bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cyclin-dependent kinase inhibitor fadraciclib (CYC065) depletes anti-apoptotic protein and synergizes with venetoclax in primary chronic lymphocytic leukemia cells
Authors: Chen Rong , Chen Yuling , Xiong Ping , Zheleva Daniella , Blake David , Keating Michael J. , Wierda William G. , Plunkett William 
Published: April 2022

AbstractFadraciclib (CYC065) is a second-generation aminopurine CDK2/9 inhibitor with increased potency and selectivity toward CDK2 and CDK9 compared to seliciclib (R-roscovitine). In chronic lymphocytic leukemia (CLL), a disease that depends on the over-expression of anti-apoptotic proteins for its survival, inhibition of CDK9 by fadraciclib reduced phosphorylation of the C-terminal domain of RNA polymerase II and blocked transcription in vitro; these actions depleted the intrinsically short-lived anti-apoptotic protein Mcl-1 and induced apoptosis. While the simulated bone marrow and lymph node microenvironments induced Mcl-1 expression and protected CLL cells from apoptosis, these conditions did not prolong the turnover rate of Mcl-1, and fadraciclib efficiently abrogated the protective effect. Further, fadraciclib was synergistic with the Bcl-2 antagonist venetoclax, inducing more profound CLL cell death, especially in samples with 17p deletion. While fadraciclib, venetoclax, and the combination each had distinct kinetics of cell death induction, their activities were reversible, as no additional cell death was induced upon removal of the drugs. The best combination effects were achieved when both drugs were maintained together. Altogether, this study provides a rationale for the clinical development of fadraciclib in CLL, either alone or in combination with a Bcl-2 antagonist.
A potent human monoclonal antibody with pan-neutralizing activities directly dislocates S trimer of SARS-CoV-2 through binding both up and down forms of RBD
Authors: Wang Xiaofei , Hu Ao , Chen Xiangyu , Zhang Yixin , Yu Fei , Yue Shuai , Li Arong , Zhang Junsong , Pan Zhiwei , Yang Yang , Lin Yao , Gao Leiqiong , Zhou Jing , Zhao Jing , Li Fang , Shi Yaling , Huang Feng , Yang Xiaofan , Peng Yi , Tu Luoyang , Zhang Huan , Zheng Huanying , He Jun , Zhang Hui , Xu Lifan , Huang Qizhao , Zhu Yongqun , Deng Kai , Ye Lilin 
Published: April 2022

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of novel coronavirus disease (COVID-19). The neutralizing monoclonal antibodies (mAbs) targeting the receptor-binding domain (RBD) of SARS-CoV-2 are among the most promising strategies to prevent and treat COVID-19. However, SARS-CoV-2 variants of concern (VOCs) profoundly reduced the efficacies of most of mAbs and vaccines approved for clinical use. Herein, we demonstrated mAb 35B5 efficiently neutralizes both wild-type (WT) SARS-CoV-2 and VOCs, including B.1.617.2 (delta) variant, in vitro and in vivo. Cryo-electron microscopy (cryo-EM) revealed that 35B5 neutralizes SARS-CoV-2 by targeting a unique epitope that avoids the prevailing mutation sites on RBD identified in circulating VOCs, providing the molecular basis for its pan-neutralizing efficacy. The 35B5-binding epitope could also be exploited for the rational design of a universal SARS-CoV-2 vaccine.
Strain-specific transcriptional responses overshadow salinity effects in a marine diatom sampled along the Baltic Sea salinity cline
Authors: Pinseel Eveline , Nakov Teofil , Van den Berge Koen , Downey Kala M , Judy Kathryn J , Kourtchenko Olga , Kremp Anke , Ruck Elizabeth C , Sjöqvist Conny , Töpel Mats , Godhe Anna , Alverson Andrew J 
Published: April 2022

