bluuebunny/crossref_metadata_2025_split huggingface dataset:
A squarate-pillared titanium oxide quantum sieve towards practical hydrogen isotope separation
Authors: Yan Qingqing , Wang Jing , Zhang Linda , Liu Jiaqi , Wahiduzzaman Mohammad , Yan Nana , Yu Liang , Dupuis Romain , Wang Hao , Maurin Guillaume , Hirscher Michael , Guo Peng , Wang Sujing , Du Jiangfeng 
Published: July 2023

AbstractSeparating deuterium from hydrogen isotope mixtures is of vital importance to develop nuclear energy industry, as well as other isotope-related advanced technologies. As one of the most promising alternatives to conventional techniques for deuterium purification, kinetic quantum sieving using porous materials has shown a great potential to address this challenging objective. From the knowledge gained in this field; it becomes clear that a quantum sieve encompassing a wide range of practical features in addition to its separation performance is highly demanded to approach the industrial level. Here, the rational design of an ultra-microporous squarate pillared titanium oxide hybrid framework has been achieved, of which we report the comprehensive assessment towards practical deuterium separation. The material not only displays a good performance combining high selectivity and volumetric uptake, reversible adsorption-desorption cycles, and facile regeneration in adsorptive sieving of deuterium, but also features a cost-effective green scalable synthesis using chemical feedstock, and a good stability (thermal, chemical, mechanical and radiolytic) under various working conditions. Our findings provide an overall assessment of the material for hydrogen isotope purification and the results represent a step forward towards next generation practical materials for quantum sieving of important gas isotopes.
Reprogramming of the transcriptome after heat stress mediates heat hormesis in Caenorhabditis elegans
Authors: Xu Fan , Li Ruoyao , von Gromoff Erika D. , Drepper Friedel , Knapp Bettina , Warscheid Bettina , Baumeister Ralf , Qi Wenjing 
Published: July 2023

AbstractTransient stress experiences not only trigger acute stress responses, but can also have long-lasting effects on cellular functions. InCaenorhabditis elegans, a brief exposure to heat shock during early adulthood extends lifespan and improves stress resistance, a phenomenon known as heat hormesis. Here, we investigated the prolonged effect of hormetic heat stress on the transcriptome of worms and found that the canonical heat shock response is followed by a profound transcriptional reprogramming in the post-stress period. This reprogramming relies on the endoribonuclease ENDU-2 but not the heat shock factor 1. ENDU-2 co-localizes with chromatin and interacts with RNA polymerase II, enabling specific regulation of transcription after the stress period. Failure to activate the post-stress response does not affect the resistance of animals to heat shock but eliminates the beneficial effects of hormetic heat stress. In summary, our work discovers that the RNA-binding protein ENDU-2 mediates the long-term impacts of transient heat stress via reprogramming transcriptome after stress exposure.
Structural journey of an insecticidal protein against western corn rootworm
Authors: Marini Guendalina , Poland Brad , Leininger Chris , Lukoyanova Natalya , Spielbauer Dan , Barry Jennifer K. , Altier Dan , Lum Amy , Scolaro Eric , Ortega Claudia Pérez , Yalpani Nasser , Sandahl Gary , Mabry Tim , Klever Jeffrey , Nowatzki Timothy , Zhao Jian-Zhou , Sethi Amit , Kassa Adane , Crane Virginia , Lu Albert L. , Nelson Mark E. , Eswar Narayanan , Topf Maya , Saibil Helen R. 
Published: July 2023

