Miracle-dz/UniFAR-FTUs huggingface dataset:
CONTRACTION SIGNATURES TOWARD DENSE CORES IN THE PERSEUS MOLECULAR CLOUD

We report the results of an HCO+ (3–2) and N2D+ (3–2) molecular line survey performed toward 91 dense cores in the Perseus molecular cloud using the James Clerk Maxwell Telescope, to identify the fraction of starless and protostellar cores with systematic radial motions. We quantify the HCO+ asymmetry using a dimensionless asymmetry parameter δv, and identify 20 cores with significant blue or red line asymmetries in optically thick emission indicative of collapsing or expanding motions, respectively. We separately fit the HCO+ profiles with an analytic collapse model and determine contraction (expansion) speeds toward 22 cores. Comparing the δv and collapse model results, we find that δv is a good tracer of core contraction if the optically thin emission is aligned with the model-derived systemic velocity. The contraction speeds range from subsonic (0.03 km s−1) to supersonic (0.4 km s−1), where the supersonic contraction speeds may trace global rather than local core contraction. Most cores have contraction speeds significantly less than their free-fall speeds. Only 7 of 28 starless cores have spectra well-fit by the collapse model, which more than doubles (15 of 28) for protostellar cores. Starless cores with masses greater than the Jeans mass (M/MJ > 1) are somewhat more likely to show contraction motions. We find no trend of optically thin non-thermal line width with M/MJ, suggesting that any undetected contraction motions are small and subsonic. Most starless cores in Perseus are either not in a state of collapse or expansion, or are in a very early stage of collapse.
All-optical reconstruction of atomic ground-state population

The population distribution within the ground state of an atomic ensemble is of great significance in a variety of quantum-optics processes. We present a method to reconstruct the detailed population distribution from a set of absorption measurements with various frequencies and polarizations, by utilizing the differences between the dipole matrix elements of the probed transitions. The technique is experimentally implemented on a thermal rubidium vapor, demonstrating a population-based analysis in two optical-pumping examples. The results are used to verify and calibrate an elaborated numerical model, and the limitations of the reconstruction scheme, which result from the symmetry properties of the dipole matrix elements, are discussed.
Oral mitis group streptococci reduce infectivity of influenza A virus via acidification and H2O2 production

Members of the mitis group streptococci are the most abundant inhabitants of the oral cavity and dental plaque. Influenza A virus (IAV), the causative agent of influenza, infects the upper respiratory tract, and co-infection with Streptococcus pneumoniae is a major cause of morbidity during influenza epidemics. S. pneumoniae is a member of mitis group streptococci and shares many features with oral mitis group streptococci. In this study, we investigated the effect of viable Streptococcus oralis, a representative member of oral mitis group, on the infectivity of H1N1 IAV. The infectivity of IAV was measured by a plaque assay using Madin-Darby canine kidney cells. When IAV was incubated in growing culture of S. oralis, the IAV titer decreased in a time- and dose-dependent manner and became less than 100-fold, whereas heat-inactivated S. oralis had no effect. Other oral streptococci such as Streptococcus mutans and Streptococcus salivarius also reduced the viral infectivity to a lesser extent compared to S. oralis and Streptococcus gordonii, another member of the oral mitis group. S. oralis produces hydrogen peroxide (H2O2) at a concentration of 1–2 mM, and its mutant deficient in H2O2 production showed a weaker effect on the inactivation of IAV, suggesting that H2O2 contributes to viral inactivation. The contribution of H2O2 was confirmed by an inhibition assay using catalase, an H2O2-decomposing enzyme. These oral streptococci produce short chain fatty acids (SCFA) such as acetic acid as a by-product of sugar metabolism, and we also found that the inactivation of IAV was dependent on the mildly acidic pH (around pH 5.0) of these streptococcal cultures. Although inactivation of IAV in buffers of pH 5.0 was limited, incubation in the same buffer containing 2 mM H2O2 resulted in marked inactivation of IAV, which was similar to the effect of growing S. oralis culture. Taken together, these results reveal that viable S. oralis can inactivate IAV via the production of SCFAs and H2O2. This finding also suggests that the combination of mildly acidic pH and H2O2 at low concentrations could be an effective method to inactivate IAV.
TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms

Abstract Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene−gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg. cuhk.edu. hk/.
Embedded 3D printing of dilute particle suspensions into dense complex tissue fibers using shear thinning xanthan baths

In order to fabricate functional organoids and microtissues, a high cell density is generally required. As such, the placement of cell suspensions in molds or microwells to allow for cell concentration by sedimentation is the current standard for the production of organoids and microtissues. Even though molds offer some level of control over the shape of the resulting microtissue, this control is limited as microtissues tend to compact towards a sphere after sedimentation of the cells. 3D bioprinting on the other hand offers complete control over the shape of the resulting structure. Even though the printing of dense cell suspensions in the ink has been reported, extruding dense cellular suspensions is challenging and generally results in high shear stresses on the cells and a poor shape fidelity of the print. As such, additional materials such as hydrogels are added in the bioink to limit shear stresses, and to improve shape fidelity and resolution. The maximum cell concentration that can be incorporated in a hydrogel-based ink before the ink’s rheological properties are compromised, is significantly lower than the concentration in a tissue equivalent. Additionally, the hydrogel components often interfere with cellular self-assembly processes. To circumvent these limitations, we report a simple and inexpensive xanthan bath based embedded printing method to 3D print dense functional linear tissues using dilute particle suspensions consisting of cells, spheroids, hydrogel beads, or combinations thereof. Using this method, we demonstrated the self-organization of functional cardiac tissue fibers with a layer of epicardial cells surrounding a body of cardiomyocytes.
Intelligent COVID-19 screening platform based on breath analysis

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p < 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and an F1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network, k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.
Gravitational wave signals of electroweak phase transition triggered by dark matter

We study in this work a scenario that the universe undergoes a two step phase transition with the first step happened to the dark matter sector and the second step being the transition between the dark matter and the electroweak vacuums, where the barrier between the two vacuums, that is necessary for a strongly first order electroweak phase transition (EWPT) as required by the electroweak baryogenesis mechanism, arises at the tree-level. We illustrate this idea by working with the standard model (SM) augmented by a scalar singlet dark matter and an extra scalar singlet which mixes with the SM Higgs boson. We study the conditions for such pattern of phase transition to occur and especially for the strongly first order EWPT to take place, as well as its compatibility with the basic requirements of a successful dark matter, such as observed relic density and constraints of direct detections. We further explore the discovery possibility of this pattern EWPT by searching for the gravitational waves generated during this process in spaced based interferometer, by showing a representative benchmark point of the parameter space that the generated gravitational waves fall within the sensitivity of eLISA, DECIGO and BBO.
e-TSN: an interactive visual exploration platform for target–disease knowledge mapping from literature

Abstract Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target–disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug–target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target–disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.
Discovery of a nearby young brown dwarf binary candidate

In near-infrared NaCo observations of the young brown dwarf 2MASS J0041353-562112, we discovered a companion a little less than a magnitude fainter than the primary. The binary candidate has a separation of 143 mas, and the spectral types of the two components are M 6.5 and M 9.0. Colors and flux ratios of the components are consistent with their locations being at the same distance minimizing the probability of the secondary being a background object. The brown dwarf is known to exhibit Li absorption constraining the age to be younger than ∼200 Myr, and has been suspected of experiencing ongoing accretion, which implies an age as young as ∼ 10 Myr. We estimate distance and orbital parameters of the binary as a function of age. For an age of 10 Myr, the distance to the system is 50 pc, the orbital period is 126 yr, and the masses of the components are ∼30 and ∼15 M Jup . The binary brown dwarf fills a so far unoccupied region in the parameters mass and age; it is a valuable new benchmark object for brown dwarf atmospheric and evolutionary models.
The associations between depressive symptoms, functional impairment, and quality of life, in patients with major depression: undirected and Bayesian network analyses