Abstract
The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of acclimated S. marinoi grown in a common garden. Our experiment included eight strains from source populations spanning the Baltic Sea salinity cline. Gene expression analysis revealed that low salinities induced changes in the cellular metabolism of S. marinoi, including upregulation of photosynthesis and storage compound biosynthesis, increased nutrient demand, and a complex response to oxidative stress. However, the strain effect overshadowed the salinity effect, as strains differed significantly in their response, both regarding the strength and the strategy (direction of gene expression) of their response. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond to environmental change.
The in-practice prevention programme: an example of flexible commissioning from Yorkshire and the Humber
Authors: Sandom Fiona , Hearnshaw Simon , Grant Siobhan , Williams Lynne , Brocklehurst Paul 
Published: April 2022

AbstractIntroduction An In-Practice Prevention (IPP) programme was developed by the Local Dental Network in the North Yorkshire and the Humber area in England in response to an oral health needs assessment. The underpinning logic model drew on a flexible commissioning approach and aimed to incentivise dental teams with NHS contracts to promote the delivery of prevention. This used care pathways that involved the whole dental team and was cost-neutral.Aim The programme was evaluated using realist methodology to identify 'what works, in which circumstances, how and for who?'.Design Realist evaluations are explanatory in nature and attempt to understand the factors that appear to influence the success (or not) of an intervention, rather than demonstrating causality.Methods and results Following a review of the pertinent literature, semi-structured interviews and focus groups, five theory areas were considered to be critical to the delivery of IPP. In order of stated priority, these were: 1) clinical leadership; 2) 'skill mix'; 3) financial incentives; 4) institutional logic/practice culture; and 5) behaviour change.Conclusion The results appear to show that clinically-led programmes could offer value to dental commissioners within a flexible commissioning model, although this would need to be further tested using an experiment design.
KLF5 inhibition overcomes oxaliplatin resistance in patient-derived colorectal cancer organoids by restoring apoptotic response
Authors: Shen Xiaohui , Zhang Yuchen , Xu Zhuoqing , Gao Han , Feng Wenqing , Li Wenchang , Miao Yiming , Xu Zifeng , Zong Yaping , Zhao Jingkun , Lu Aiguo 
Published: April 2022

AbstractOxaliplatin resistance is a major challenge in the treatment of colorectal cancer (CRC). Many molecular targeted drugs for refractory CRC have been developed to solve CRC drug resistance, but their effectiveness and roles in the progression of CRC and oxaliplatin resistance remain unclear. Here, we successfully constructed CRC PDOs and selected the Kruppel-like factor 5 (KLF5) inhibitor ML264 as the research object based on the results of the in vitro drug screening assay. ML264 significantly restored oxaliplatin sensitivity in CRC PDOs by restoring the apoptotic response, and this effect was achieved by inhibiting the KLF5/Bcl-2/caspase3 signaling pathway. Chromatin immunoprecipitation (ChIP) and luciferase reporter assays verified that KLF5 promoted the transcription of Bcl-2 in CRC cells. KLF5 inhibition also overcame oxaliplatin resistance in xenograft tumors. Taken together, our study demonstrated that ML264 can restore oxaliplatin sensitivity in CRC PDOs by restoring the apoptotic response. KLF5 may be a potential therapeutic target for oxaliplatin-resistant CRC. PDOs have a strong potential for evaluating inhibitors and drug combination therapy in a preclinical environment.
The lncRNA MIAT regulates CPT-1a mediated cardiac hypertrophy through m6A RNA methylation reading protein Ythdf2
Authors: Yang Yiqing , Mbikyo Muisha B. , Zhang Junzhe , Zhang Yuan , Zhang Naijin , Li Zhao 
Published: April 2022

