bluuebunny/crossref_metadata_2025_split huggingface dataset:
A randomized, double-blind, active placebo-controlled study of efficacy, safety, and durability of repeated vs single subanesthetic ketamine for treatment-resistant depression
Authors: Shiroma Paulo R. , Thuras Paul , Wels Joseph , Albott C. Sophia , Erbes Christopher , Tye Susannah , Lim Kelvin O. 
Published: June 2020

AbstractThe strategy of repeated ketamine in open-label and saline-control studies of treatment-resistant depression suggested greater antidepressant response beyond a single ketamine. However, consensus guideline stated the lack of evidence to support frequent ketamine administration. We compared the efficacy and safety of single vs. six repeated ketamine using midazolam as active placebo. Subjects received either six ketamine or five midazolam followed by a single ketamine during 12 days followed by up to 6-month post-treatment period. The primary end point was the change from baseline in the Montgomery-Åsberg Depression Rating Scale (MADRS) score at 24 h after the last infusion. Fifty-four subjects completed all six infusions. For the primary outcome measure, there was no significant difference in change of MADRS scores between six ketamine group and single ketamine group at 24 h post-last infusion. Repeated ketamine showed greater antidepressant efficacy compared to midazolam after five infusions before receiving single ketamine infusion. Remission and response favored the six ketamine after infusion 4 and 5, respectively, compared to midazolam before receiving single ketamine infusion. For those who responded, the median time-to-relapse was nominally but not statistically different (2 and 6 weeks for the single and six ketamine group, respectively). Repeated infusions were relatively well-tolerated. Repeated ketamine showed greater antidepressant efficacy to midazolam after five infusions but fell short of significance when compared to add-on single ketamine to midazolam at the end of 2 weeks. Increasing knowledge on the mechanism of ketamine should drive future studies on the optimal balance of dosing ketamine for maximum antidepressant efficacy with minimum exposure.
Tumour budding and its clinical implications in gastrointestinal cancers
Authors: Zlobec Inti , Berger Martin D. , Lugli Alessandro 
Published: June 2020

AbstractTumour budding in colorectal cancer has become an important prognostic factor. Represented by single cells or small tumour cell clusters at the invasion front of the tumour mass, these tumour buds seem to reflect cells in a ‘hybrid’ state of epithelial–mesenchymal transition, and evidence indicates that the presence of these entities is associated with lymph node metastasis, local recurrence and distant metastatic disease. The International Tumour Budding Consensus Conference (ITBCC) has highlighted a scoring system for the reporting of tumour budding in colorectal cancer, as well as different clinical scenarios that could affect patient management. Other organs are not spared: tumour budding has been described in numerous gastrointestinal and non-gastrointestinal cancers. Here, we give an update on ITBCC validation studies in the context of colorectal cancer and the clinical implications of tumour budding throughout the upper gastrointestinal and pancreatico-biliary tract.
Electromagnetic character of the competitive γγ/γ-decay from 137mBa
Authors: Söderström P.-A. , Capponi L. , Açıksöz E. , Otsuka T. , Tsoneva N. , Tsunoda Y. , Balabanski D. L. , Pietralla N. , Guardo G. L. , Lattuada D. , Lenske H. , Matei C. , Nichita D. , Pappalardo A. , Petruse T. 
Published: June 2020

AbstractSecond-order processes in physics is a research topic focusing attention from several fields worldwide including, for example, non-linear quantum electrodynamics with high-power lasers, neutrinoless double-β decay, and stimulated atomic two-photon transitions. For the electromagnetic nuclear interaction, the observation of the competitive double-γ decay from 137mBa has opened up the nuclear structure field for detailed investigation of second-order processes through the manifestation of off-diagonal nuclear polarisability. Here, we confirm this observation with an 8.7σ significance, and an improved value on the double-photon versus single-photon branching ratio as 2.62 × 10−6(30). Our results, however, contradict the conclusions from the original experiment, where the decay was interpreted to be dominated by a quadrupole-quadrupole component. Here, we find a substantial enhancement in the energy distribution consistent with a dominating octupole-dipole character and a rather small quadrupole-quadrupole component in the decay, hindered due to an evolution of the internal nuclear structure. The implied strongly hindered double-photon branching in 137mBa opens up the possibility of the double-photon branching as a feasible tool for nuclear-structure studies on off-diagonal polarisability in nuclei where this hindrance is not present.
Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning
Authors: Freitas Rodrigo , Reed Evan J. 
Published: June 2020

