bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combination treatment of T1-44, a PRMT5 inhibitor with Vactosertib, an inhibitor of TGF-β signaling, inhibits invasion and prolongs survival in a mouse model of pancreatic tumors
Authors: Hong Eunji , Barczak Wojciech , Park Sujin , Heo Jin Sun , Ooshima Akira , Munro Shonagh , Hong Chang Pyo , Park Jinah , An Haein , Park Joon Oh , Park Seok Hee , La Thangue Nick B. , Kim Seong-Jin 
Published: February 2023

AbstractPancreatic ductal adenocarcinoma (PDAC) is the most lethal type of cancer and the third leading cause of cancer death with the lowest 5-year survival rate. Heterogeneity, difficulty in diagnosis, and rapid metastatic progression are the causes of high mortality in pancreatic cancer. Recent studies have shown that Protein arginine methyltransferase 5 (PRMT5) is overexpressed in pancreatic cancers, and these patients have a worse prognosis. Recently, PRMT5 as an anti-cancer target has gained considerable interest. In this study, we investigated whether inhibition of PRMT5 activity was synergistic with blockade of TGF-β1 signaling, which plays an important role in the construction of the desmoplastic matrix in pancreatic cancer and induces therapeutic vulnerability. Compared with T1-44, a selective inhibitor of PRMT5 activity, the combination of T1-44 with the TGF-β1 signaling inhibitor Vactosertib significantly reduced tumor size and surrounding tissue invasion and significantly improved long-term survival. RNA sequencing analysis of mouse tumors revealed that the combination of T1-44 and Vactosertib significantly altered the expression of genes involved in cancer progression, such as cell migration, extracellular matrix, and apoptotic processes. In particular, the expression of Btg2, known as a tumor suppressor factor in various cancers, was markedly induced by combination treatment. Ectopic overexpression of Btg2 inhibited the EMT response, blocking cell migration, and promoted cancer cell death. These data demonstrate that the combination therapy of T1-44 with Vactosertib is synergistic for pancreatic cancer, suggesting that this novel combination therapy has value in the treatment strategy of patients with pancreatic cancer.
Unique DUOX2+ACE2+ small cholangiocytes are pathogenic targets for primary biliary cholangitis
Authors: Li Xi , Li Yan , Xiao Jintao , Wang Huiwen , Guo Yan , Mao Xiuru , Shi Pan , Hou Yanliang , Zhang Xiaoxun , Zhao Nan , Zheng Minghua , He Yonghong , Ding Jingjing , Tan Ya , Liao Min , Li Ling , Peng Ying , Li Xuan , Pan Qiong , Xie Qiaoling , Li Qiao , Li Jianwei , Li Ying , Chen Zhe , Huang Yongxiu , Assis David N. , Cai Shi-Ying , Boyer James L. , Huang Xuequan , Tang Can-E , Liu Xiaowei , Peng Shifang , Chai Jin 
Published: February 2023

AbstractCholangiocytes play a crucial role in bile formation. Cholangiocyte injury causes cholestasis, including primary biliary cholangitis (PBC). However, the etiology of PBC remains unclear despite being characterized as an autoimmune disease. Using single-cell RNA sequencing (scRNA-seq), fluorescence-activated-cell-sorting, multiplex immunofluorescence (IF) and RNAscope analyses, we identified unique DUOX2+ACE2+ small cholangiocytes in human and mouse livers. Their selective decrease in PBC patients was associated with the severity of disease. Moreover, proteomics, scRNA-seq, and qPCR analyses indicated that polymeric immunoglobulin receptor (pIgR) was highly expressed in DUOX2+ACE2+ cholangiocytes. Serum anti-pIgR autoantibody levels were significantly increased in PBC patients, regardless of positive and negative AMA-M2. Spatial transcriptomics and multiplex IF revealed that CD27+ memory B and plasma cells accumulated in the hepatic portal tracts of PBC patients. Collectively, DUOX2+ACE2+ small cholangiocytes are pathogenic targets in PBC, and preservation of DUOX2+ACE2+ cholangiocytes and targeting anti-pIgR autoantibodies may be valuable strategies for therapeutic interventions in PBC.
High entropy liquid electrolytes for lithium batteries
Authors: Wang Qidi , Zhao Chenglong , Wang Jianlin , Yao Zhenpeng , Wang Shuwei , Kumar Sai Govind Hari , Ganapathy Swapna , Eustace Stephen , Bai Xuedong , Li Baohua , Wagemaker Marnix 
Published: February 2023

