bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tolerance of repeated toxic injuries of murine livers is associated with steatosis and inflammation
Authors: Hammad Seddik , Ogris Christoph , Othman Amnah , Erdoesi Pia , Schmidt-Heck Wolfgang , Biermayer Ina , Helm Barbara , Gao Yan , Piorońska Weronika , Holland Christian H. , D’Alessandro Lorenza A. , de la Torre Carolina , Sticht Carsten , Al Aoua Sherin , Theis Fabian J. , Bantel Heike , Ebert Matthias P. , Klingmüller Ursula , Hengstler Jan G. , Dooley Steven , Mueller Nikola S. 
Published: July 2023

AbstractThe human liver has a remarkable capacity to regenerate and thus compensate over decades for fibrosis caused by toxic chemicals, drugs, alcohol, or malnutrition. To date, no protective mechanisms have been identified that help the liver tolerate these repeated injuries. In this study, we revealed dysregulation of lipid metabolism and mild inflammation as protective mechanisms by studying longitudinal multi-omic measurements of liver fibrosis induced by repeated CCl4injections in mice (n = 45). Based on comprehensive proteomics, transcriptomics, blood- and tissue-level profiling, we uncovered three phases of early disease development—initiation, progression, and tolerance. Using novel multi-omic network analysis, we identified multi-level mechanisms that are significantly dysregulated in the injury-tolerant response. Public data analysis shows that these profiles are altered in human liver diseases, including fibrosis and early cirrhosis stages. Our findings mark the beginning of the tolerance phase as the critical switching point in liver response to repetitive toxic doses. After fostering extracellular matrix accumulation as an acute response, we observe a deposition of tiny lipid droplets in hepatocytes only in the Tolerant phase. Our comprehensive study shows that lipid metabolism and mild inflammation may serve as biomarkers and are putative functional requirements to resist further disease progression.
Robust negative longitudinal magnetoresistance and spin–orbit torque in sputtered Pt3Sn and Pt3SnxFe1-x topological semimetal
Authors: Zhang Delin , Jiang Wei , Yun Hwanhui , Benally Onri Jay , Peterson Thomas , Cresswell Zach , Fan Yihong , Lv Yang , Yu Guichuan , Barriocanal Javier Garcia , Swatek Przemyslaw Wojciech , Mkhoyan K. Andre , Low Tony , Wang Jian-Ping 
Published: July 2023

AbstractContrary to topological insulators, topological semimetals possess a nontrivial chiral anomaly that leads to negative magnetoresistance and are hosts to both conductive bulk states and topological surface states with intriguing transport properties for spintronics. Here, we fabricate highly-ordered metallic Pt3Sn and Pt3SnxFe1-x thin films via sputtering technology. Systematic angular dependence (both in-plane and out-of-plane) study of magnetoresistance presents surprisingly robust quadratic and linear negative longitudinal magnetoresistance features for Pt3Sn and Pt3SnxFe1-x, respectively. We attribute the anomalous negative longitudinal magnetoresistance to the type-II Dirac semimetal phase (pristine Pt3Sn) and/or the formation of tunable Weyl semimetal phases through symmetry breaking processes, such as magnetic-atom doping, as confirmed by first-principles calculations. Furthermore, Pt3Sn and Pt3SnxFe1-x show the promising performance for facilitating the development of advanced spin-orbit torque devices. These results extend our understanding of chiral anomaly of topological semimetals and can pave the way for exploring novel topological materials for spintronic devices.
Multi-batch single-cell comparative atlas construction by deep learning disentanglement
Authors: Lynch Allen W. , Brown Myles , Meyer Clifford A. 
Published: July 2023

AbstractCell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL’s capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data.
External globus pallidus input to the dorsal striatum regulates habitual seeking behavior in male mice
Authors: Baker Matthew , Kang Seungwoo , Hong Sa-Ik , Song Minryung , Yang Minsu Abel , Peyton Lee , Essa Hesham , Lee Sang Wan , Choi Doo-Sup 
Published: July 2023