AbstractThe process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence.
Epigenetic regulation of spurious transcription initiation in Arabidopsis
Authors: Le Ngoc Tu , Harukawa Yoshiko , Miura Saori , Boer Damian , Kawabe Akira , Saze Hidetoshi 
Published: June 2020

AbstractIn plants, epigenetic regulation is critical for silencing transposons and maintaining proper gene expression. However, its impact on the genome-wide transcription initiation landscape remains elusive. By conducting a genome-wide analysis of transcription start sites (TSSs) using cap analysis of gene expression (CAGE) sequencing, we show that thousands of TSSs are exclusively activated in various epigenetic mutants of Arabidopsis thaliana and referred to as cryptic TSSs. Many have not been identified in previous studies, of which up to 65% are contributed by transposons. They possess similar genetic features to regular TSSs and their activation is strongly associated with the ectopic recruitment of RNAPII machinery. The activation of cryptic TSSs significantly alters transcription of nearby TSSs, including those of genes important for development and stress responses. Our study, therefore, sheds light on the role of epigenetic regulation in maintaining proper gene functions in plants by suppressing transcription from cryptic TSSs.
Rationally designed ruthenium complexes for 1- and 2-photon photodynamic therapy
Authors: Karges Johannes , Kuang Shi , Maschietto Federica , Blacque Olivier , Ciofini Ilaria , Chao Hui , Gasser Gilles 
Published: June 2020

AbstractThe use of photodynamic therapy (PDT) against cancer has received increasing attention over recent years. However, the application of the currently approved photosensitizers (PSs) is limited by their poor aqueous solubility, aggregation, photobleaching and slow clearance from the body. To overcome these limitations, there is a need for the development of new classes of PSs with ruthenium(II) polypyridine complexes currently gaining momentum. However, these compounds generally lack significant absorption in the biological spectral window, limiting their application to treat deep-seated or large tumors. To overcome this drawback, ruthenium(II) polypyridine complexes designed in silico with (E,E′)-4,4′-bisstyryl-2,2′-bipyridine ligands show impressive 1- and 2-Photon absorption up to a magnitude higher than the ones published so far. While nontoxic in the dark, these compounds are phototoxic in various 2D monolayer cells, 3D multicellular tumor spheroids and are able to eradicate a multiresistant tumor inside a mouse model upon clinically relevant 1-Photon and 2-Photon excitation.
Behavior-dependent directional tuning in the human visual-navigation network
Authors: Nau Matthias , Navarro Schröder Tobias , Frey Markus , Doeller Christian F. 
Published: June 2020

AbstractThe brain derives cognitive maps from sensory experience that guide memory formation and behavior. Despite extensive efforts, it still remains unclear how the underlying population activity unfolds during spatial navigation and how it relates to memory performance. To examine these processes, we combined 7T-fMRI with a kernel-based encoding model of virtual navigation to map world-centered directional tuning across the human cortex. First, we present an in-depth analysis of directional tuning in visual, retrosplenial, parahippocampal and medial temporal cortices. Second, we show that tuning strength, width and topology of this directional code during memory-guided navigation depend on successful encoding of the environment. Finally, we show that participants’ locomotory state influences this tuning in sensory and mnemonic regions such as the hippocampus. We demonstrate a direct link between neural population tuning and human cognition, where high-level memory processing interacts with network-wide visuospatial coding in the service of behavior.
Tracking down the molecular architecture of the synaptonemal complex by expansion microscopy
Authors: Zwettler Fabian U. , Spindler Marie-Christin , Reinhard Sebastian , Klein Teresa , Kurz Andreas , Benavente Ricardo , Sauer Markus 
Published: June 2020

AbstractThe synaptonemal complex (SC) is a meiosis-specific nuclear multiprotein complex that is essential for proper synapsis, recombination and segregation of homologous chromosomes. We combined structured illumination microscopy (SIM) with different expansion microscopy (ExM) protocols including U-ExM, proExM, and magnified analysis of the proteome (MAP) to investigate the molecular organization of the SC. Comparison with structural data obtained by single-molecule localization microscopy of unexpanded SCs allowed us to investigate ultrastructure preservation of expanded SCs. For image analysis, we developed an automatic image processing software that enabled unbiased comparison of structural properties pre- and post-expansion. Here, MAP-SIM provided the best results and enabled reliable three-color super-resolution microscopy of the SCs of a whole set of chromosomes in a spermatocyte with 20–30 nm spatial resolution. Our data demonstrate that post-expansion labeling by MAP-SIM improves immunolabeling efficiency and allowed us thus to unravel previously hidden details of the molecular organization of SCs.
Representation of molecular structures with persistent homology for machine learning applications in chemistry
Authors: Townsend Jacob , Micucci Cassie Putman , Hymel John H. , Maroulas Vasileios , Vogiatzis Konstantinos D. 
Published: June 2020