Abstract Background Depressive symptoms, functional impairment, and decreased quality of life (QOL) are three important domains of major depressive disorder (MDD). However, the possible causal relationship between these factors has yet to be elucidated. Moreover, it is not known whether certain symptoms of MDD are more impairing than others. The network approach is a promising solution to these shortfalls. Methods The baseline data of a multicenter prospective project conducted in 11 governances of China were analyzed. In total, 1385 patients with MDD were included. Depressive symptoms, functioning disability, and QOL were evaluated by the 17-item Hamilton Depression Rating Scale (HAMD-17), the Sheehan Disability Scale (SDS), and the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). The network was estimated through the graphical Least Absolute Shrinkage and Selection Operator (LASSO) technique in combination with the directed acyclic graph. Results Three centrality metrics of the graphical LASSO showed that social life dysfunction, QOL, and late insomnia exhibited the highest strength centrality. The network accuracy and stability were estimated to be robust and stable. The Bayesian network indicated that some depressive symptoms were directly associated with QOL, while other depressive symptoms showed an indirect association with QOL mediated by impaired function. Depressed mood was positioned at the highest level in the model and predicted the activation of functional impairment and anxiety. Conclusions Functional disability mediated the relationship between depressive symptoms and QOL. Family functionality and suicidal symptoms were directly related to QOL. Depressed mood played the predominant role in activating both anxiety symptom and functional impairment.
ACCRETION AND ORBITAL INSPIRAL IN GAS-ASSISTED SUPERMASSIVE BLACK HOLE BINARY MERGERS

Many galaxies are expected to harbor binary supermassive black holes (SMBHs) in their centers. Their interaction with the surrounding gas results in the accretion and exchange of angular momentum via tidal torques, facilitating binary inspiral. Here, we explore the non-trivial coupling between these two processes and analyze how the global properties of externally supplied circumbinary disks depend on the binary accretion rate. By formulating our results in terms of the angular momentum flux driven by internal stresses, we come up with a very simple classification of the possible global disk structures, which differ from the standard constant accretion disk solution. The suppression of accretion by the binary tides, leading to a significant mass accumulation in the inner disk, accelerates binary inspiral. We show that once the disk region strongly perturbed by the viscously transmitted tidal torque exceeds the binary semimajor axis, the binary can merge in less than its mass-doubling time due to accretion. Thus, unlike the inspirals driven by stellar scattering, the gas-assisted merger can occur even if the binary is embedded in a relatively low-mass disk (lower than its own mass). This is important for resolving the “last parsec” problem for SMBH binaries and understanding powerful gravitational wave sources in the universe. We argue that the enhancement of accretion by the binary found in some recent simulations cannot persist for a long time and should not affect the long-term orbital inspiral. We also review existing simulations of SMBH binary–disk coupling and propose a numerical setup which is particularly well suited to verifying our theoretical predictions.
Inositol 1,4,5‐trisphosphate receptor gene variants are related to the risk of breast cancer in a Chinese population

Mammalian inositol 1,4,5‐trisphosphate receptor (ITPR) genes encode ubiquitously expressed endoplasmic reticulum Ca2+ channels that have recently been shown to be closely linked to the pathogenesis of several cancers. However, few studies to date have explored associations between ITPR gene family single nucleotide polymorphisms (SNPs) and breast cancer risk.
Evaluating the use of semi-structured crowdsourced data to quantify inequitable access to urban biodiversity: A case study with eBird