AbstractThe external globus pallidus (GPe) coordinates action-selection through GABAergic projections throughout the basal ganglia. GPe arkypallidal (arky) neurons project exclusively to the dorsal striatum, which regulates goal-directed and habitual seeking. However, the role of GPe arky neurons in reward-seeking remains unknown. Here, we identified that a majority of arky neurons target the dorsolateral striatum (DLS). Using fiber photometry, we found that arky activities were higher during random interval (RI; habit) compared to random ratio (RR; goal) operant conditioning. Support vector machine analysis demonstrated that arky neuron activities have sufficient information to distinguish between RR and RI behavior. Genetic ablation of this arkyGPe→DLS circuit facilitated a shift from goal-directed to habitual behavior. Conversely, chemogenetic activation globally reduced seeking behaviors, which was blocked by systemic D1R agonism. Our findings reveal a role of this arkyGPe→DLS circuit in constraining habitual seeking in male mice, which is relevant to addictive behaviors and other compulsive disorders.
Structural and photophysical characterization of the small ultra-red fluorescent protein
Authors: Maiti Atanu , Buffalo Cosmo Z. , Saurabh Saumya , Montecinos-Franjola Felipe , Hachey Justin S. , Conlon William J. , Tran Geraldine N. , Hassan Bakar , Walters Kylie J. , Drobizhev Mikhail , Moerner W. E. , Ghosh Partho , Matsuo Hiroshi , Tsien Roger Y. , Lin John Y. , Rodriguez Erik A. 
Published: July 2023

AbstractThe small Ultra-Red Fluorescent Protein (smURFP) represents a new class of fluorescent protein with exceptional photostability and brightness derived from allophycocyanin in a previous directed evolution. Here, we report the smURFP crystal structure to better understand properties and enable further engineering of improved variants. We compare this structure to the structures of allophycocyanin and smURFP mutants to identify the structural origins of the molecular brightness. We then use a structure-guided approach to develop monomeric smURFP variants that fluoresce with phycocyanobilin but not biliverdin. Furthermore, we measure smURFP photophysical properties necessary for advanced imaging modalities, such as those relevant for two-photon, fluorescence lifetime, and single-molecule imaging. We observe that smURFP has the largest two-photon cross-section measured for a fluorescent protein, and that it produces more photons than organic dyes. Altogether, this study expands our understanding of the smURFP, which will inform future engineering toward optimal FPs compatible with whole organism studies.
Kondo interaction in FeTe and its potential role in the magnetic order
Authors: Kim Younsik , Kim Min-Seok , Kim Dongwook , Kim Minjae , Kim Minsoo , Cheng Cheng-Maw , Choi Joonyoung , Jung Saegyeol , Lu Donghui , Kim Jong Hyuk , Cho Soohyun , Song Dongjoon , Oh Dongjin , Yu Li , Choi Young Jai , Kim Hyeong-Do , Han Jung Hoon , Jo Younjung , Shim Ji Hoon , Seo Jungpil , Huh Soonsang , Kim Changyoung 
Published: July 2023

AbstractFinding d-electron heavy fermion states has been an important topic as the diversity in d-electron materials can lead to many exotic Kondo effect-related phenomena or new states of matter such as correlation-driven topological Kondo insulator. Yet, obtaining direct spectroscopic evidence for a d-electron heavy fermion system has been elusive to date. Here, we report the observation of Kondo lattice behavior in an antiferromagnetic metal, FeTe, via angle-resolved photoemission spectroscopy, scanning tunneling spectroscopy and transport property measurements. The Kondo lattice behavior is represented by the emergence of a sharp quasiparticle and Fano-type tunneling spectra at low temperatures. The transport property measurements confirm the low-temperature Fermi liquid behavior and reveal successive coherent-incoherent crossover upon increasing temperature. We interpret the Kondo lattice behavior as a result of hybridization between localized Fe 3dxy and itinerant Te 5pz orbitals. Our observations strongly suggest unusual cooperation between Kondo lattice behavior and long-range magnetic order.
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
Authors: Kroll Alexander , Rousset Yvan , Hu Xiao-Pan , Liebrand Nina A. , Lercher Martin J. 
Published: July 2023