AbstractMachine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.
Tumor invasion in draining lymph nodes is associated with Treg accumulation in breast cancer patients
Authors: Núñez Nicolas Gonzalo , Tosello Boari Jimena , Ramos Rodrigo Nalio , Richer Wilfrid , Cagnard Nicolas , Anderfuhren Cyrill Dimitri , Niborski Leticia Laura , Bigot Jeremy , Meseure Didier , De La Rochere Philippe , Milder Maud , Viel Sophie , Loirat Delphine , Pérol Louis , Vincent-Salomon Anne , Sastre-Garau Xavier , Burkhard Becher , Sedlik Christine , Lantz Olivier , Amigorena Sebastian , Piaggio Eliane 
Published: June 2020

AbstractTumor-draining lymph node (TDLN) invasion by metastatic cells in breast cancer correlates with poor prognosis and is associated with local immunosuppression, which can be partly mediated by regulatory T cells (Tregs). Here, we study Tregs from matched tumor-invaded and non-invaded TDLNs, and breast tumors. We observe that Treg frequencies increase with nodal invasion, and that Tregs express higher levels of co-inhibitory/stimulatory receptors than effector cells. Also, while Tregs show conserved suppressive function in TDLN and tumor, conventional T cells (Tconvs) in TDLNs proliferate and produce Th1-inflammatory cytokines, but are dysfunctional in the tumor. We describe a common transcriptomic signature shared by Tregs from tumors and nodes, including CD80, which is significantly associated with poor patient survival. TCR RNA-sequencing analysis indicates trafficking between TDLNs and tumors and ongoing Tconv/Treg conversion. Overall, TDLN Tregs are functional and express a distinct pattern of druggable co-receptors, highlighting their potential as targets for cancer immunotherapy.
Cavity piezo-mechanics for superconducting-nanophotonic quantum interface
Authors: Han Xu , Fu Wei , Zhong Changchun , Zou Chang-Ling , Xu Yuntao , Sayem Ayed Al , Xu Mingrui , Wang Sihao , Cheng Risheng , Jiang Liang , Tang Hong X. 
Published: June 2020

AbstractHybrid quantum systems are essential for the realization of distributed quantum networks. In particular, piezo-mechanics operating at typical superconducting qubit frequencies features low thermal excitations, and offers an appealing platform to bridge superconducting quantum processors and optical telecommunication channels. However, integrating superconducting and optomechanical elements at cryogenic temperatures with sufficiently strong interactions remains a tremendous challenge. Here, we report an integrated superconducting cavity piezo-optomechanical platform where 10 GHz phonons are resonantly coupled with photons in a superconducting cavity and a nanophotonic cavity at the same time. Taking advantage of the large piezo-mechanical cooperativity (Cem ~7) and the enhanced optomechanical coupling boosted by a pulsed optical pump, we demonstrate coherent interactions at cryogenic temperatures via the observation of efficient microwave-optical photon conversion. This hybrid interface makes a substantial step towards quantum communication at large scale, as well as novel explorations in microwave-optical photon entanglement and quantum sensing mediated by gigahertz phonons.
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
Authors: Dan Yabo , Zhao Yong , Li Xiang , Li Shaobo , Hu Ming , Hu Jianjun 
Published: June 2020

AbstractA major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.
Building a PubMed knowledge graph
Authors: Xu Jian , Kim Sunkyu , Song Min , Jeong Minbyul , Kim Donghyeon , Kang Jaewoo , Rousseau Justin F. , Li Xin , Xu Weijia , Torvik Vetle I. , Bu Yi , Chen Chongyan , Ebeid Islam Akef , Li Daifeng , Ding Ying 
Published: June 2020

AbstractPubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.
Life Course Trajectories of Maternal Cardiovascular Risk Factors according to Offspring Birthweight: The HUNT Study
Authors: Horn Julie , Haug Eirin B. , Markovitz Amanda R. , Fraser Abigail , Vatten Lars J. , Romundstad Pål R. , Rich-Edwards Janet W. , Åsvold Bjørn O. 
Published: June 2020