Credibly estimating social-ecological relationships requires data with broad coverage and fine geographic resolutions that are not typically available from standard ecological surveys. Open and unstructured data from crowdsourced platforms offer an opportunity for collecting large quantities of user-submitted ecological data. However, the representativeness of the areas sampled by these data portals is not well known. We investigate how data availability in eBird, one of the largest and most popular crowdsourced science platforms, correlates with race and income of census tracts in two cities: Boston, MA and Phoenix, AZ. We find that checklist submissions vary greatly across census tracts, with similar patterns within both metropolitan regions. In particular, census tracts with high income and high proportions of white residents are most likely to be represented in the data in both cities, which indicates selection bias in eBird coverage. Our results illustrate the non-representativeness of eBird data, and they also raise deeper questions about the validity of statistical inferences regarding disparities that can be drawn from such datasets. We discuss these challenges and illustrate how sample selection problems in unstructured or semi-structured crowdsourced data can lead to spurious conclusions regarding the relationships between race, income, and access to urban bird biodiversity. While crowdsourced data are indispensable and complementary to more traditional approaches for collecting ecological data, we conclude that unstructured or semi-structured data may not be well-suited for all lines of inquiry, particularly those requiring consistent data coverage, and should thus be handled with appropriate care.
De novo transcriptome assembly and identification of G-Protein-Coupled-Receptors (GPCRs) in two species of monogenean parasites of fish

Genomic resources for Platyhelminthes of the class Monogenea are scarce, despite the diversity of these parasites, some species of which are highly pathogenic to their fish hosts. This work aimed to generate de novo-assembled transcriptomes of two monogenean species, Scutogyrus longicornis (Dactylogyridae) and Rhabdosynochus viridisi (Diplectanidae), providing a protocol for cDNA library preparation with low input samples used in single cell transcriptomics. This allowed us to work with sub-microgram amounts of total RNA with success. These transcriptomes consist of 25,696 and 47,187 putative proteins, respectively, which were further annotated according to the Swiss-Prot, Pfam, GO, KEGG, and COG databases. The completeness values of these transcriptomes evaluated with BUSCO against Metazoa databases were 54.1% and 73%, respectively, which is in the range of other monogenean species. Among the annotations, a large number of terms related to G-protein-coupled receptors (GPCRs) were found. We identified 109 GPCR-like sequences in R. viridisi, and 102 in S. longicornis, including family members specific for Platyhelminthes. Rhodopsin was the largest family according to GRAFS classification. Two putative melatonin receptors found in S. longicornis represent the first record of this group of proteins in parasitic Platyhelminthes. Forty GPCRs of R. viridisi and 32 of S. longicornis that were absent in Vertebrata might be potential drug targets. The present study provides the first publicly available transcriptomes for monogeneans of the subclass Monopisthocotylea, which can serve as useful genomic datasets for functional genomic research of this important group of parasites.
The Impact of Fractional Flow Reserve on Clinical Outcomes after Coronary Artery Bypass Grafting: A Meta-analysis

Background This meta-analysis was conducted to evaluate the effect of fractional flow reserve (FFR) on clinical outcomes after coronary artery bypass grafting (CABG). Methods Five online databases were searched for studies that (1) enrolled patients who underwent isolated CABG or CABG with aortic valve replacement and (2) demonstrated the effect of an FFR-guided strategy on major adverse cardiac events (MACE) after surgery based on a randomized controlled trial or adjusted analysis. MACE included cardiac death, acute myocardial infarction (MI), and repeated revascularization. The primary outcomes were all MACE outcomes and a composite of all-cause death and MI, and the secondary outcomes were the individual MACE outcomes. Publication bias was assessed using a funnel plot and the Egger test. Results Six articles (3 randomized and 3 non-randomized studies n=1,027) were selected. MACE data were extracted from 4 studies. The pooled analyses showed that the risk of MACE was not significantly different between patients who underwent FFR-guided CABG and those who underwent angiography-guided CABG (hazard ratio [HR], 0.80; 95% CI, 0.57–1.12). However, the risk of the composite of death or MI was significantly lower in patients undergoing FFR-guided CABG (HR, 0.62; 95% CI, 0.41–0.94). The individual MACE outcomes were not significantly different between FFR-guided and angiography-guided CABG. Conclusion FFR-guided CABG might be beneficial in terms of the composite outcome of death or MI compared with angiography-guided CABG although data are limited.