AbstractHigh-entropy alloys/compounds have large configurational entropy by introducing multiple components, showing improved functional properties that exceed those of conventional materials. However, how increasing entropy impacts the thermodynamic/kinetic properties in liquids that are ambiguous. Here we show this strategy in liquid electrolytes for rechargeable lithium batteries, demonstrating the substantial impact of raising the entropy of electrolytes by introducing multiple salts. Unlike all liquid electrolytes so far reported, the participation of several anionic groups in this electrolyte induces a larger diversity in solvation structures, unexpectedly decreasing solvation strengths between lithium ions and solvents/anions, facilitating lithium-ion diffusivity and the formation of stable interphase passivation layers. In comparison to the single-salt electrolytes, a low-concentration dimethyl ether electrolyte with four salts shows an enhanced cycling stability and rate capability. These findings, rationalized by the fundamental relationship between entropy-dominated solvation structures and ion transport, bring forward high-entropy electrolytes as a composition-rich and unexplored space for lithium batteries and beyond.
ZBTB12 is a molecular barrier to dedifferentiation in human pluripotent stem cells
Authors: Han Dasol , Liu Guojing , Oh Yujeong , Oh Seyoun , Yang Seungbok , Mandjikian Lori , Rani Neha , Almeida Maria C. , Kosik Kenneth S. , Jang Jiwon 
Published: February 2023

AbstractDevelopment is generally viewed as one-way traffic of cell state transition from primitive to developmentally advanced states. However, molecular mechanisms that ensure the unidirectional transition of cell fates remain largely unknown. Through exact transcription start site mapping, we report an evolutionarily conserved BTB domain-containing zinc finger protein, ZBTB12, as a molecular barrier for dedifferentiation of human pluripotent stem cells (hPSCs). Single-cell RNA sequencing reveals that ZBTB12 is essential for three germ layer differentiation by blocking hPSC dedifferentiation. Mechanistically, ZBTB12 fine-tunes the expression of human endogenous retrovirus H (HERVH), a primate-specific retrotransposon, and targets specific transcripts that utilize HERVH as a regulatory element. In particular, the downregulation of HERVH-overlapping long non-coding RNAs (lncRNAs) by ZBTB12 is necessary for a successful exit from a pluripotent state and lineage derivation. Overall, we identify ZBTB12 as a molecular barrier that safeguards the unidirectional transition of metastable stem cell fates toward developmentally advanced states.
Astrocytic lactate dehydrogenase A regulates neuronal excitability and depressive-like behaviors through lactate homeostasis in mice
Authors: Yao Shan , Xu Min-Dong , Wang Ying , Zhao Shen-Ting , Wang Jin , Chen Gui-Fu , Chen Wen-Bing , Liu Jian , Huang Guo-Bin , Sun Wen-Juan , Zhang Yan-Yan , Hou Huan-Li , Li Lei , Sun Xiang-Dong 
Published: February 2023

AbstractAlterations in energy metabolism are associated with depression. However, the role of glycolysis in the pathogenesis of depression and the underlying molecular mechanisms remain unexplored. Through an unbiased proteomic screen coupled with biochemical verifications, we show that the levels of glycolysis and lactate dehydrogenase A (LDHA), a glycolytic enzyme that catalyzes L-lactate production, are reduced in the dorsomedial prefrontal cortex (dmPFC) of stress-susceptible mice in chronic social defeat stress (CSDS) model. Conditional knockout of LDHA from the brain promotes depressive-like behaviors in both male and female mice, accompanied with reduced L-lactate levels and decreased neuronal excitability in the dmPFC. Moreover, these phenotypes could be duplicated by knockdown of LDHA in the dmPFC or specifically in astrocytes. In contrast, overexpression of LDHA reverses these phenotypic changes in CSDS-susceptible mice. Mechanistic studies demonstrate that L-lactate promotes neuronal excitability through monocarboxylic acid transporter 2 (MCT2) and by inhibiting large-conductance Ca2+-activated potassium (BK) channel. Together, these results reveal a role of LDHA in maintaining neuronal excitability to prevent depressive-like behaviors.
Promoting water dissociation for efficient solar driven CO2 electroreduction via improving hydroxyl adsorption
Authors: Chen Xin , Chen Junxiang , Chen Huayu , Zhang Qiqi , Li Jiaxuan , Cui Jiwei , Sun Yanhui , Wang Defa , Ye Jinhua , Liu Lequan 
Published: February 2023

