bluuebunny/crossref_metadata_2025_split huggingface dataset:
Operando monitoring of dendrite formation in lithium metal batteries via ultrasensitive tilted fiber Bragg grating sensors
Authors: Han Xile , Zhong Hai , Li Kaiwei , Xue Xiaobin , Wu Wen , Hu Nan , Lu Xihong , Huang Jiaqiang , Xiao Gaozhi , Mai Yaohua , Guo Tuan 
Published: January 2024

AbstractLithium (Li) dendrite growth significantly deteriorates the performance and shortens the operation life of lithium metal batteries. Capturing the intricate dynamics of surface localized and rapid mass transport at the electrolyte–electrode interface of lithium metal is essential for the understanding of the dendrite growth process, and the evaluation of the solutions mitigating the dendrite growth issue. Here we demonstrate an approach based on an ultrasensitive tilted fiber Bragg grating (TFBG) sensor which is inserted close to the electrode surface in a working lithium metal battery, without disturbing its operation. Thanks to the superfine optical resonances of the TFBG, in situ and rapid monitoring of mass transport kinetics and lithium dendrite growth at the nanoscale interface of lithium anodes have been achieved. Reliable correlations between the performance of different natural/artificial solid electrolyte interphases (SEIs) and the time-resolved optical responses have been observed and quantified, enabling us to link the nanoscale ion and SEI behavior with the macroscopic battery performance. This new operando tool will provide additional capabilities for parametrization of the batteries’ electrochemistry and help identify the optimal interphases of lithium metal batteries to enhance battery performance and its safety.
A compact multi-pixel superconducting nanowire single-photon detector array supporting gigabit space-to-ground communications
Authors: Hao Hao , Zhao Qing-Yuan , Huang Yang-Hui , Deng Jie , Yang Fan , Ru Sai-Ying , Liu Zhen , Wan Chao , Liu Hao , Li Zhi-Jian , Wang Hua-Bing , Tu Xue-Cou , Zhang La-Bao , Jia Xiao-Qing , Wu Xing-Long , Chen Jian , Kang Lin , Wu Pei-Heng 
Published: January 2024

AbstractClassical and quantum space-to-ground communications necessitate highly sensitive receivers capable of extracting information from modulated photons to extend the communication distance from near-earth orbits to deep space explorations. To achieve gigabit data rates while mitigating strong background noise photons and beam drift in a highly attenuated free-space channel, a comprehensive design of a multi-functional detector is indispensable. In this study, we present an innovative compact multi-pixel superconducting nanowire single-photon detector array that integrates near-unity detection efficiency (91.6%), high photon counting rate (1.61 Gcps), large dynamic range for resolving different photon numbers (1–24), and four-quadrant position sensing function all within one device. Furthermore, we have constructed a communication testbed to validate the advantages offered by such an architecture. Through 8-PPM (pulse position modulation) format communication experiments, we have achieved an impressive maximum data rate of 1.5 Gbps, demonstrating sensitivities surpassing previous benchmarks at respective speeds. By incorporating photon number information into error correction codes, the receiver can tolerate maximum background noise levels equivalent to 0.8 photons/slot at a data rate of 120 Mbps—showcasing a great potential for daylight operation scenarios. Additionally, preliminary beam tracking tests were conducted through open-loop scanning techniques, which revealed clear quantitative dependence indicating sensitivity variations based on beam location. Based on the device characterizations and communication results, we anticipate that this device architecture, along with its corresponding signal processing and coding techniques, will be applicable in future space-to-ground communication tasks.
Hydroxynorketamine, but not ketamine, acts via α7 nicotinic acetylcholine receptor to control presynaptic function and gene expression
Authors: Guhathakurta Debarpan , Petrušková Aneta , Akdaş Enes Yağız , Perelló-Amorós Bartomeu , Frischknecht Renato , Anni Daniela , Weiss Eva-Maria , Walter Martin , Fejtová Anna 
Published: January 2024

