bluuebunny/crossref_metadata_2025_split huggingface dataset:
Controlling crystal-electric field levels through symmetry-breaking uniaxial pressure in a cubic super heavy fermion
Authors: Gati Elena , Schmidt Burkhard , Bud’ko Sergey L. , Mackenzie Andrew P. , Canfield Paul C. 
Published: November 2023

AbstractYbPtBi is one of the heavy-fermion systems with largest Sommerfeld coefficient γ and is thus classified as a ‘super’-heavy fermion material. In this work, we resolve the long-debated question about the hierarchy of relevant energy scales, such as crystal-electric field (CEF) levels, Kondo and magnetic ordering temperature, in YbPtBi. Through measurements of the a.c. elastocaloric effect and generic symmetry arguments, we identify an elastic level splitting that is unambiguously associated with the symmetry-allowed splitting of a quartet CEF level. This quartet, which we identify to be the first excited state at Δ/kB ≈ 1.6 K above the doublet ground state at ambient pressure, is well below the proposed Kondo temperature TK ≈ 10 K. Consequently, this analysis of the energy scheme can provide support models that predict that the heavy electron mass is a result of an enhanced degeneracy of the CEF ground state, i.e., a quasi-sextet in YbPtBi. At the same time, our study shows the potential of the a.c. elastocaloric effect to control and quantify strain-induced changes of the CEF schemes, opening a different route to disentangle the CEF energy scales from other relevant energy scales in correlated quantum materials.
Distinct distributions of elliptical and disk galaxies across the Local Supercluster as a ΛCDM prediction
Authors: Sawala Till , Frenk Carlos , Jasche Jens , Johansson Peter H. , Lavaux Guilhem 
Published: November 2023

AbstractGalaxies of different types are not equally distributed in the Local Universe. In particular, the supergalactic plane is prominent among the brightest ellipticals, but inconspicuous among the brightest disk galaxies. This striking difference provides a unique test for our understanding of galaxy and structure formation. Here we use the SIBELIUS DARK constrained simulation to confront the predictions of the standard Lambda Cold Dark Matter (ΛCDM) model and standard galaxy formation theory with these observations. We find that SIBELIUS DARK reproduces the spatial distributions of disks and ellipticals and, in particular, the observed excess of massive ellipticals near the supergalactic equator. We show that this follows directly from the local large-scale structure and from the standard galaxy formation paradigm, wherein disk galaxies evolve mostly in isolation, while giant ellipticals congregate in the massive clusters that define the supergalactic plane. Rather than being anomalous as earlier works have suggested, the distributions of giant ellipticals and disks in the Local Universe and in relation to the supergalactic plane are key predictions of the ΛCDM model.
Spatial metatranscriptomics resolves host–bacteria–fungi interactomes
Authors: Saarenpää Sami , Shalev Or , Ashkenazy Haim , Carlos Vanessa , Lundberg Derek Severi , Weigel Detlef , Giacomello Stefania 
Published: November 2023

AbstractThe interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host–microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and find tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intrakingdom spatial interactions among microorganisms, as well as the host response to microbial hotspots. SmT provides an approach for answering fundamental questions on host–microbiome interplay.
Spatial host–microbiome sequencing reveals niches in the mouse gut
Authors: Lötstedt Britta , Stražar Martin , Xavier Ramnik , Regev Aviv , Vickovic Sanja 
Published: November 2023

AbstractMucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host–microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host–microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host–bacteria interactions. SHM-seq should enhance the study of native host–microbe interactions in health and disease.
Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries
Authors: An Ulzee , Pazokitoroudi Ali , Alvarez Marcus , Huang Lianyun , Bacanu Silviu , Schork Andrew J. , Kendler Kenneth , Pajukanta Päivi , Flint Jonathan , Zaitlen Noah , Cai Na , Dahl Andy , Sankararaman Sriram 
Published: November 2023

AbstractBiobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or ‘fill-in’ missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.
Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder
Authors: Dahl Andrew , Thompson Michael , An Ulzee , Krebs Morten , Appadurai Vivek , Border Richard , Bacanu Silviu-Alin , Werge Thomas , Flint Jonathan , Schork Andrew J. , Sankararaman Sriram , Kendler Kenneth S. , Cai Na 
Published: November 2023

AbstractBiobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.
Large-scale pancreatic cancer detection via non-contrast CT and deep learning
Authors: Cao Kai , Xia Yingda , Yao Jiawen , Han Xu , Lambert Lukas , Zhang Tingting , Tang Wei , Jin Gang , Jiang Hui , Fang Xu , Nogues Isabella , Li Xuezhou , Guo Wenchao , Wang Yu , Fang Wei , Qiu Mingyan , Hou Yang , Kovarnik Tomas , Vocka Michal , Lu Yimei , Chen Yingli , Chen Xin , Liu Zaiyi , Zhou Jian , Xie Chuanmiao , Zhang Rong , Lu Hong , Hager Gregory D. , Yuille Alan L. , Lu Le , Shao Chengwei , Shi Yu , Zhang Qi , Liang Tingbo , Zhang Ling , Lu Jianping 
Published: November 2023

AbstractPancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.
LINC01638 sustains human mesenchymal stem cell self-renewal and competency for osteogenic cell fate
Authors: Gordon Jonathan A. R. , Tye Coralee E. , Banerjee Bodhisattwa , Ghule Prachi N. , van Wijnen Andre J. , Kabala Fleur S. , Page Natalie A. , Falcone Michelle M. , Stein Janet L. , Stein Gary S. , Lian Jane B. 
Published: November 2023

AbstractThe skeleton forms from multipotent human mesenchymal stem cells (hMSCs) competent to commit to specific lineages. Long noncoding RNAs (lncRNAs) have been identified as key epigenetic regulators of tissue development. However, regulation of osteogenesis by lncRNAs as mediators of commitment to the bone phenotype is largely unexplored. We focused on LINC01638, which is highly expressed in hMSCs and has been studied in cancers, but not in regulating osteogenesis. We demonstrated that LINC01638 promotes initiation of the osteoblast phenotype. Our findings reveal that LINC01638 is present at low levels during the induction of osteoblast differentiation. CRISPRi knockdown of LINC01638 in MSCs prevents osteogenesis and alkaline phosphatase expression, inhibiting osteoblast differentiation. This resulted in decreased MSC growth rate, accompanied by double-strand breaks, DNA damage, and cell senescence. Transcriptome profiling of control and LINC01638-depleted hMSCs identified > 2000 differentially expressed mRNAs related to cell cycle, cell division, spindle formation, DNA repair, and osteogenesis. Using ChIRP-qPCR, molecular mechanisms of chromatin interactions revealed the LINC01638 locus (Chr 22) includes many lncRNAs and bone-related genes. These novel findings identify the obligatory role for LINC01638 to sustain MSC pluripotency regulating osteoblast commitment and growth, as well as for physiological remodeling of bone tissue.
Association between dietary inflammatory index and musculoskeletal disorders in adults
Authors: Khamoushi Firoozeh , Soleimani Davood , Najafi Farid , Ahmadi Neshat , Heidarzadeh-Esfahani Neda , Anvari Bita , Shakiba Ebrahim , Pasdar Yahya 
Published: November 2023

AbstractThis research investigated how the Dietary Inflammatory Index (DII) related to musculoskeletal issues in adults. It used a cross-sectional design with a sample of 3477 female and 3572 male participants aged 35 to 65 from the Ravansar Non-Communicable Diseases cohort study in western Iran. The DII is calculated from a Food Frequency Questionnaire (FFQ) to measure dietary intake. Musculoskeletal disorders including back pain, back pain/stiffness, joint pain, and joint pain/stiffness were evaluated by the RaNCD cohort study physician using a standard questionnaire. Logistic regression analysis examined the association between DII and musculoskeletal disorders. The findings demonstrated a positive association between higher DII scores and back pain/stiffness (OR 1.32, 95% CI 1.04–1.73, P = 0.047). Furthermore, DII displayed a significant association with a heightened odd to joint pain (OR 1.26, CI 1.10–1.46) when compared to those with lower DII scores (Q3 vs. Q1). After adjusting for cofounding factors, the Q3 DII quintile participants showed a 44% higher odd of experiencing joint pain/stiffness (OR 1.44, CI 1.01–2.05, P = 0.047). However, the study found no significant association between back pain and DII (P > 0.05). In conclusion, the research suggests that consuming a pro-inflammatory diet might be linked to developing musculoskeletal issues in adults.
Directional intensified feature description using tertiary filtering for augmented reality tracking
Authors: S Indhumathi. , Clement J. Christopher 
Published: November 2023