AbstractExploring efficient electrocatalysts with fundamental understanding of the reaction mechanism is imperative in CO2electroreduction. However, the impact of sluggish water dissociation as proton source and the surface species in reaction are still unclear. Herein, we report a strategy of promoting protonation in CO2electroreduction by implementing oxygen vacancy engineering on Bi2O2CO3over which high Faradaic efficiency of formate (above 90%) and large partial current density (162 mA cm−2) are achieved. Systematic study reveals that the production rate of formate is mainly hampered by water dissociation, while the introduction of oxygen vacancy accelerates water dissociation kinetics by strengthening hydroxyl adsorption and reduces the energetic span of CO2electroreduction. Moreover, CO3* involved in formate formation as the key surface species is clearly identified by electron spin resonance measurements and designed in situ Raman spectroscopy study combined with isotopic labelling. Coupled with photovoltaic device, the solar to formate energy conversion efficiency reaches as high as 13.3%.
Planthopper salivary sheath protein LsSP1 contributes to manipulation of rice plant defenses
Authors: Huang Hai-Jian , Wang Yi-Zhe , Li Li-Li , Lu Hai-Bin , Lu Jia-Bao , Wang Xin , Ye Zhuang-Xin , Zhang Ze-Long , He Yu-Juan , Lu Gang , Zhuo Ji-Chong , Mao Qian-Zhuo , Sun Zong-Tao , Chen Jian-Ping , Li Jun-Min , Zhang Chuan-Xi 
Published: February 2023

AbstractSalivary elicitors secreted by herbivorous insects can be perceived by host plants to trigger plant immunity. However, how insects secrete other salivary components to subsequently attenuate the elicitor-induced plant immunity remains poorly understood. Here, we study the small brown planthopper, Laodelphax striatellus salivary sheath protein LsSP1. Using Y2H, BiFC and LUC assays, we show that LsSP1 is secreted into host plants and binds to salivary sheath via mucin-like protein (LsMLP). Rice plants pre-infested with dsLsSP1-treated L. striatellus are less attractive to L. striatellus nymphs than those pre-infected with dsGFP-treated controls. Transgenic rice plants with LsSP1 overexpression rescue the insect feeding defects caused by a deficiency of LsSP1 secretion, consistent with the potential role of LsSP1 in manipulating plant defenses. Our results illustrate the importance of salivary sheath proteins in mediating the interactions between plants and herbivorous insects.
Variation of lightning-ignited wildfire patterns under climate change
Authors: Pérez-Invernón Francisco J. , Gordillo-Vázquez Francisco J. , Huntrieser Heidi , Jöckel Patrick 
Published: February 2023

AbstractLightning is the main precursor of natural wildfires and Long-Continuing-Current (LCC) lightning flashes are proposed to be the main igniters of lightning-ignited wildfires (LIW). Previous studies predict a change of the global occurrence rate and spatial pattern of total lightning. Nevertheless, the sensitivity of lightning-ignited wildfire occurrence to climate change is uncertain. Here, we investigate space-based measurements of LCC lightning associated with lightning ignitions and present LCC lightning projections under the Representative Concentration Pathway RCP6.0 for the 2090s by applying a recent LCC lightning parameterization based on the updraft strength in thunderstorms. We find a 41% global increase of the LCC lightning flash rate. Increases are largest in South America, the western coast of North America, Central America, Australia, Southern and Eastern Asia, and Europe, while only regional variations are found in northern polar forests, where fire risk can affect permafrost soil carbon release. These results show that lightning schemes including LCC lightning are needed to project the occurrence of lightning-ignited wildfires under climate change.
Quantum face recognition protocol with ghost imaging
Authors: Salari Vahid , Paneru Dilip , Saglamyurek Erhan , Ghadimi Milad , Abdar Moloud , Rezaee Mohammadreza , Aslani Mehdi , Barzanjeh Shabir , Karimi Ebrahim 
Published: February 2023

AbstractFace recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an extensive database. Quantum algorithms have been shown to improve the efficiency and speed of many computational tasks, and as such, they could also potentially improve the complexity of the face recognition process. Here, we propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis, and quantum independent component analysis. A novel quantum algorithm for finding dissimilarity in the faces based on the computation of trace and determinant of a matrix (image) is also proposed. The overall complexity of our pattern recognition algorithm is $$O(N\,\log N)$$

O
(
N

log
N
)

—N is the image dimension. As an input to these pattern recognition algorithms, we consider experimental images obtained from quantum imaging techniques with correlated photons, e.g. “interaction-free” imaging or “ghost” imaging. Interfacing these imaging techniques with our quantum pattern recognition processor provides input images that possess a better signal-to-noise ratio, lower exposures, and higher resolution, thus speeding up the machine learning process further. Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system with potential applications extending beyond face recognition, e.g., in medical imaging for diagnosing sensitive tissues or biology for protein identification.
Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network
Authors: Jang Miso , Bae Hyun-jin , Kim Minjee , Park Seo Young , Son A-yeon , Choi Se Jin , Choe Jooae , Choi Hye Young , Hwang Hye Jeon , Noh Han Na , Seo Joon Beom , Lee Sang Min , Kim Namkug 
Published: February 2023

AbstractThe generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training.
Presenting rose odor during learning, sleep and retrieval helps to improve memory consolidation: a real-life study
Authors: Knötzele Jessica , Riemann Dieter , Frase Lukas , Feige Bernd , van Elst Ludger Tebartz , Kornmeier Jürgen 
Published: February 2023

