Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimized High-Throughput Immuno-Plaque Assay for SARS-CoV-2

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been identified as the causative agent of coronavirus disease 2019 and is capable of human-to-human transmission and rapid global spread. The rapid emergence and global spread of SARS-CoV-2 has encouraged the establishment of a rapid, sensitive, and reliable viral detection and quantification methodology. Here, we present an alternative assay, termed immuno-plaque assay (iPA), which utilizes a combination of plaque assay and immunofluorescence techniques. We have extensively optimized the conditions for SARS-CoV-2 infection and demonstrated the great flexibility of iPA detection using several antibodies and dual-probing with two distinct epitope-specific antibodies. In addition, we showed that iPA could be utilized for ultra-high-throughput viral titration and neutralization assay within 24 h and is amenable to a 384-well format. These advantages will significantly accelerate SARS-CoV-2 research outcomes during this pandemic period.
Inattention, Impulsivity, and Hyperactivity in Deaf Children Are Not Due to Deficits in Inhibitory Control, but May Reflect an Adaptive Strategy

The present study had two main aims: (1) to determine whether deaf children show higher rates of key behaviors of ADHD (inattentive, hyperactive, and impulsive behaviors) and of Conduct Disorder—CD—(disruptive, aggressive, or antisocial behaviors) than hearing children, also examining whether the frequency of these behaviors in deaf children varied based on cochlear implant (CI) use, type of school (regular vs. specific for deaf) and level of receptive vocabulary; and (2) to determine whether any behavioral differences between deaf and hearing children could be explained by deficits in inhibitory control. We measured behaviors associated with ADHD and CD in 34 deaf and hearing children aged 9–10 years old, using the revised Spanish version of the Conners scale. We then assessed inhibitory control ability using a computerized Stroop task and a short version of the Attention Network Test for children. To obtain a measure of the level of receptive vocabulary of the deaf children we used a Spanish version of the Carolina Picture Vocabulary Test for Deaf and hearing-impaired children. Deaf children showed significantly higher rates of behaviors associated with ADHD and CD, and over 85% of cases detected with high risk of ADHD-inattentive type in the entire present sample were deaf children. Further, in the group of deaf children a negative correlation was found between receptive vocabulary and frequency of disruptive, aggressive, or antisocial behaviors associated with CD. However, inhibitory control scores did not differ between deaf and hearing children. Our results suggested that the ADHD-related behaviors seen in deaf children were not associated with a deficit in inhibitory control, at least in the interference suppression subcomponent. An alternative explanation could be that these behaviors are reflecting an adaptive strategy that permits deaf children to access information from their environment which is not available to them via audition.
Tackling Domain-Specific Winograd Schemas with Knowledge-Based Reasoning and Machine Learning

The Winograd Schema Challenge (WSC) is a common-sense reasoning task that requires background knowledge. In this paper, we contribute to tackling WSC in four ways. Firstly, we suggest a keyword method to define a restricted domain where distinctive high-level semantic patterns can be found. A thanking domain was defined by key-words, and the data set in this domain is used in our experiments. Secondly, we develop a high-level knowledge-based reasoning method using semantic roles which is based on the method of Sharma [2019]. Thirdly, we propose an ensemble method to combine knowledge-based reasoning and machine learning which shows the best performance in our experiments. As a machine learning method, we used Bidirectional Encoder Representations from Transformers (BERT) [Kocijan et al., 2019]. Lastly, in terms of evaluation, we suggest a "robust" accuracy measurement by modifying that of Trichelair et al. [2018]. As with their switching method, we evaluate a model by considering its performance on trivial variants of each sentence in the test set.
Unsupervised Discovery of Disentangled Manifolds in GANs

As recent generative models can generate photo-realistic images, people seek to understand the mechanism behind the generation process. Interpretable generation process is beneficial to various image editing applications. In this work, we propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adversarial networks. We propose to learn the transformation from prior one-hot vectors representing different attributes to the latent space used by pre-trained models. Furthermore, we apply a centroid loss function to improve consistency and smoothness while traversing through different directions. We demonstrate the efficacy of the proposed framework on a wide range of datasets. The discovered direction vectors are shown to be visually corresponding to various distinct attributes and thus enable attribute editing.
The effects of the shook swarm technique on honey bee (Apis mellifera L.) colony productivity and honey quality