AbstractThe broad adoption of transgenic crops has revolutionized agriculture. However, resistance to insecticidal proteins by agricultural pests poses a continuous challenge to maintaining crop productivity and new proteins are urgently needed to replace those utilized for existing transgenic traits. We identified an insecticidal membrane attack complex/perforin (MACPF) protein, Mpf2Ba1, with strong activity against the devastating coleopteran pest western corn rootworm (WCR) and a novel site of action. Using an integrative structural biology approach, we determined monomeric, pre-pore and pore structures, revealing changes between structural states at high resolution. We discovered an assembly inhibition mechanism, a molecular switch that activates pre-pore oligomerization upon gut fluid incubation and solved the highest resolution MACPF pore structure to-date. Our findings demonstrate not only the utility of Mpf2Ba1 in the development of biotechnology solutions for protecting maize from WCR to promote food security, but also uncover previously unknown mechanistic principles of bacterial MACPF assembly.
Label-free adaptive optics single-molecule localization microscopy for whole zebrafish
Authors: Park Sanghyeon , Jo Yonghyeon , Kang Minsu , Hong Jin Hee , Ko Sangyoon , Kim Suhyun , Park Sangjun , Park Hae Chul , Shim Sang-Hee , Choi Wonshik 
Published: July 2023

AbstractSpecimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 μm with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to whole zebrafish that cause the loss of localization number owing to severe tissue aberrations.
Exon-intron boundary inhibits m6A deposition, enabling m6A distribution hallmark, longer mRNA half-life and flexible protein coding
Authors: Luo Zhiyuan , Ma Qilian , Sun Shan , Li Ningning , Wang Hongfeng , Ying Zheng , Ke Shengdong 
Published: July 2023

AbstractRegional bias of N6-methyladenosine (m6A) mRNA modification avoiding splice site region, calls for an open hypothesis whether exon-intron boundary could affect m6A deposition. By deep learning modeling, we find that exon-intron boundary represses a proportion (12% to 34%) of m6A deposition at adjacent exons (~100 nt to splice site). Experiments validate that m6A signal increases once the host gene does not undergo pre-mRNA splicing to produce the same mRNA. Inhibited m6A sites have higher m6A enhancers and lower m6A silencers locally and show high heterogeneity at different exons genome-widely, with only a small proportion (12% to 15%) of exons showing strong inhibition, enabling more stable mRNAs and flexible protein coding. m6A is majorly responsible for why mRNAs with more exons be more stable. Exon junction complex (EJC) only partially contributes to this exon-intron boundary m6A inhibition in some short internal exons, highlighting additional factors yet to be identified.
ACTL6A protects gastric cancer cells against ferroptosis through induction of glutathione synthesis
Authors: Yang Ziqing , Zou Shaomin , Zhang Yijing , Zhang Jieping , Zhang Peng , Xiao Lishi , Xie Yunling , Meng Manqi , Feng Junyan , Kang Liang , Lee Mong-Hong , Fang Lekun 
Published: July 2023

AbstractGastric cancer (GC), one of the most common malignant tumors in the world, exhibits a rapid metastasis rate and causes high mortality. Diagnostic markers and potential therapeutic targets for GCs are urgently needed. Here we show that Actin-like protein 6 A (ACTL6A), encoding an SWI/SNF subunit, is highly expressed in GCs. ACTL6A is found to be critical for regulating the glutathione (GSH) metabolism pathway because it upregulates γ-glutamyl-cysteine ligase catalytic subunit (GCLC) expression, thereby reducing reactive oxygen species (ROS) levels and inhibiting ferroptosis, a regulated form of cell death driven by the accumulation of lipid-based ROS. Mechanistic studies show that ACTL6A upregulates GCLC as a cotranscription factor with Nuclear factor (erythroid-derived 2)-like 2 (NRF2) and that the hydrophobic region of ACTL6A plays an important role. Our data highlight the oncogenic role of ACTL6A in GCs and indicate that inhibition of ACTL6A or GCLC could be a potential treatment strategy for GCs.
A unified field theory of topological defects and non-linear local excitations
Authors: Skogvoll Vidar , Rønning Jonas , Salvalaglio Marco , Angheluta Luiza 
Published: July 2023