AbstractImproving our learning abilities is important for numerous aspects of our life. Several studies found beneficial effects of presenting cues (odor or sounds) during learning and during sleep for memory performance. A recent study applying a real-life paradigm indicated that additional odor cueing during a Final Test can further increase this cueing effect. The present online study builds on these findings with the following questions: (1) Can we replicate beneficial memory effects of additional odor cueing during tests? (2) How many odor cueing learning sessions and odor cueing nights of sleep maximize the learning success? (3) Can odor cueing also reduce the amount of forgetting over time? 160 Participants learned 40 German Japanese word pairs in four groups with separate experimental conditions over three days. Group N received no odor during the whole study. Group LS received odor cueing during learning and sleep, group LT during learning and testing and group LST during learning, sleep and testing. Participants performed intermediate tests after each learning session plus three final tests 1, 7 and 28 days after the last learning session. Results: (1) Group LST learned 8.5% more vocabulary words than the other groups overall. (2) This odor cueing effect increased across the three days of cued learning. (3) We found no clear evidence for effects of odor cueing on the forgetting dynamics. Our findings support the notion of a beneficial effect of odor cueing. They further suggest to use at least 3 days and nights of odor cueing. Overall, this study indicates that there is an easy, efficient and economical way to enhance memory performance in daily life.
Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features
Authors: Barough Siavash Shirzadeh , Safavi-Naini Seyed Amir Ahmad , Siavoshi Fatemeh , Tamimi Atena , Ilkhani Saba , Akbari Setareh , Ezzati Sadaf , Hatamabadi Hamidreza , Pourhoseingholi Mohamad Amin 
Published: February 2023

AbstractWe aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.
Metal-induced oxidative stress and human plasma protein oxidation after SARS-CoV-2 infection
Authors: Aryal Baikuntha , Tillotson Joseph , Ok Kiwon , Stoltzfus Andrew T. , Michel Sarah L. J. , Rao V. Ashutosh 
Published: February 2023

AbstractPathogenesis of COVID-19 by SARS-CoV-2 resulted in a global pandemic and public health emergency in 2020. Viral infection can induce oxidative stress through reactive oxygen species (ROS). Inflammation and environmental stress are major sources of oxidative stress after infection. Micronutrients such as iron, copper, zinc, and manganese play various roles in human tissues and their imbalance in blood can impact immune responses against pathogens including SARS CoV-2. We hypothesized that alteration of free metal ions during infection and metal-catalyzed oxidation plays a critical role towards pathogenesis after infection. We analyzed convalescent and hospitalized COVID-19 patient plasma using orthogonal analytical techniques to determine redox active metal concentrations, overall protein oxidation, oxidative modifications, and protein levels via proteomics to understand the consequences of metal-induced oxidative stress in COVID-19 plasma proteins. Metal analysis using ICP-MS showed significantly greater concentrations of copper in COVID-19 plasma compared to healthy controls. We demonstrate significantly greater total protein carbonylation, other oxidative modifications, and deamidation of plasma proteins in COVID-19 plasma compared to healthy controls. Proteomics analysis showed that levels of redox active proteins including hemoglobulin were elevated in COVID-19 plasma. Molecular modeling concurred with potential interactions between iron binding proteins and SARS CoV-2 surface proteins. Overall, increased levels of redox active metals and protein oxidation indicate that oxidative stress-induced protein oxidation in COVID-19 may be a consequence of the interactions of SARS-CoV-2 proteins with host cell metal binding proteins resulting in altered cellular homeostasis.
The applicability of species sensitivity distributions to the development of generic doses for phytosanitary irradiation
Authors: Penca Cory , Beam Andrea L. , Bailey Woodward D. 
Published: February 2023

AbstractIonizing radiation is used as a phytosanitary treatment to prevent the introduction of pests through trade. Generic doses are a valuable means to increase the number of pest-commodity combinations that can be treated using phytosanitary irradiation. Generic doses allow for the treatment of the entire taxa for which the dose has been approved, allowing for the treatment of untested species. As such, the approval of a generic dose requires substantial supporting data and careful consideration of the risks involved. We adopt the Species Sensitivity Distribution (SSD) framework, already in widespread use in the field of ecotoxicology and environmental risk assessment, to evaluate generic doses for phytosanitary irradiation treatments. Parametric SSDs for Curculionidae and Tephritidae were developed using existing data on efficacious phytosanitary irradiation treatments. The resulting SSDs provided estimates of the taxa coverage expected by the generic dose, along with the margin of uncertainty. The SSD analysis lends support to the existing 150 Gy generic dose for Tephritidae and a proposed 175 Gy generic dose for Curculionidae. The quantitative estimates of risk produced by the SSD approach can be a valuable tool for phytosanitary rule making, improving the process for generic dose development and approval.