SummaryThe productivity of honey bee colonies and the quality of hive products may be greatly influenced by colony management. In beekeeping, shaking of the worker bees together with the queen into a clean empty hive is known as the shook swarm technique. In this study, the shook swarm technique was compared to standard beekeeping methods in relation to colony productivity and honey quality, including adult worker bee population, wax production, comb area, brood area and honey yield. In addition, the honey was examined to determine the content of water, proline, hydroxymethylfurfural, glucose, fructose, sucrose, diastase, electrical conductivity, invert sugar, fructose/glucose ratio, maltose, vitamin C and potassium. The naphthalene and pesticide contents were also measured. Shook swarm group colonies were smaller than those of the control group in terms of worker bee population, brood area and honey yield (P 0.05) and greater in terms of wax production (P<0.01).
Large-scale computational content analysis on magazines targeting men and women: the case of Argentina 2008-2018

ABSTRACT Differences in magazines content aimed specifically at women or men are a means to create and reproduce gender stereotypes. Novel computational tools allow to study differences in magazines content taking into account all available articles. In this study, we analyse the case of two Argentinian magazines published by the same publishing house over a decade (2008–2018), advertised by the publishing house as targeting women and men respectively. Using computational tools, we are able to analyse more than 24,000 articles, which would have been an impossible task using manual content analysis methodologies. With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify their different frequency between magazines over time. Then, we performed a word-frequency analysis to validate this methodology and extend the analysis to other subjects. Our results show that topics such as Family, Business and Women as sex objects present an initial bias that tends to disappear over time. Conversely, in Fashion and Science topics, the initial differences are maintained. Also, we identify a considerable increase in the use of words associated with feminism since 2015 and specifically the word abortion in 2018. Furthermore, we develop a website where everyone can perform additional analysis.
Young stellar population gradients in central cluster galaxies from NUV and optical spectroscopy

Central cluster galaxies are the largest and most massive galaxies in the Universe. Although they host very old stellar populations, several studies found the existence of blue cores in some BCGs indicating ongoing star formation. We analyse VLT/X-Shooter stacked spectra of 6 nearby massive central galaxies with high central velocity dispersions ($\sigma$>300 km/s) at different galactocentric distances. We quantify the young stellar population out to 4 kpc by fitting near-UV and optical absorption line indices with predictions of composite stellar populations modelled by an old and a young stellar component. We also use IMF-sensitive indices since these galaxies have been found to host a bottom-heavy IMF in their central regions. We derive negative young stellar populations gradients, with mass fractions of stars younger than 1 Gyr decreasing with galactocentric distance, from 0.70% within 0.8 kpc to zero beyond 2 kpc. We also measure the mass fraction in young stars for individual galaxies in the highest S/N central regions. All the galaxies have young components of less than one percent. Our results clearly suggest that the star formation in massive central cluster galaxies takes place in their galaxy cores (<2 kpc), which, with deeper gravitational potential wells, are capable of retaining more gas. Among the possible sources for the gas required to form these young stars, our results are consistent with an in-situ origin via stellar evolution, which is sufficient to produce the observed young stellar populations.
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.
A combination of two ELISA tests for nasopharyngeal carcinoma screening in endemic areas based on a case-control study

For populations with a high risk of nasopharyngeal carcinoma (NPC) in Guangdong province in southern China, mass screening is the first choice to prevent death from NPC. To improve the performance of NPC screening, we used a combination based on the IgA antibody against the Epstein-Barr virus (EBV) capsid antigen (VCA-IgA) and the IgA antibody against Epstein-Barr virus nuclear antigen 1 (EBNA1-IgA) to NPC screening by enzyme-linked immunosorbent assay (ELISA). A multiplication model was applied to measure the level of the combination. We evaluated the NPC screening effect of the markers.A case-control study was performed to assess the NPC screening effect of the markers. A total of 10,894 serum specimens were collected, including 554 samples from NPC patients and 10,340 samples from healthy controls. In the training stage, 640 subjects were randomly selected, including 320 NPC cases and 320 healthy controls. In the verification stage, 10,254 subjects were used to verify the NPC screening effect of the combination. Receiver operating characteristic (ROC) analysis was performed. In the verification stage, the combination achieved an sensitivity of 91.45%, a specificity of 93.45%, and an area under the ROC curve (AUC) of 0.978 (95% CI [0.968–0.987]). Compared with VCA-IgA and EBNA1-IgA individually, the combination had an improved screening performance. A probability (PROB) calculated by logistic regression model based on VCA-IgA and EBNA1-IgA was applied to NPC screening by ELISA in China. The AUC of the combination was a little bit larger than the PROB. There was a slight increase (3.13%) in the sensitivity of the combination compared to the sensitivity of the PROB, while the specificity was lower for the combination (92.50%) than for the PROB (95.94%). We successfully applied a combination of two ELISA tests based on VCA-IgA and EBNA1-IgA for NPC screening by using a multiplication model. The results suggested that the combination was effective and can be an option for NPC screening.
Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. This representation not only makes the solution space well-constrained but also enables our method to be solved iteratively with a recurrent framework, which greatly reduces the difficulty of learning. Second, we introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images so that our full framework can be trained end-to-end without ground truth supervision. Extensive experiments on several different datasets demonstrate that our proposed method outperforms the previous state-of-the-art by a large margin.
TGF-β-Induced Endothelial to Mesenchymal Transition Is Determined by a Balance Between SNAIL and ID Factors

