bluuebunny/crossref_metadata_2025_split huggingface dataset:
Laser-annealing Josephson junctions for yielding scaled-up superconducting quantum processors
Authors: Hertzberg Jared B. , Zhang Eric J. , Rosenblatt Sami , Magesan Easwar , Smolin John A. , Yau Jeng-Bang , Adiga Vivekananda P. , Sandberg Martin , Brink Markus , Chow Jerry M. , Orcutt Jason S. 
Published: August 2021

AbstractAs superconducting quantum circuits scale to larger sizes, the problem of frequency crowding proves a formidable task. Here we present a solution for this problem in fixed-frequency qubit architectures. By systematically adjusting qubit frequencies post-fabrication, we show a nearly tenfold improvement in the precision of setting qubit frequencies. To assess scalability, we identify the types of “frequency collisions” that will impair a transmon qubit and cross-resonance gate architecture. Using statistical modeling, we compute the probability of evading all such conditions, as a function of qubit frequency precision. We find that, without post-fabrication tuning, the probability of finding a workable lattice quickly approaches 0. However, with the demonstrated precisions it is possible to find collision-free lattices with favorable yield. These techniques and models are currently employed in available quantum systems and will be indispensable as systems continue to scale to larger sizes.
Effects of a large-scale social media advertising campaign on holiday travel and COVID-19 infections: a cluster randomized controlled trial
Authors: Breza Emily , Stanford Fatima Cody , Alsan Marcella , Alsan Burak , Banerjee Abhijit , Chandrasekhar Arun G. , Eichmeyer Sarah , Glushko Traci , Goldsmith-Pinkham Paul , Holland Kelly , Hoppe Emily , Karnani Mohit , Liegl Sarah , Loisel Tristan , Ogbu-Nwobodo Lucy , Olken Benjamin A. , Torres Carlos , Vautrey Pierre-Luc , Warner Erica T. , Wootton Susan , Duflo Esther 
Published: August 2021

AbstractDuring the Coronavirus Disease 2019 (COVID-19) epidemic, many health professionals used social media to promote preventative health behaviors. We conducted a randomized controlled trial of the effect of a Facebook advertising campaign consisting of short videos recorded by doctors and nurses to encourage users to stay at home for the Thanksgiving and Christmas holidays (NCT04644328 and AEARCTR-0006821). We randomly assigned counties to high intensity (n = 410 (386) at Thanksgiving (Christmas)) or low intensity (n = 410 (381)). The intervention was delivered to a large fraction of Facebook subscribers in 75% and 25% of randomly assigned zip codes in high- and low-intensity counties, respectively. In total, 6,998 (6,716) zip codes were included, and 11,954,109 (23,302,290) users were reached at Thanksgiving (Christmas). The first two primary outcomes were holiday travel and fraction leaving home, both measured using mobile phone location data of Facebook users. Average distance traveled in high-intensity counties decreased by −0.993 percentage points (95% confidence interval (CI): –1.616, −0.371; P = 0.002) for the 3 days before each holiday compared to low-intensity counties. The fraction of people who left home on the holiday was not significantly affected (adjusted difference: 0.030; 95% CI: −0.361, 0.420; P = 0.881). The third primary outcome was COVID-19 infections recorded at the zip code level in the 2-week period starting 5 days after the holiday. Infections declined by 3.5% (adjusted 95% CI: −6.2%, −0.7%; P = 0.013) in intervention compared to control zip codes. Social media messages recorded by health professionals before the winter holidays in the United States led to a significant reduction in holiday travel and subsequent COVID-19 infections.
Synthesis of triarylpyridines with sulfonate and sulfonamide moieties via a cooperative vinylogous anomeric-based oxidation
Authors: Torabi Morteza , Zolfigol Mohammad Ali , Yarie Meysam , Notash Behrouz , Azizian Saeid , Azandaryani Mina Mirzaei 
Published: August 2021