AbstractsPathological cardiac hypertrophy is a key contributor in heart failure (HF). Long non-coding RNAs (lncRNAs) and N6-methyladenosine (m6A) modification play a vital role in cardiac hypertrophy respectively. Nevertheless, the interaction between lncRNA and m6A methylase in cardiac hypertrophy is scarcely reported. Here, we constructed a cardiac hypertrophy mouse model by transverse aortic constriction (TAC) surgery and H9c2 cell model by stimulating with AngII. We found that lncRNA MIAT mRNA level, and m6A RNA methylation reading protein Ythdf2 mRNA and protein levels, were significantly increased in the cardiac hypertrophy model both in vivo and vitro. MIAT or Ythdf2 overexpression aggravated cardiac hypertrophy, and vice versa. Through bioinformatics prediction, western blotting, FISH, RNA pull-down, and RIP, we found that MIAT bound to Ythdf2 and regulated its expression. Furthermore, we discovered that Ythdf2 function was a downstream of MIAT in cardiac hypertrophy. Finally, we found that MIAT was a necessary regulator of cardiac hypertrophy due to its regulation of the Ythdf2/PPARα/CPT-1a axis. This study indicated a new hypertrophic signaling pathway: MIAT/Ythdf2/PPARα/CPT-1a. The results provided a new understanding of the MIAT and m6A RNA methylation reading protein, Ythdf2, function and mechanism in cardiac hypertrophy and highlighted the potential therapeutic benefits in the heart.
Akt-GSK3β-mPTP pathway regulates the mitochondrial dysfunction contributing to odontoblasts apoptosis induced by glucose oxidative stress
Authors: Wu Danni , Yan Liya , Zheng Chuchu , Ren Xuekun , Pan Yihuai , Huang Shengbin , Pan Lijun , Li Zongli 
Published: April 2022

AbstractDiabetes Mellitus can cause dental pulp cells apoptosis by oxidative stress, and affect the integrity and function of dental pulp tissue. Mitochondria are the main attack targets of oxidative stress and have a critical role in apoptosis. However, whether mitochondria are involved in dental pulp damage caused by diabetes mellitus remains unclear. This study aimed to investigate the role of mitochondria in the apoptosis of odontoblast-like cell line (mDPC6T) induced by glucose oxidative stress, and to explore its possible mechanism. We established an oxidative stress model in vitro using glucose oxidase/glucose to simulate the pathological state under diabetic conditions. We found that the opening of mitochondrial permeability transition pore (mPTP) contributed to the apoptosis of mDPC6T treated with glucose oxidase, as evidenced by enhanced mitochondrial reactive oxygen species (mtROS) and intracellular Ca2+ disorder, significantly reduced mitochondrial membrane potential (MMP) and ATP production. Antioxidant N-acetylcysteine (NAC) or Cyclosporine A (mPTP inhibitor) blocked the mPTP opening, which significantly attenuated mitochondrial dysfunction and apoptosis induced by glucose oxidative stress. In addition, we found that glucose oxidative stress stimulated mPTP opening may through inhibition of Akt-GSK3β pathway. This study provides a new insight into the mitochondrial mechanism underlying diabetes-associated odontoblast-like cell apoptosis, laying a foundation for the prevention and treatment of diabetes-associated pulp injury.
DUB3/KLF4 combats tumor growth and chemoresistance in hepatocellular carcinoma
Authors: Jia Xuebing , Li Lei , Wang Fan , Xue Ying , Wu Tongde , Jia Qianqian , Li Yunhui , Wu Chenming , Chen Yuping , Wu Jinhuan , Su Yang , Wang Xinshu , Zhuang Tao , Dong Xiao , Ling Jing , Yuan Jian , Li Qi 
Published: April 2022