AbstractAugmented Reality (AR) is applied in almost every field, and a few, but not limited, are engineering, medical, gaming and internet of things. The application of image tracking is inclusive in all these mentioned fields. AR uses image tracking to localize and register the position of the user/AR device for superimposing the virtual image into the real-world. In general terms, tracking the image enhance the users’ experience. However, in the image tracking application, establishing the interface between virtual realm and the physical world has many shortcomings. Many tracking systems are available, but it lacks in robustness and efficiency. The robustness of the tracking algorithm, is the challenging task of implementation. This study aims to enhance the users’ experience in AR by describing an image using Directional Intensified Features with Tertiary Filtering. This way of describing the features improve the robustness, which is desired in the image tracking. A feature descriptor is robust, in the sense that it does not compromise, when the image undergoes various transformations. This article, describes the features based on the Directional Intensification using Tertiary Filtering (DITF). The robustness of the algorithm is improved, because of the inherent design of Tri-ocular, Bi-ocular and Dia-ocular filters that can intensify the features in all required directions. The algorithm’s robustness is verified with respect to various image transformations. The oxford dataset is used for performance analysis and validation. DITF model is designed to achieve the repeatability score of illumination-variation , blur changes and view-point variation, as 100%, 100% and 99% respectively. The comparative analysis has been performed in terms of precision and re-call. DITF outperforms the state-of-the-art descriptors, namely, BEBLID, BOOST, HOG, LBP, BRISK and AKAZE. An Implementation of DITF source code is available in the following GitHub repository: github.com/Johnchristopherclement/Directional-Intensified-Feature-Descriptor.
Network coding construction for a special class of three unicast sessions
Authors: PU Baoxing 
Published: November 2023

AbstractFor a special class of three unicast sessions, in which the maximum flow from each sender to each receiver is the same positive integer k, a network coding approach is proposed. A multigeneration mixed strategy, in which (2 × n + 1) consecutive generations are taken as a mixed set, is adopted. The precoding strategy is adopted at the senders, random linear network coding technology is adopted at intermediate nodes to transmit data, and interference alignment technology is used at the receivers to eliminate some interference information. The proposed approach can construct a network coding data transmission scheme, in which the transmission rate vector of the senders is [(n + 1) × k/(2 × n + 1), n × k/(2 × n + 1), n × k/(2 × n + 1)]. The feasibility of the proposed approach is mathematically deduced and proven, and the simulation results verify the conclusions of the theoretical analysis.
Associations between weekend catch-up sleep and health-related quality of life with focusing on gender differences
Authors: Oh Jinkyung , Kim Eunmi , Huh Iksoo 
Published: November 2023

AbstractThis study investigated associations between weekend catch-up sleep (WCUS) and health-related quality of life (HRQoL) in 15,837 participants from the 7th (2016–2018) Korea National Health and Nutrition Examination Survey. We categorized WCUS durations into four groups: none (≤ 0 h [h]), short (> 0 h, ≤ 1 h), medium (> 1 h, ≤ 2 h), and long (> 2 h), and performed complex samples logistic regression and likelihood ratio χ2 test. The study found significant associations in women for the European Quality of Life-5 Dimensions (EQ-5D) index and three EQ-5D subdomains (self-care, usual activities, and anxiety/depression) with the WCUS durations, but no significant association in men. Compared to the non-WCUS, the short or medium WCUS was positively associated with the EQ-5D index and EQ-5D subdomains (usual activities and anxiety/depression) in women, while the long WCUS significantly reduced the quality of life in the self-care domain. In an additional subgroup analysis by age, middle-aged and elderly women had a more noticeable effect of WCUS on HRQoL than young women, and the short or medium WCUS improved HRQoL in middle-aged and elderly women in general. Therefore, we recommend appropriate WCUS durations to improve HRQoL, considering both gender and age.
Mobile phone data reveals spatiotemporal recreational patterns in conservation areas during the COVID pandemic
Authors: Kim Ji Yoon , Kubo Takahiro , Nishihiro Jun 
Published: November 2023