AbstractWomen with small or large for gestational age offspring are at increased risk of cardiovascular disease later in life. How their cardiovascular risk factors develop across the life course is incompletely known. We linked data from the population-based HUNT Study (1984–2008) and the Medical Birth Registry of Norway (1967–2012) for 22,487 women. Mixed effect models were used to compare cardiovascular risk factor trajectories for women according to first offspring birthweight for gestational age. Women with small for gestational age (SGA) offspring had 1–2 mmHg higher systolic and diastolic blood pressure across the life course, but lower measures of adiposity, compared to women with offspring who were appropriate for gestational age (AGA). In contrast, women with large for gestational age (LGA) offspring had higher measures of adiposity, ~0.1 mmol/l higher non-HDL cholesterol and triglycerides and 0.2 mmol/l higher non-fasting glucose, compared with mothers of AGA offspring. These differences were broadly stable from prior to first pregnancy until 60 years of age. Our findings point to different cardiovascular risk profiles in mothers of SGA versus LGA offspring, where giving birth to SGA offspring might primarily reflect adverse maternal vascular health whereas LGA offspring might reflect the mother’s metabolic health.
Validation of four-dimensional flow cardiovascular magnetic resonance for aortic stenosis assessment
Authors: Archer Gareth T. , Elhawaz Alaa , Barker Natasha , Fidock Benjamin , Rothman Alexander , van der Geest R. J. , Hose Rod , Briffa Norman , Hall Ian R. , Grech Ever , Bissell Malenka , Al-Mohammad Abdallah , Treibel Thomas A. , Swift Andrew J. , Wild James M. , Garg Pankaj 
Published: June 2020

AbstractThe management of patients with aortic stenosis (AS) crucially depends on accurate diagnosis. The main aim of this study were to validate the four-dimensional flow (4D flow) cardiovascular magnetic resonance (CMR) methods for AS assessment. Eighteen patients with clinically severe AS were recruited. All patients had pre-valve intervention 6MWT, echocardiography and CMR with 4D flow. Of these, ten patients had a surgical valve replacement, and eight patients had successful transcatheter aortic valve implantation (TAVI). TAVI patients had invasive pressure gradient assessments. A repeat assessment was performed at 3–4 months to assess the remodelling response. The peak pressure gradient by 4D flow was comparable to an invasive pressure gradient (54 ± 26 mmHG vs 50 ± 34 mmHg, P = 0.67). However, Doppler yielded significantly higher pressure gradient compared to invasive assessment (61 ± 32 mmHG vs 50 ± 34 mmHg, P = 0.0002). 6MWT was associated with 4D flow CMR derived pressure gradient (r = −0.45, P = 0.01) and EOA (r = 0.54, P < 0.01) but only with Doppler EOA (r = 0.45, P = 0.01). Left ventricular mass regression was better associated with 4D flow derived pressure gradient change (r = 0.64, P = 0.04). 4D flow CMR offers an alternative method for non-invasive assessment of AS. In addition, 4D flow derived valve metrics have a superior association to prognostically relevant 6MWT and LV mass regression than echocardiography.
Numerical evaluation of spray position for improved nasal drug delivery
Authors: Basu Saikat , Holbrook Landon T. , Kudlaty Kathryn , Fasanmade Olulade , Wu Jihong , Burke Alyssa , Langworthy Benjamin W. , Farzal Zainab , Mamdani Mohammed , Bennett William D. , Fine Jason P. , Senior Brent A. , Zanation Adam M. , Ebert Charles S. , Kimple Adam J. , Thorp Brian D. , Frank-Ito Dennis O. , Garcia Guilherme J. M. , Kimbell Julia S. 
Published: June 2020

AbstractTopical intra-nasal sprays are amongst the most commonly prescribed therapeutic options for sinonasal diseases in humans. However, inconsistency and ambiguity in instructions show a lack of definitive knowledge on best spray use techniques. In this study, we have identified a new usage strategy for nasal sprays available over-the-counter, that registers an average 8-fold improvement in topical delivery of drugs at diseased sites, when compared to prevalent spray techniques. The protocol involves re-orienting the spray axis to harness inertial motion of particulates and has been developed using computational fluid dynamics simulations of respiratory airflow and droplet transport in medical imaging-based digital models. Simulated dose in representative models is validated through in vitro spray measurements in 3D-printed anatomic replicas using the gamma scintigraphy technique. This work breaks new ground in proposing an alternative user-friendly strategy that can significantly enhance topical delivery inside human nose. While these findings can eventually translate into personalized spray usage instructions and hence merit a change in nasal standard-of-care, this study also demonstrates how relatively simple engineering analysis tools can revolutionize everyday healthcare. Finally, with respiratory mucosa as the initial coronavirus infection site, our findings are relevant to intra-nasal vaccines that are in-development, to mitigate the COVID-19 pandemic.