AbstractThe turnover number kcat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental kcat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through differential reaction fingerprints and by representing enzymes through a modified and re-trained Transformer Network model for protein sequences. TurNuP outperforms previous models and generalizes well even to enzymes that are not similar to proteins in the training set. Parameterizing metabolic models with TurNuP-predicted kcat values leads to improved proteome allocation predictions. To provide a powerful and convenient tool for the study of molecular biochemistry and physiology, we implemented a TurNuP web server.
Membranes prime the RapGEF EPAC1 to transduce cAMP signaling
Authors: Sartre Candice , Peurois François , Ley Marie , Kryszke Marie-Hélène , Zhang Wenhua , Courilleau Delphine , Fischmeister Rodolphe , Ambroise Yves , Zeghouf Mahel , Cianferani Sarah , Ferrandez Yann , Cherfils Jacqueline 
Published: July 2023

AbstractEPAC1, a cAMP-activated GEF for Rap GTPases, is a major transducer of cAMP signaling and a therapeutic target in cardiac diseases. The recent discovery that cAMP is compartmentalized in membrane-proximal nanodomains challenged the current model of EPAC1 activation in the cytosol. Here, we discover that anionic membranes are a major component of EPAC1 activation. We find that anionic membranes activate EPAC1 independently of cAMP, increase its affinity for cAMP by two orders of magnitude, and synergize with cAMP to yield maximal GEF activity. In the cell cytosol, where cAMP concentration is low, EPAC1 must thus be primed by membranes to bind cAMP. Examination of the cell-active chemical CE3F4 in this framework further reveals that it targets only fully activated EPAC1. Together, our findings reformulate previous concepts of cAMP signaling through EPAC proteins, with important implications for drug discovery.
Immune sensing of food allergens promotes avoidance behaviour
Authors: Florsheim Esther B. , Bachtel Nathaniel D. , Cullen Jaime L. , Lima Bruna G. C. , Godazgar Mahdieh , Carvalho Fernando , Chatain Carolina P. , Zimmer Marcelo R. , Zhang Cuiling , Gautier Gregory , Launay Pierre , Wang Andrew , Dietrich Marcelo O. , Medzhitov Ruslan 
Published: July 2023

AbstractIn addition to its canonical function of protection from pathogens, the immune system can also alter behaviour1,2. The scope and mechanisms of behavioural modifications by the immune system are not yet well understood. Here, using mouse models of food allergy, we show that allergic sensitization drives antigen-specific avoidance behaviour. Allergen ingestion activates brain areas involved in the response to aversive stimuli, including the nucleus of tractus solitarius, parabrachial nucleus and central amygdala. Allergen avoidance requires immunoglobulin E (IgE) antibodies and mast cells but precedes the development of gut allergic inflammation. The ability of allergen-specific IgE and mast cells to promote avoidance requires cysteinyl leukotrienes and growth and differentiation factor 15. Finally, a comparison of C57BL/6 and BALB/c mouse strains revealed a strong effect of the genetic background on the avoidance behaviour. These findings thus point to antigen-specific behavioural modifications that probably evolved to promote niche selection to avoid unfavourable environments.
High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
Authors: Rettie Fasil M. , Gayler Sebastian , Weber Tobias K. D. , Tesfaye Kindie , Streck Thilo 
Published: July 2023

AbstractHigh-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized thebias correction constructed analogues with quantile mapping reordering(BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using aperfect siblingapproach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.
A global database for modeling tumor-immune cell communication
Authors: Xie Yunjin , Zhou Weiwei , Shi Jingyi , Xu Mengjia , Lin Zijing , Li Donghao , Li Jianing , Cheng Shujun , Shao Tingting , Xu Juan 
Published: July 2023

AbstractCommunications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.
Patient-derived glioblastoma cell lines with conserved genome profiles of the original tissue
Authors: Kim Soon-Chan , Cho Young-Eun , Shin Young-Kyoung , Yu Hyeon Jong , Chowdhury Tamrin , Kim Sojin , Yi Kyung Sik , Choi Chi-Hoon , Cha Sang-Hoon , Park Chul-Kee , Ku Ja-Lok 
Published: July 2023