AbstractHerein, novel magnetic nanoparticles with pyridinium bridges namely Fe3O4@SiO2@PCLH-TFA through a multi-step pathway were designed and synthesized. The desired catalyst and its corresponding precursors were characterized with different techniques such as Fourier transform infrared (FT-IR) spectroscopy, 1H NMR, 13C NMR, Mass spectroscopy, energy dispersive X-ray (EDX) analysis, thermogravimetric/derivative thermogravimetry (TG/DTG) analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and vibrating sample magnetometer (VSM). In addition, the catalytic application of the prepared catalyst in the synthesis of new series of triarylpyridines bearing sulfonate and sulfonamide moieties via a cooperative vinylogous anomeric-based oxidation was highlighted. The current trend revealed that the mentioned catalyst shows high recoverability in the reported synthesis.
Structural and optical properties of Nd:YAB-nanoparticle-doped PDMS elastomers for random lasers
Authors: Hlil Antsar R. , Thomas Jyothis , Garcia-Puente Yalina , Boisvert Jean-Sebastien , Lima Bismarck C. , Rakotonandrasana Ando , Maia Lauro J. Q. , Tehranchi Amirhossein , Loranger Sebastien , Gomes Anderson S. L. , Messaddeq Younes , Kashyap Raman 
Published: August 2021

AbstractWe report the structural and optical properties of Nd:YAB (NdxY1−x Al3(BO3)4)-nanoparticle-doped PDMS elastomer films for random lasing (RL) applications. Nanoparticles with Nd ratios of x = 0.2, 0.4, 0.6, 0.8, and 1.0 were prepared and then incorporated into the PDMS elastomer to control the optical gain density and scattering center content over a wide range. The morphology and thermal stability of the elastomer composites were studied. A systematic investigation of the lasing wavelength, threshold, and linewidth of the laser was carried out by tailoring the concentration and optical gain of the scattering centers. The minimum threshold and linewidth were found to be 0.13 mJ and 0.8 nm for x = 1 and 0.8. Furthermore, we demonstrated that the RL intensity was easily tuned by controlling the degree of mechanical stretching, with strain reaching up to 300%. A strong, repeatable lasing spectrum over ~ 50 cycles of applied strain was observed, which demonstrates the high reproducibility and robustness of the RL. In consideration for biomedical applications that require long-term RL stability, we studied the intensity fluctuation of the RL emission, and confirmed that it followed Lévy-like statistics. Our work highlights the importance of using rare-earth doped nanoparticles with polymers for RL applications.
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
Authors: Jiang Shuai , Zanazzi George J. , Hassanpour Saeed 
Published: August 2021

AbstractWe developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.
Implications of zonal architecture on differential gene expression profiling and altered pathway expressions in mandibular condylar cartilage
Authors: Basudan Aisha M. , Aziz Mohammad Azhar , Yang Yanqi 
Published: August 2021

AbstractMandibular condylar cartilage (MCC) is a multi-zonal heterogeneous fibrocartilage containing different types of cells, but the factors/mechanisms governing the phenotypic transition across the zones have not been fully understood. The reliability of molecular studies heavily rely on the procurement of pure cell populations from the heterogeneous tissue. We used a combined laser-capture microdissection and microarray analysis approach which allowed identification of differential zone-specific gene expression profiling and altered pathways in the MCC of 5-week-old rats. The bioinformatics analysis demonstrated that the MCC cells clearly exhibited distinguishable phenotypes from the articular chondrocytes. Additionally, a set of genes has been determined as potential markers to identify each MCC zone individually; Crab1 gene showed the highest enrichment while Clec3a was the most downregulated gene at the superficial layer, which consists of fibrous (FZ) and proliferative zones (PZ). Ingenuity Pathway Analysis revealed numerous altered signaling pathways; Leukocyte extravasation signaling pathway was predicted to be activated at all MCC zones, in particular mature and hypertrophic chondrocytes zones (MZ&HZ), when compared with femoral condylar cartilage (FCC). Whereas Superpathway of Cholesterol Biosynthesis showed predicted activation in both FZ and PZ as compared with deep MCC zones and FCC. Determining novel zone-specific differences of large group of potential genes, upstream regulators and pathways in healthy MCC would improve our understanding of molecular mechanisms on regional (zonal) basis, and provide new insights for future therapeutic strategies.
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm
Authors: Sardesai Abha Umesh , Tanak Ambalika Sanjeev , Krishnan Subramaniam , Striegel Deborah A. , Schully Kevin L. , Clark Danielle V. , Muthukumar Sriram , Prasad Shalini 
Published: August 2021

AbstractSepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.
A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
Authors: Li Wen , Liang Yuan , Zhang Xuan , Liu Chao , He Lei , Miao Leiying , Sun Weibin 
Published: August 2021

AbstractRoutine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.
SARS-CoV-2 infection initiates interleukin-17-enriched transcriptional response in different cells from multiple organs
Authors: Hasan Md Zobaer , Islam Syful , Matsumoto Kenichi , Kawai Taro 
Published: August 2021

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has emerged as a pandemic. Paucity of information concerning the virus and therapeutic interventions have made SARS-CoV-2 infection a genuine threat to global public health. Therefore, there is a growing need for understanding the molecular mechanism of SARS-CoV-2 infection at cellular level. To address this, we undertook a systems biology approach by analyzing publicly available RNA-seq datasets of SARS-CoV-2 infection of different cells and compared with other lung pathogenic infections. Our study identified several key genes and pathways uniquely associated with SARS-CoV-2 infection. Genes such as interleukin (IL)-6, CXCL8, CCL20, CXCL1 and CXCL3 were upregulated, which in particular regulate the cytokine storm and IL-17 signaling pathway. Of note, SARS-CoV-2 infection strongly activated IL-17 signaling pathway compared with other respiratory viruses. Additionally, this transcriptomic signature was also analyzed to predict potential drug repurposing and small molecule inhibitors. In conclusion, our comprehensive data analysis identifies key molecular pathways to reveal underlying pathological etiology and potential therapeutic targets in SARS-CoV-2 infection.
Diversity of MHC IIB genes and parasitism in hybrids of evolutionarily divergent cyprinoid species indicate heterosis advantage
Authors: Šimková Andrea , Gettová Lenka , Civáňová Kristína , Seifertová Mária , Janáč Michal , Vetešník Lukáš 
Published: August 2021

AbstractThe genes of the major histocompatibility complex (MHC) are an essential component of the vertebrate immune system and MHC genotypes may determine individual susceptibility to parasite infection. In the wild, selection that favors MHC variability can create situations in which interspecies hybrids experience a survival advantage. In a wild system of two naturally hybridizing leuciscid fish, we assessed MHC IIB genetic variability and its potential relationships to hosts’ ectoparasite communities. High proportions of MHC alleles and parasites were species-specific. Strong positive selection at specific MHC codons was detected in both species and hybrids. MHC allele expression in hybrids was slightly biased towards the maternal species. Controlling for a strong seasonal effect on parasite communities, we found no clear associations between host-specific parasites and MHC alleles or MHC supertypes. Hybrids shared more MHC alleles with the more MHC-diverse parental species, but expressed intermediate numbers of MHC alleles and positively selected sites. Hybrids carried significantly fewer ectoparasites than either parent species, suggesting a hybrid advantage via potential heterosis.
A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction
Authors: Wang Kang , Niu Xin , Dou Yong , Xie Dongxing , Yang Tuo 
Published: August 2021

AbstractGrading individual knee osteoarthritis (OA) features is a fine-grained knee OA severity assessment. Existing methods ignore following problems: (1) more accurately located knee joints benefit subsequent grades prediction; (2) they do not consider knee joints’ symmetry and semantic information, which help to improve grades prediction performance. To this end, we propose a SE-ResNext50-32x4d-based Siamese network with adaptive gated feature fusion method to simultaneously assess eight tasks. In our method, two cascaded small convolution neural networks are designed to locate more accurate knee joints. Detected knee joints are further cropped and split into left and right patches via their symmetry, which are fed into SE-ResNext50-32x4d-based Siamese network with shared weights, extracting more detailed knee features. The adaptive gated feature fusion method is used to capture richer semantic information for better feature representation here. Meanwhile, knee OA/non-knee OA classification task is added, helping extract richer features. We specially introduce a new evaluation metric (top±1 accuracy) aiming to measure model performance with ambiguous data labels. Our model is evaluated on two public datasets: OAI and MOST datasets, achieving the state-of-the-art results comparing to competing approaches. It has the potential to be a tool to assist clinical decision making.
Genetics of PlGF plasma levels highlights a role of its receptors and supports the link between angiogenesis and immunity
Authors: Ruggiero Daniela , Nutile Teresa , Nappo Stefania , Tirozzi Alfonsina , Bellenguez Celine , Leutenegger Anne-Louise , Ciullo Marina 
Published: August 2021