AbstractKetamine is clinically used fast-acting antidepressant. Its metabolite hydroxynorketamine (HNK) shows a robust antidepressant effect in animal studies. It is unclear, how these chemically distinct compounds converge on similar neuronal effects. While KET acts mostly as N-methyl-d-aspartate receptor (NMDAR) antagonist, the molecular target of HNK remains enigmatic. Here, we show that KET and HNK converge on rapid inhibition of glutamate release by reducing the release competence of synaptic vesicles and induce nuclear translocation of pCREB that controls expression of neuroplasticity genes connected to KET- and HNK-mediated antidepressant action. Ro25-6981, a selective antagonist of GluN2B, mimics effect of KET indicating that GluN2B-containing NMDAR might mediate the presynaptic effect of KET. Selective antagonist of α7 nicotinic acetylcholine receptors (α7nAChRs) or genetic deletion of Chrna7, its pore-forming subunit, fully abolishes HNK-induced synaptic and nuclear regulations, but leaves KET-dependent cellular effects unaffected. Thus, KET or HNK-induced modulation of synaptic transmission and nuclear translocation of pCREB can be mediated by selective signaling via NMDAR or α7nAChRs, respectively. Due to the rapid metabolism of KET to HNK, it is conceivable that subsequent modulation of glutamatergic and cholinergic neurotransmission affects circuits in a cell-type-specific manner and contributes to the therapeutic potency of KET. This finding promotes further exploration of new combined medications for mood disorders.
Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator
Authors: Wong Ting Yat , Luo Hao , Tang Jennifer , Moore Tyler M. , Gur Ruben C. , Suen Yi Nam , Hui Christy Lai Ming , Lee Edwin Ho Ming , Chang Wing Chung , Yan Wai Ching , Chui Eileena , Poon Lap Tak , Lo Alison , Cheung Koi-Man , Kan Chui Kwan , Chen Eric Yu Hai , Chan Sherry Kit Wa 
Published: January 2024

AbstractAbout 15–40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.
RBL2 represses the transcriptional activity of Multicilin to inhibit multiciliogenesis
Authors: Quiroz Erik J. , Kim Seongjae , Gautam Lalit K. , Borok Zea , Kintner Christopher , Ryan Amy L. 
Published: January 2024

AbstractA core pathophysiologic feature underlying many respiratory diseases is multiciliated cell dysfunction, leading to inadequate mucociliary clearance. Due to the prevalence and highly variable etiology of mucociliary dysfunction in respiratory diseases, it is critical to understand the mechanisms controlling multiciliogenesis that may be targeted to restore functional mucociliary clearance. Multicilin, in a complex with E2F4, is necessary and sufficient to drive multiciliogenesis in airway epithelia, however this does not apply to all cell types, nor does it occur evenly across all cells in the same cell population. In this study we further investigated how co-factors regulate the ability of Multicilin to drive multiciliogenesis. Combining data in mouse embryonic fibroblasts and human bronchial epithelial cells, we identify RBL2 as a repressor of the transcriptional activity of Multicilin. Knockdown of RBL2 in submerged cultures or phosphorylation of RBL2 in response to apical air exposure, in the presence of Multicilin, allows multiciliogenesis to progress. These data demonstrate a dynamic interaction between RBL2 and Multicilin that regulates the capacity of cells to differentiate and multiciliate. Identification of this mechanism has important implications for facilitating MCC differentiation in diseases with impaired mucociliary clearance.
Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory
Authors: Wu Xiangjin , Khan Asir Intisar , Lee Hengyuan , Hsu Chen-Feng , Zhang Huairuo , Yu Heshan , Roy Neel , Davydov Albert V. , Takeuchi Ichiro , Bao Xinyu , Wong H.-S. Philip , Pop Eric 
Published: January 2024

AbstractData-centric applications are pushing the limits of energy-efficiency in today’s computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge4Sb6Te7), to achieve record-low power density ≈ 5 MW/cm2 and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also simultaneously exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 108 cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge4Sb6Te7 nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing.
Quantifying coastal freshwater extremes during unprecedented rainfall using long timeseries multi-platform salinity observations
Authors: Malan Neil , Roughan Moninya , Hemming Michael , Ingleton Tim 
Published: January 2024

AbstractDuring 2022, extreme rainfall occurred across southeast Australia, making it the wettest year on record. The oceanic impact of extreme rainfall events in normally ‘dry’ regions is not well understood, as their effects are challenging to observe. Here, we use unique multi-platform timeseries and spatial data from 36 autonomous ocean glider missions over 13 years, and we define an extreme salinity threshold inshore of the East Australian Current. We show that the freshwater plume extended fivefold further than previously thought. The compound effect of multiple large rainfall events resulted in a newly observed stratification (‘double-stacking’) dynamic, with the stratification being largely controlled by salinity. Extreme salinity events are known to be important for species composition of local fisheries as well as detrimental for coastal water quality. Such events and their impacts may become more common as extreme rainfall events are projected to become more frequent in a changing climate. Hence, comprehensive observing strategies facilitating identification of salinity extremes are essential.
MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer
Authors: Liao Hanqing , Barra Carolina , Zhou Zhicheng , Peng Xu , Woodhouse Isaac , Tailor Arun , Parker Robert , Carré Alexia , Borrow Persephone , Hogan Michael J. , Paes Wayne , Eisenlohr Laurence C. , Mallone Roberto , Nielsen Morten , Ternette Nicola 
Published: January 2024

AbstractUnderstanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.
The UBP5 histone H2A deubiquitinase counteracts PRCs-mediated repression to regulate Arabidopsis development
Authors: Godwin James , Govindasamy Mohan , Nedounsejian Kiruba , March Eduardo , Halton Ronan , Bourbousse Clara , Wolff Léa , Fort Antoine , Krzyszton Michal , López Corrales Jesús , Swiezewski Szymon , Barneche Fredy , Schubert Daniel , Farrona Sara 
Published: January 2024

AbstractPolycomb Repressive Complexes (PRCs) control gene expression through the incorporation of H2Aub and H3K27me3. In recent years, there is increasing evidence of the complexity of PRCs’ interaction networks and the interplay of these interactors with PRCs in epigenome reshaping, which is fundamental to understand gene regulatory mechanisms. Here, we identified UBIQUITIN SPECIFIC PROTEASE 5 (UBP5) as a chromatin player able to counteract the deposition of the two PRCs’ epigenetic hallmarks in Arabidopsis thaliana. We demonstrated that UBP5 is a plant developmental regulator based on functional analyses of ubp5-CRISPR Cas9 mutant plants. UBP5 promotes H2A monoubiquitination erasure, leading to transcriptional de-repression. Furthermore, preferential association of UBP5 at PRC2 recruiting motifs and local H3K27me3 gaining in ubp5 mutant plants suggest the existence of functional interplays between UBP5 and PRC2 in regulating epigenome dynamics. In summary, acting as an antagonist of the pivotal epigenetic repressive marks H2Aub and H3K27me3, UBP5 provides novel insights to disentangle the complex regulation of PRCs’ activities.
LOX-1 acts as an N6-methyladenosine-regulated receptor for Helicobacter pylori by binding to the bacterial catalase
Authors: Zeng Judeng , Xie Chuan , Huang Ziheng , Cho Chi H. , Chan Hung , Li Qing , Ashktorab Hassan , Smoot Duane T. , Wong Sunny H. , Yu Jun , Gong Wei , Liang Cong , Xu Hongzhi , Chen Huarong , Liu Xiaodong , Wu Justin C. Y. , Ip Margaret , Gin Tony , Zhang Lin , Chan Matthew T. V. , Hu Wei , Wu William K. K. 
Published: January 2024

AbstractThe role of N6-methyladenosine (m6A) modification of host mRNA during bacterial infection is unclear. Here, we show that Helicobacter pylori infection upregulates host m6A methylases and increases m6A levels in gastric epithelial cells. Reducing m6A methylase activity via hemizygotic deletion of methylase-encoding gene Mettl3 in mice, or via small interfering RNAs targeting m6A methylases, enhances H. pylori colonization. We identify LOX-1 mRNA as a key m6A-regulated target during H. pylori infection. m6A modification destabilizes LOX-1 mRNA and reduces LOX-1 protein levels. LOX-1 acts as a membrane receptor for H. pylori catalase and contributes to bacterial adhesion. Pharmacological inhibition of LOX-1, or genetic ablation of Lox-1, reduces H. pylori colonization. Moreover, deletion of the bacterial catalase gene decreases adhesion of H. pylori to human gastric sections. Our results indicate that m6A modification of host LOX-1 mRNA contributes to protection against H. pylori infection by downregulating LOX-1 and thus reducing H. pylori adhesion.
Connectome-based reservoir computing with the conn2res toolbox
Authors: Suárez Laura E. , Mihalik Agoston , Milisav Filip , Marshall Kenji , Li Mingze , Vértes Petra E. , Lajoie Guillaume , Misic Bratislav 
Published: January 2024

AbstractThe connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present : an open-source Python toolbox for implementing biological neural networks as artificial neural networks. is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.
Self-similar chiral organic molecular cages
Authors: Wang Zhen , Zhang Qing-Pu , Guo Fei , Ma Hui , Liang Zi-Hui , Yi Chang-Hai , Zhang Chun , Chen Chuan-Feng 
Published: January 2024