AbstractTopological defects and smooth excitations determine the properties of systems showing collective order. We introduce a generic non-singular field theory that comprehensively describes defects and excitations in systems with O(n) broken rotational symmetry. Within this formalism, we explore fast events, such as defect nucleation/annihilation and dynamical phase transitions where the interplay between topological defects and non-linear excitations is particularly important. To highlight its versatility, we apply this formalism in the context of Bose-Einstein condensates, active nematics, and crystal lattices.
End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design
Authors: Liu Han , Liu Yuhan , Li Kevin , Zhao Zhangji , Schoenholz Samuel S. , Cubuk Ekin D. , Gupta Puneet , Bauchy Mathieu 
Published: July 2023

AbstractNumerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design.
Different pieces of the same puzzle: a multifaceted perspective on the complex biological basis of Parkinson’s disease
Authors: Müller-Nedebock Amica C. , Dekker Marieke C. J. , Farrer Matthew J. , Hattori Nobutaka , Lim Shen-Yang , Mellick George D. , Rektorová Irena , Salama Mohamed , Schuh Artur F. S. , Stoessl A. Jon , Sue Carolyn M. , Tan Ai Huey , Vidal Rene L. , Klein Christine , Bardien Soraya 
Published: July 2023

AbstractThe biological basis of the neurodegenerative movement disorder, Parkinson’s disease (PD), is still unclear despite it being ‘discovered’ over 200 years ago in Western Medicine. Based on current PD knowledge, there are widely varying theories as to its pathobiology. The aim of this article was to explore some of these different theories by summarizing the viewpoints of laboratory and clinician scientists in the PD field, on the biological basis of the disease. To achieve this aim, we posed this question to thirteen “PD experts” from six continents (for global representation) and collated their personal opinions into this article. The views were varied, ranging from toxin exposure as a PD trigger, to LRRK2 as a potential root cause, to toxic alpha-synuclein being the most important etiological contributor. Notably, there was also growing recognition that the definition of PD as a single disease should be reconsidered, perhaps each with its own unique pathobiology and treatment regimen.
Two distinct trajectories of clinical and neurodegeneration events in Parkinson’s disease
Authors: Zhou Cheng , Wang Linbo , Cheng Wei , Lv JinChao , Guan Xiaojun , Guo Tao , Wu Jingjing , Zhang Wei , Gao Ting , Liu Xiaocao , Bai Xueqin , Wu Haoting , Cao Zhengye , Gu Luyan , Chen Jingwen , Wen Jiaqi , Huang Peiyu , Xu Xiaojun , Zhang Baorong , Feng Jianfeng , Zhang Minming 
Published: July 2023

AbstractIncreasing evidence suggests that Parkinson’s disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique—Subtype and Stage Inference (SuStaIn) — to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.
A jeff = 1/2 Kitaev material on the triangular lattice: the case of NaRuO2
Authors: Razpopov Aleksandar , Kaib David A. S. , Backes Steffen , Balents Leon , Wilson Stephen D. , Ferrari Francesco , Riedl Kira , Valentí Roser 
Published: July 2023

AbstractMotivated by recent reports of a quantum-disordered ground state in the triangular lattice compound NaRuO2, we derive a jeff = 1/2 magnetic model for this system by means of first-principles calculations. The pseudospin Hamiltonian is dominated by bond-dependent off-diagonal Γ interactions, complemented by a ferromagnetic Heisenberg exchange and a notably antiferromagnetic Kitaev term. In addition to bilinear interactions, we find a sizable four-spin ring exchange contribution with a strongly anisotropic character, which has been so far overlooked when modeling Kitaev materials. The analysis of the magnetic model, based on the minimization of the classical energy and exact diagonalization of the quantum Hamiltonian, points toward the existence of a rather robust easy-plane ferromagnetic order, which cannot be easily destabilized by physically relevant perturbations.
Studying the genetics of participation using footprints left on the ascertained genotypes
Authors: Benonisdottir Stefania , Kong Augustine 
Published: July 2023