AbstractPlacental growth factor (PlGF) is a member of the vascular endothelial growth factor family and is involved in bone marrow-derived cell activation, endothelial stimulation and pathological angiogenesis. High levels of PlGF have been observed in several pathological conditions especially in cancer, cardiovascular, autoimmune and inflammatory diseases. Little is known about the genetics of circulating PlGF levels. Indeed, although the heritability of circulating PlGF levels is around 40%, no studies have assessed the relation between PlGF plasma levels and genetic variants at a genome-wide level. In the current study, PlGF plasma levels were measured in a population-based sample of 2085 adult individuals from three isolated populations of South Italy. A GWAS was performed in a discovery cohort (N = 1600), followed by a de novo replication (N = 468) from the same populations. The meta-analysis of the discovery and replication samples revealed one signal significantly associated with PlGF circulating levels. This signal was mapped to the PlGF co-receptor coding gene NRP1, indicating its important role in modulating the PlGF plasma levels. Two additional signals, at the PlGF receptor coding gene FLT1 and RAPGEF5 gene, were identified at a suggestive level. Pathway and TWAS analyses highlighted genes known to be involved in angiogenesis and immune response, supporting the link between these processes and PlGF regulation. Overall, these data improve our understanding of the genetic variation underlying circulating PlGF levels. This in turn could lead to new preventive and therapeutic strategies for a wide variety of PlGF-related pathologies.
Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
Authors: Li Yang , Wang Zheng , Li Li-Ping , You Zhu-Hong , Huang Wen-Zhun , Zhan Xin-Ke , Wang Yan-Bin 
Published: August 2021

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.
A systematic review and meta-analysis of obstetric and maternal outcomes after prior uterine artery embolization
Authors: Matsuzaki Shinya , Lee Misooja , Nagase Yoshikazu , Jitsumori Mariko , Matsuzaki Satoko , Maeda Michihide , Takiuchi Tsuyoshi , Kakigano Aiko , Mimura Kazuya , Ueda Yutaka , Tomimatsu Takuji , Endo Masayuki , Kimura Tadashi 
Published: August 2021

AbstractThis study aimed to review the obstetric complications during subsequent pregnancies after uterine artery embolization (UAE) for postpartum hemorrhage (PPH) by exploring the relationship between prior UAE and obstetric complications through a meta-analysis. We conducted a systematic literature review through March 31, 2021, using PubMed, Scopus, and the Cochrane Central Register of Controlled Trials in compliance with the PRISMA guidelines and determined the effect of prior UAE for PPH on the rate of placenta accreta spectrum (PAS), PPH, placenta previa, hysterectomy, fetal growth restriction (FGR), and preterm birth (PTB). Twenty-three retrospective studies (2003–2021) met the inclusion criteria. They included 483 pregnancies with prior UAE and 320,703 pregnancies without prior UAE. The cumulative results of all women with prior UAE indicated that the rates of obstetric complications PAS, hysterectomy, and PPH were 16.3% (34/208), 6.5% (28/432), and 24.0% (115/480), respectively. According to the patient background-matched analysis based on the presence of prior PPH, women with prior UAE were associated with higher rates of PAS (odds ratio [OR] 20.82; 95% confidence interval [CI] 3.27–132.41) and PPH (OR 5.32, 95% CI 1.40–20.16) but not with higher rates of hysterectomy (OR 8.93, 95% CI 0.43–187.06), placenta previa (OR 2.31, 95% CI 0.35–15.22), FGR (OR 7.22, 95% CI 0.28–188.69), or PTB (OR 3.00, 95% CI 0.74–12.14), compared with those who did not undergo prior UAE. Prior UAE for PPH may be a significant risk factor for PAS and PPH during subsequent pregnancies. Therefore, at the time of delivery, clinicians should be more attentive to PAS and PPH when women have undergone prior UAE. Since the number of women included in the patient background-matched study was limited, further investigations are warranted to confirm the results of this study.