AbstractThis study aimed to investigate the role of deubiquitinating enzyme 3 (DUB3) in the regulation of Krüppel-like factor 4 (KLF4) expression in hepatocellular carcinoma (HCC). Gain- and loss-of-function assay, luciferase reporter assay, co-immunoprecipitation, and intracellular and extracellular deubiquitination assays were conducted in vitro. A tumor xenograft mouse model was established. The expression of DUB3 and KLF4 was examined in HCC patient specimens. The results showed that DUB3 upregulated KLF4 expression by deubiquitinating and stabilizing KLF4 protein in HCC cells through binding with KLF4. DUB3 inhibited HCC cell proliferation in vitro and tumor growth in vivo while enhancing the chemosensitivity of HCC cells in a KLF4-dependent manner. Furthermore, KLF4 promoted DUB3 transcription by binding to the DUB3 promoter. In HCC patients, DUB3 expression positively correlated with KLF4 expression in HCC tissues. Low DUB3 expression predicted worse overall survival and recurrence in HCC patients. In conclusion, this study revealed a positive DUB3/KLF4 feedback loop that inhibits tumor growth and chemoresistance in HCC. These results suggest that DUB3/KLF4 activation might be a potential therapeutic approach for HCC treatment.
A metafluid with multistable density and internal energy states
Authors: Peretz Ofek , Ben Abu Ezra , Zigelman Anna , Givli Sefi , Gat Amir D. 
Published: April 2022

AbstractInvestigating and tailoring the thermodynamic properties of different fluids is crucial to many fields. For example, the efficiency, operation range, and environmental safety of applications in energy and refrigeration cycles are highly affected by the properties of the respective available fluids. Here, we suggest combining gas, liquid and multistable elastic capsules to create an artificial fluid with a multitude of stable states. We study, theoretically and experimentally, the suspension’s internal energy, equilibrium pressure-density relations, and their stability for both adiabatic and isothermal processes. We show that the elastic multistability of the capsules endows the fluid with multistable thermodynamic properties, including the ability of capturing and storing energy at standard atmospheric conditions, not found in naturally available fluids.
Atomic-scale visualization of chiral charge density wave superlattices and their reversible switching
Authors: Song Xuan , Liu Liwei , Chen Yaoyao , Yang Han , Huang Zeping , Hou Baofei , Hou Yanhui , Han Xu , Yang Huixia , Zhang Quanzhen , Zhang Teng , Zhou Jiadong , Huang Yuan , Zhang Yu , Gao Hong-Jun , Wang Yeliang 
Published: April 2022

AbstractChirality is essential for various phenomena in life and matter. However, chirality and its switching in electronic superlattices, such as charge density wave (CDW) superlattices, remain elusive. In this study, we characterize the chirality switching with atom-resolution imaging in a single-layer NbSe2 CDW superlattice by the technique of scanning tunneling microscopy. The atomic arrangement of the CDW superlattice is found continuous and intact although its chirality is switched. Several intermediate states are tracked by time-resolved imaging, revealing the fast and dynamic chirality transition. Importantly, the switching is reversibly realized with an external electric field. Our findings unveil the delicate switching process of chiral CDW superlattice in a two-dimensional (2D) crystal down to the atomic scale.
Recent advances and applications of deep learning methods in materials science
Authors: Choudhary Kamal , DeCost Brian , Chen Chi , Jain Anubhav , Tavazza Francesca , Cohn Ryan , Park Cheol Woo , Choudhary Alok , Agrawal Ankit , Billinge Simon J. L. , Holm Elizabeth , Ong Shyue Ping , Wolverton Chris 
Published: April 2022

AbstractDeep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.
Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes
Authors: Guevarra Dan , Zhou Lan , Richter Matthias H. , Shinde Aniketa , Chen Di , Gomes Carla P. , Gregoire John M. 
Published: April 2022

AbstractProperties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO4-like phase with both Cu2V2O7-like and CuV2O6-like phases, creating avenues for understanding complex photoelectrocatalysts.
fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data
Authors: Zhao Ruiyang , Yaman Burhaneddin , Zhang Yuxin , Stewart Russell , Dixon Austin , Knoll Florian , Huang Zhengnan , Lui Yvonne W. , Hansen Michael S. , Lungren Matthew P. 
Published: April 2022