AbstractGlioblastoma (GBM) is the most lethal intracranial tumor. Sequencing technologies have supported personalized therapy for precise diagnosis and optimal treatment of GBM by revealing clinically actionable molecular characteristics. Although accumulating sequence data from brain tumors and matched normal tissues have facilitated a comprehensive understanding of genomic features of GBM, these in silico evaluations could gain more biological credibility when they are verified with in vitro and in vivo models. From this perspective, GBM cell lines with whole exome sequencing (WES) datasets of matched tumor tissues and normal blood are suitable biological platforms to not only investigate molecular markers of GBM but also validate the applicability of druggable targets. Here, we provide a complete WES dataset of 26 GBM patient-derived cell lines along with their matched tumor tissues and blood to demonstrate that cell lines can mostly recapitulate genomic profiles of original tumors such as mutational signatures and copy number alterations.
The speed and temporal frequency of visual apparent motion modulate auditory duration perception
Authors: He Xiang , Ke Zijun , Wu Zehua , Chen Lihan , Yue Zhenzhu 
Published: July 2023

AbstractIn the present study, we investigated how the perception of auditory duration could be modulated by a task-irrelevant, concurrent visual apparent motion, induced by visual bars alternating between left and right sides. Moreover, we examined the influence of the speed and temporal frequency of visual apparent motion on the perception of auditory duration. In each trial, the standard visual stimuli (two vertical bars) were presented sequentially, except that visual apparent motion was included in the fourth stimulus. A tone was presented simultaneously with each visual stimulus, while the fourth tone was presented with varied duration. Participants judged whether the fourth tone lasted longer than the other four tones. In Experiment 1, the speed of visual apparent motion (Fast vs. Slow) was manipulated by changing the interval between two bars. The mean point of subjective equality (PSE) in the Slow apparent motion condition was larger than that in the Static condition. Moreover, participants tended to overestimate the duration only in the Static condition, i.e., time dilation effect, which disappeared under apparent motion conditions. In Experiment 2, in addition to speed, we controlled the temporal frequency of apparent motion by manipulating the number of bars, generating four conditions of visual apparent motion (Physical-fast, Perceived-fast, Perceived-slow, vs. Static). The mean PSE was significantly smaller in the Physical-fast condition than in the Static and Perceived-slow conditions. Moreover, we found a time compression effect in both the Perceived-slow and Static conditions but not in the Perceived-fast and Physical-fast conditions. These results suggest that the auditory duration could be modulated by the concurrent, contextual visual apparent motion, and both the speed and temporal frequency of the task-irrelevant visual apparent motion contribute to the bias in perceiving the auditory duration.
Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan
Authors: Berganzo-Besga Iban , Orengo Hector A. , Lumbreras Felipe , Alam Aftab , Campbell Rosie , Gerrits Petrus J. , de Souza Jonas Gregorio , Khan Afifa , Suárez-Moreno María , Tomaney Jack , Roberts Rebecca C. , Petrie Cameron A. 
Published: July 2023

AbstractThis paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.
Forecasting the progression of human civilization on the Kardashev Scale through 2060 with a machine learning approach
Authors: Zhang Antong , Yang Jiani , Luo Yangcheng , Fan Siteng 
Published: July 2023

AbstractEnergy has been propelling the development of human civilization for millennia. Humanity presently stands at Type 0.7276 on the Kardashev Scale, which was proposed to quantify the relationship between energy consumption and the development of civilizations. However, current predictions of human civilization remain underdeveloped and energy consumption models are oversimplified. In order to improve the precision of the prediction, we use machine learning models random forest and autoregressive integrated moving average to simulate and predict energy consumption on a global scale and the position of humanity on the Kardashev Scale through 2060. The result suggests that global energy consumption is expected to reach ~ 887 EJ in 2060, and humanity will become a Type 0.7449 civilization. Additionally, the potential energy segmentation changes before 2060 and the influence of the advent of nuclear fusion are discussed. We conclude that if energy strategies and technologies remain in the present course, it may take human civilization millennia to become a Type 1 civilization. The machine learning tool we develop significantly improves the previous projection of the Kardashev Scale, which is critical in the context of civilization development.