AbstractThe endeavor to enhance utility of organic molecular cages involves the evolution of them into higher-level chiral superstructures with self-similar, presenting a meaningful yet challenging. In this work, 2D tri-bladed propeller-shaped triphenylbenzene serves as building blocks to synthesize a racemic 3D tri-bladed propeller-shaped helical molecular cage. This cage, in turn, acts as a building block for a pair of higher-level 3D tri-bladed chiral helical molecular cages, featuring multilayer sandwich structures and displaying elegant characteristics with self-similarity in discrete superstructures at different levels. The evolutionary procession of higher-level cages reveals intramolecular self-shielding effects and exclusive chiral narcissistic self-sorting behaviors. Enantiomers higher-level cages can be interconverted by introducing an excess of corresponding chiral cyclohexanediamine. In the solid state, higher-level cages self-assemble into supramolecular architectures of L-helical or D-helical nanofibers, achieving the scale transformation of chiral characteristics from chiral atoms to microscopic and then to mesoscopic levels.
Self-renewing human naïve pluripotent stem cells dedifferentiate in 3D culture and form blastoids spontaneously
Authors: Guo Mingyue , Wu Jinyi , Chen Chuanxin , Wang Xinggu , Gong An , Guan Wei , Karvas Rowan M. , Wang Kexin , Min Mingwei , Wang Yixuan , Theunissen Thorold W. , Gao Shaorong , Silva José C. R. 
Published: January 2024

AbstractHuman naïve pluripotent stem cells (hnPSCs) can generate integrated models of blastocysts termed blastoids upon switch to inductive medium. However, the underlying mechanisms remain obscure. Here we report that self-renewing hnPSCs spontaneously and efficiently give rise to blastoids upon three dimensional (3D) suspension culture. The spontaneous blastoids mimic early stage human blastocysts in terms of structure, size, and transcriptome characteristics and are capable of progressing to post-implantation stages. This property is conferred by the glycogen synthase kinase-3 (GSK3) signalling inhibitor IM-12 present in 5iLAF self-renewing medium. IM-12 upregulates oxidative phosphorylation-associated genes that underly the capacity of hnPSCs to generate blastoids spontaneously. Starting from day one of self-organization, hnPSCs at the boundary of all 3D aggregates dedifferentiate into E5 embryo-like intermediates. Intermediates co-express SOX2/OCT4 and GATA6 and by day 3 specify trophoblast fate, which coincides with cavity and blastoid formation. In summary, spontaneous blastoid formation results from 3D culture triggering dedifferentiation of hnPSCs into earlier embryo-like intermediates which are then competent to segregate blastocyst fates.
Towards an increasingly biased view on Arctic change
Authors: López-Blanco Efrén , Topp-Jørgensen Elmer , Christensen Torben R. , Rasch Morten , Skov Henrik , Arndal Marie F. , Bret-Harte M. Syndonia , Callaghan Terry V. , Schmidt Niels M. 
Published: January 2024

AbstractThe Russian invasion of Ukraine hampers the ability to adequately describe conditions across the Arctic, thus biasing the view on Arctic change. Here we benchmark the pan-Arctic representativeness of the largest high-latitude research station network, INTERACT, with or without Russian stations. Excluding Russian stations lowers representativeness markedly, with some biases being of the same magnitude as the expected shifts caused by climate change by the end of the century.
A database of computed Raman spectra of inorganic compounds with accurate hybrid functionals
Authors: Li Yuheng , Lee Damien K. J. , Cai Pengfei , Zhang Ziyi , Gorai Prashun , Canepa Pieremanuele 
Published: January 2024

AbstractRaman spectroscopy is widely applied in identifying local structures in materials, but the interpretation of Raman spectra is non-trivial. An accurate computational database of reference spectra calculated with a consistent level of theory can significantly aid in interpreting measured Raman spectra. Here, we present a database of Raman spectra of inorganic compounds calculated with accurate hybrid functionals in density functional theory. Raman spectra were obtained by calculating dynamical matrices and polarizability tensors for structures from the Inorganic Crystal Structure Database. The calculated Raman spectra and other phonon properties (e.g., infrared spectra) are stored in a MongoDB database publicly shared through a web application. We assess the accuracy of our Raman calculations by statistically comparing ~80 calculated spectra with an existing experimental Raman database. To date, the database contains 161 compounds and is continuously growing as we add more materials computed with our automated workflow.