AbstractImproving speed and image quality of Magnetic Resonance Imaging (MRI) using deep learning reconstruction is an active area of research. The fastMRI dataset contains large volumes of raw MRI data, which has enabled significant advances in this field. While the impact of the fastMRI dataset is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks and exploring important questions regarding rendering of specific pathology using such novel approaches. This work introduces fastMRI+, which consists of 16154 subspecialist expert bounding box annotations and 13 study-level labels for 22 different pathology categories on the fastMRI knee dataset, and 7570 subspecialist expert bounding box annotations and 643 study-level labels for 30 different pathology categories for the fastMRI brain dataset. The fastMRI+ dataset is open access and aims to support further research and advancement of medical imaging in MRI reconstruction and beyond.
A three-year dataset supporting research on building energy management and occupancy analytics
Authors: Luo Na , Wang Zhe , Blum David , Weyandt Christopher , Bourassa Norman , Piette Mary Ann , Hong Tianzhen 
Published: April 2022

AbstractThis paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m2) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications—building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls—to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.
Influence of wind and light on the floating and sinking process of Microcystis
Authors: Xue Zongpu , Zhu Wei , Zhu Yuyang , Fan Xihui , Chen Huaimin , Feng Ganyu 
Published: April 2022

AbstractThe vertical migration and accumulation of Microcystis colonies is a critical process in algal bloom formation. This work explored the effect of wind and light intensity on the vertical migration of Microcystis colonies. The wind-driven currents, light-driven changes in mass density of colonies, and the effect of colony size was coupled to simulate the vertical motion of colonies via Ansys Fluent and MATLAB. Results showed that light causes Microcystis to exhibit a ‘day-sinking and night-floating’ (d-n) phenomenon, however, wind weakens the phenomenon by forming a turbulent drag force that inhibits the vertical movement of Microcystis. This study proposed a kinetic ratio-based method, that there is a specific equilibrium turbulent kinetic energy and when turbulent kinetic energy of the water body is greater than the equilibrium turbulent kinetic energy, the d-n phenomenon does not occur. For Lake Taihu, the wind-driven turbulent kinetic energy is usually greater than the equilibrium turbulent kinetic energy. Therefore, Microcystis colonies may not exhibit the d-n phenomenon. Our findings provide a new theoretical basis for current process-based models in simulating algal blooms in large shallow lakes.
Short-term effects of different PM2.5 ranges on daily all-cause mortality in Jinan, China
Authors: Ma Zhixiang , Meng Xiangwei , Chen Cai , Chao Baoting , Zhang Chuanzhen , Li Wei 
Published: April 2022

AbstractTo examine the effects of different PM2.5 concentration ranges on daily all-cause mortality, 8768 all-cause deaths were recorded in the database of the Shandong Provincial Hospital Affiliated to Shandong First Medical University. Data of air pollutants (PM2.5 and O3) concentration were provided by the Jinan Environment Monitoring Center. The relative risk of all-cause mortality was assessed using a quasi-Poisson regression model after adjusting for confounding factors. The concentrations of PM2.5 were divided into four ranges 0–35 μg/m3; 35–75 μg/m3; 75–115 μg/m3; 115–150 μg/m3. There was no significant relationship between PM2.5 exposure and all-cause deaths in individuals aged < 60 years. However, for individuals aged ≥ 60 years, there was a significant positive association between exposure concentrations and all-cause deaths within the ranges 0–35 μg/m3, 35–75 μg/m3, and 115–150 μg/m3 with a mortality increase of 1.07 (1.01, 1.13), 1.03 (1.00, 1.05), and 1.05 (1.01, 1.08), respectively. When the population aged ≥ 60 years was stratified into gender groups, exposure to PM2.5 in the range 0–35 μg/m3 increased the mortality risk in men but not women. All-cause mortality in women, but not men, increased significantly with exposure to PM2.5 in the ranges of 35–75, 75–115, and 115–150 μg/m3.