a collagen scaffold containing bone graft substitute (i.e., Bone graft scaffold, BG) or BG supplemented with costal chondrocytes (cBG). The rats were subjected to live CT imaging at 1, 6, 9, and 12 weeks following the surgical procedure and sacrificed for microCT imaging of the defect site. Moreover, serum markers and histological evaluation were assessed to determine osseous tissue regeneration and turnover. Live CT and microCT indicated cBG implants displayed expedited bone repair vs, BG alone, already at 6 weeks post defect induction. cBG also displayed a shorter distance between the defect edges and greater mineral apposition distance compared to BG. Summerizing, the data support the addition of chondrocytes to bone substitute, accelerates the formation of new bone within a critical size defect.
Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors
Authors: Liu Junyan , Islam Md Tauhidul , Sang Shengtian , Qiu Liang , Xing Lei 
Published: November 2023

AbstractThe response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The “black box” nature of deep learning is a major concern in healthcare field. This model’s interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a ‘black box’ approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.
Corvids optimize working memory by categorizing continuous stimuli
Authors: Apostel Aylin , Panichello Matthew , Buschman Timothy J. , Rose Jonas 
Published: November 2023

AbstractWorking memory (WM) is a crucial element of the higher cognition of primates and corvid songbirds. Despite its importance, WM has a severely limited capacity and is vulnerable to noise. In primates, attractor dynamics mitigate the effect of noise by discretizing continuous information. Yet, it remains unclear whether similar dynamics are seen in avian brains. Here, we show jackdaws (Corvus monedula) have similar behavioral biases as humans; memories are less precise and more biased as memory demands increase. Model-based analysis reveal discrete attractors are evenly spread across the stimulus space. Altogether, our comparative approach suggests attractor dynamics in primates and corvids mitigate the effect of noise by systematically drifting towards specific attractors. By demonstrating this effect in an evolutionary distant species, our results strengthen attractor dynamics as general, adaptive biological principle to efficiently use WM.
Sustained CREB phosphorylation by lipid-peptide liquid crystalline nanoassemblies
Authors: Wu Yu , Angelov Borislav , Deng Yuru , Fujino Takehiko , Hossain Md Shamim , Drechsler Markus , Angelova Angelina 
Published: November 2023

AbstractCyclic-AMP-response element-binding protein (CREB) is a leucine zipper class transcription factor that is activated through phosphorylation. Ample CREB phosphorylation is required for neurotrophin expression, which is of key importance for preventing and regenerating neurological disorders, including the sequelae of long COVID syndrome. Here we created lipid-peptide nanoassemblies with different liquid crystalline structural organizations (cubosomes, hexosomes, and vesicles) as innovative nanomedicine delivery systems of bioactive PUFA-plasmalogens (vinyl ether phospholipids with polyunsaturated fatty acid chains) and a neurotrophic pituitary adenylate cyclase-activating polypeptide (PACAP). Considering that plasmalogen deficiency is a potentially causative factor for neurodegeneration, we examined the impact of nanoassemblies type and incubation time in an in vitro Parkinson’s disease (PD) model as critical parameters for the induction of CREB phosphorylation. The determined kinetic changes in CREB, AKT, and ERK-protein phosphorylation reveal that non-lamellar PUFA-plasmalogen-loaded liquid crystalline lipid nanoparticles significantly prolong CREB activation in the neurodegeneration model, an effect unattainable with free drugs, and this effect can be further enhanced by the cell-penetrating peptide PACAP. Understanding the sustained CREB activation response to neurotrophic nanoassemblies might lead to more efficient use of nanomedicines in neuroregeneration.
Orbital-selective metal skin induced by alkali-metal-dosing Mott-insulating Ca2RuO4
Authors: Horio Masafumi , Forte Filomena , Sutter Denys , Kim Minjae , Fatuzzo Claudia G. , Matt Christian E. , Moser Simon , Wada Tetsuya , Granata Veronica , Fittipaldi Rosalba , Sassa Yasmine , Gatti Gianmarco , Rønnow Henrik M. , Hoesch Moritz , Kim Timur K. , Jozwiak Chris , Bostwick Aaron , Rotenberg Eli , Matsuda Iwao , Georges Antoine , Sangiovanni Giorgio , Vecchione Antonio , Cuoco Mario , Chang Johan 
Published: November 2023

AbstractDoped Mott insulators are the starting point for interesting physics such as high temperature superconductivity and quantum spin liquids. For multi-band Mott insulators, orbital selective ground states have been envisioned. However, orbital selective metals and Mott insulators have been difficult to realize experimentally. Here we demonstrate by photoemission spectroscopy how Ca2RuO4, upon alkali-metal surface doping, develops a single-band metal skin. Our dynamical mean field theory calculations reveal that homogeneous electron doping of Ca2RuO4 results in a multi-band metal. All together, our results provide evidence for an orbital-selective Mott insulator breakdown, which is unachievable via simple electron doping. Supported by a cluster model and cluster perturbation theory calculations, we demonstrate a type of skin metal-insulator transition induced by surface dopants that orbital-selectively hybridize with the bulk Mott state and in turn produce coherent in-gap states.
Glycolysis-derived alanine from glia fuels neuronal mitochondria for memory in Drosophila
Authors: Rabah Yasmine , Francés Raquel , Minatchy Julia , Guédon Laura , Desnous Coraline , Plaçais Pierre-Yves , Preat Thomas 
Published: November 2023

AbstractGlucose is the primary source of energy for the brain; however, it remains controversial whether, upon neuronal activation, glucose is primarily used by neurons for ATP production or if it is partially oxidized in astrocytes, as proposed by the astrocyte–neuron lactate shuttle model for glutamatergic neurons. Thus, an in vivo picture of glucose metabolism during cognitive processes is missing. Here, we uncover in Drosophila melanogaster a glia-to-neuron alanine transfer involving alanine aminotransferase that sustains memory formation. Following associative conditioning, glycolysis in glial cells produces alanine, which is back-converted into pyruvate in cholinergic neurons of the olfactory memory center to uphold their increased mitochondrial needs. Alanine, as a mediator of glia–neuron coupling, could be an alternative to lactate in cholinergic systems. In parallel, a dedicated glial glucose transporter imports glucose specifically for long-term memory, by directly transferring it to neurons for use by the pentose phosphate pathway. Our results demonstrate in vivo the compartmentalization of glucose metabolism between neurons and glial cells during memory formation.
Machine learning-derived reaction statistics for 3D spectroimaging of copper sulfidation in heterogeneous rubber/brass composites
Authors: Matsui Hirosuke , Muramoto Yuta , Niwa Ryusei , Kakubo Takashi , Amino Naoya , Uruga Tomoya , Ha Minh-Quyet , Dinh Duy-Tai , Dam Hieu-Chi , Tada Mizuki 
Published: November 2023

AbstractThe sulfidation of copper derived from copper-zinc alloy (brass) in sulfur-containing rubber, used for plating steel-cord-reinforced rubber tires, is suggested to be the key reaction for the adhesive behavior between brass and rubber in tires. However, the heterogeneous structures of rubber/brass interfaces have prevented us from understanding the sulfidation of metallic copper in brass and the formation of copper sulfides at the brass surface and buried rubber interface. Here, we visualize the 3D spatial location and chemical states of copper species in a rubber/brass composite during its aging process by 3D X-ray spectroimaging with X-ray absorption fine structure-computed tomography. Machine learning-derived reaction statistics of the 3D spectroimaging data reveal the reaction mechanism of copper sulfidation in the heterogeneous rubber/brass composite.