AbstractThe trait of participating in a genetic study probably has a genetic component. Identifying this component is difficult as we cannot compare genetic information of participants with nonparticipants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than nonparticipants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. In nonoverlapping samples, a polygenic score constructed from that analysis is significantly associated with educational attainment, body mass index and being invited to a dietary study. The estimated correlation between the genetic components underlying participation in UK Biobank and educational attainment is estimated to be 36.6%—substantial but far from total. Taking participation behaviour into account would improve the analyses of the study data, including those of health traits.
A multi-omics dataset of human transcriptome and proteome stable reference
Authors: Lu Shaohua , Lu Hong , Zheng Tingkai , Yuan Huiming , Du Hongli , Gao Youhe , Liu Yongtao , Pan Xuanzhen , Zhang Wenlu , Fu Shuying , Sun Zhenghua , Jin Jingjie , He Qing-Yu , Chen Yang , Zhang Gong 
Published: July 2023

AbstractThe development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.
Interface microstructure effects on dynamic failure behavior of layered Cu/Ta microstructures
Authors: Kumar Rajesh , Chen Jie , Mishra Avanish , Dongare Avinash M. 
Published: July 2023

AbstractStructural metallic materials with interfaces of immiscible materials provide opportunities to design and tailor the microstructures for desired mechanical behavior. Metallic microstructures with plasticity contributors of the FCC and BCC phases show significant promise for damage-tolerant applications due to their enhanced strengths and thermal stability. A fundamental understanding of the dynamic failure behavior is needed to design and tailor these microstructures with desired mechanical responses under extreme environments. This study uses molecular dynamics (MD) simulations to characterize plasticity contributors for various interface microstructures and the damage evolution behavior of FCC/BCC laminate microstructures. This study uses six model Cu/Ta interface systems with different orientation relationships that are as- created, and pre-deformed to understand the modifications in the plasticity contributions and the void nucleation/evolution behavior. The results suggest that pre-existing misfit dislocations and loading orientations (perpendicular to and parallel to the interface) affect the activation of primary and secondary slip systems. The dynamic strengths are observed to correlate with the energy of the interfaces, with the strengths being highest for low-energy interfaces and lowest for high-energy interfaces. However, the presence of pre-deformation of these interface microstructures affects not only the dynamic strength of the microstructures but also the correlation with interface energy.
Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis
Authors: Kim Ji Eun , Choi Yoon Ho , Lee Yeong Chan , Seong Gyeol , Song Joo Hye , Kim Tae Jun , Kim Eun Ran , Hong Sung Noh , Chang Dong Kyung , Kim Young-Ho , Shin Soo-Yong 
Published: July 2023

AbstractThe aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis. The deep learning model utilized in this study consisted of a convolutional neural network (CNN)-based encoder, with two auxiliary classifiers for the colon and rectum, as well as a final MES classifier that combined image features from both inputs. In the internal test, the model achieved an F1-score of 0.92, surpassing the performance of seven novice classifiers by an average margin of 0.11, and outperforming their consensus by 0.02. The area under the receiver operating characteristic curve (AUROC) was calculated to be 0.97 when considering MES 1 as positive, with an area under the precision-recall curve (AUPRC) of 0.98. In the external test using the Hyperkvasir dataset, the model achieved an F1-score of 0.89, AUROC of 0.86, and AUPRC of 0.97. The results demonstrate that the proposed CNN-based model, which integrates image features from both the colon and rectum, exhibits superior performance in accurately discriminating between MES 0 and MES 1 in patients with UC.
Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism
Authors: Tataru Christine , Peras Marie , Rutherford Erica , Dunlap Kaiti , Yin Xiaochen , Chrisman Brianna S. , DeSantis Todd Z. , Wall Dennis P. , Iwai Shoko , David Maude M. 
Published: July 2023

AbstractWhile healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed “cross-omic topics”, which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.