AbstractUnderstanding visitation patterns is crucial in developing effective conservation strategies for protected areas, as it serves as an indicator for operating an ecosystem management plan that balances biodiversity and ecosystem services intertwined with public health and social benefits. However, limited data availability during the COVID-19 pandemic has hindered the comprehensive understanding of temporal changes in realized cultural ecosystem services, particularly in recreational activities within these areas. Our study utilized GPS data from mobile phones to quantify visitor characteristics and their contribution to recreational ecosystem services in protected areas at a national scale during the COVID-19 pandemic. We estimated the pandemic's relative impact on visitor patterns at 98 visitor centers in national parks and Ramsar sites in Japan. The total number of visitors and travel distance in various sizes of protected areas decreased after the outbreak of COVID-19. The number of visitors in the protected areas displayed a quick recovery despite the increasing positive COVID-19 cases during the following summer. Post-pandemic, visitors showed a preference for less densely populated protected areas closer to their home range. Our findings partly suggest that protecting a diverse range of conservation areas along the urban gradient could be an effective strategy for maintaining the resilience of recreational services during a prolonged pandemic.
Development and validation of a nomogram for predicting recurrence-free survival in endometrial cancer: a multicenter study
Authors: Li Yinuo , Hou Xin , Chen Wei , Wang Shixuan , Ma Xiangyi 
Published: November 2023

AbstractRecurrence is the main cause of death in patients with endometrial cancer (EC). This study aimed to construct and validate a nomogram to predict the recurrence-free survival of patients with EC. This was a multicenter retrospective study. A total of 812 patients from Wuhan Tongji Hospital were divided into training and validation cohorts, and 347 and 580 patients from People’s Hospital of Peking University and Qilu Hospital of Shandong, respectively, were used for validation. Univariate and multivariate Cox regression analyses were used to construct a nomogram for predicting recurrence-free survival of EC. Calibration curves, receiver operating characteristic (ROC) curves, and consistency indexes (C-indexes) were used to estimate the performance of the model. Decision curve analysis (DCA) curves were used to assess the clinical utility of the model. Age (P = 0.013), cancer antigen 125 level (P = 0.014), lymphovascular space invasion (P = 0.004), International Federation of Gynecology and Obstetrics stage (P = 0.034), and P53 (P < 0.001) were independently associated with recurrence, and we constructed a nomogram based on these variables. The C-indexes of the validation cohorts were 0.880, 0.835, and 0.875, respectively. The calibration, ROC, and DCA curves revealed that this model had excellent performance and clinical utility. Combining clinical data, clinicopathological factors, serological indicators, and immunohistochemical marks, a multicenter externally verified nomogram with robust performance was constructed to predict the recurrence of patients with EC.
Use of a point-of-care test to rapidly assess levels of SARS-CoV-2 nasal neutralising antibodies in vaccinees and breakthrough infected individuals
Authors: Tan Chee Wah , Lim Chuan Kok , Prestedge Jacqueline , Batty Mitchell , Mah Yun Yan , O’Han Michelle , Wang Lin-Fa , Kilby Dean , Anderson Danielle E. 
Published: November 2023

AbstractDespite SARS-CoV-2 vaccines eliciting systemic neutralising antibodies (nAbs), breakthrough infections still regularly occur. Infection helps to generate mucosal immunity, possibly reducing disease transmission. Monitoring mucosal nAbs is predominantly restricted to lab-based assays, which have limited application to the public. In this multi-site study, we used lateral-flow surrogate neutralisation tests to measure mucosal and systemic nAbs in vaccinated and breakthrough infected individuals in Australia and Singapore. Using three lateral flow assays to detect SARS-CoV-2 nAbs, we demonstrated that nasal mucosal nAbs were present in 71.4 (95% CI 56.3–82.9%) to 85.7% (95% CI 71.8–93.7%) of individuals with breakthrough infection (positivity rate was dependent upon the type of test), whereas only 20.7 (95% CI 17.1–49.4%) to 34.5% (95% CI 19.8–52.7%) of vaccinated individuals without breakthrough infection had detectible nasal mucosal nAbs. Of the individuals with breakthrough infection, collective mucosal anti-S antibody detection in confirmatory assays was 92.9% (95% CI 80.3–98.2%) of samples, while 72.4% (95% CI 54.1–85.5%) of the vaccinated individuals who had not experienced a breakthrough infection were positive to anti-S antibody. All breakthrough infected individuals produced systemic anti-N antibodies; however, these antibodies were not detected in the nasal cavity. Mucosal immunity is likely to play a role in limiting the transmission of SARS-CoV-2 and lateral flow neutralisation tests provide a rapid readout of mucosal nAbs at the point-of-care.