Endothelial-to-mesenchymal transition (EndMT) plays an important role in embryonic development and disease progression. Yet, how different members of the transforming growth factor-β (TGF-β) family regulate EndMT is not well understood. In the current study, we report that TGF-β2, but not bone morphogenetic protein (BMP)9, triggers EndMT in murine endothelial MS-1 and 2H11 cells. TGF-β2 strongly upregulates the transcription factor SNAIL, and the depletion of Snail is sufficient to abrogate TGF-β2-triggered mesenchymal-like cell morphology acquisition and EndMT-related molecular changes. Although SLUG is not regulated by TGF-β2, knocking out Slug also partly inhibits TGF-β2-induced EndMT in 2H11 cells. Interestingly, in addition to SNAIL and SLUG, BMP9 stimulates inhibitor of DNA binding (ID) proteins. The suppression of Id1, Id2, or Id3 expression facilitated BMP9 in inducing EndMT and, in contrast, ectopic expression of ID1, ID2, or ID3 abrogated TGF-β2-mediated EndMT. Altogether, our results show that SNAIL is critical and indispensable for TGF-β2-mediated EndMT. Although SLUG is also involved in the EndMT process, it plays less of a crucial role in it. In contrast, ID proteins are essential for maintaining endothelial traits and repressing the function of SNAIL and SLUG during the EndMT process. These data suggest that the control over endothelial vs. mesenchymal cell states is determined, at least in part, by a balance between the expression of SNAIL/SLUG and ID proteins.
Indirect comparison between immunotherapy alone and immunotherapy plus chemotherapy as first-line treatment for advanced non-small cell lung cancer: a systematic review

Objectives Use of immune checkpoint inhibitors as first-line treatment for advanced (stage IIIB/IV) non-small cell lung cancer (NSCLC) remains controversial. Clinical trials comparing single-drug immunotherapy (IO) with immunotherapy plus chemotherapy (IC) are lacking. We aimed to compare the efficacy of IO alone with that of IC as first-line treatment for advanced NSCLC. Design Systematic review. Data sources PubMed, the Cochrane Library and Embase for related studies on NSCLC; ClinicalTrials.gov, American Society of Clinical Oncology Meeting Library and World Conference on Lung Cancer for relevant conference abstracts (to July 2019). Eligibility criteria Articles meeting the following criteria were selected: (1) randomised controlled trials on NSCLC treatment, (2) all individuals in the studies had not received treatment previously and (3) research on IO monotherapy using programmed death-1/programmed death ligand-1 (PD-L1) inhibitors or IC. Data extraction and synthesis After reading the original literature, two reviewers independently extracted the relevant information. The primary outcomes were progression-free survival (PFS), overall survival (OS) and objective response rate (ORR). We also extracted data on treatment-related adverse events and immune-related adverse events (irAEs). Results Overall, 10 randomised controlled clinical trials (n=5765) were included. As first-line treatment for advanced NSCLC, IC tended to yield better PFS, OS and ORR than did IO. Furthermore, IC yielded significantly better PFS than IO when tumour PD-L1 expression was at least 50% (HR: 1.81, 95% CI: 1.18 to 2.78) and yielded a better OS and PFS when tumour PD-L1 expression was at least 1%; IO resulted in fewer adverse events than did IC. However, the incidence of irAEs was higher for IO than for IC. Conclusions The findings of the indirect comparison indicate that IC as first-line treatment for advanced NSCLC is significantly more effective than IO in patients with PD-L1 expression in at least 50% of tumour cells. Trial registration number CRD 42018116589.
The relationship between coronary slow flow phenomenon and carotid femoral pulse wave velocity and aortic elastic properties

Introduction In this study, we aimed to investigate the relationship between coronary slow flow (CSF) and carotid-femoral pulse wave velocity (CFPWV). Methods 78 (27 women, mean age 43.95 ± 7.28) patients with CSF, and 70 (22 women, mean age 44.34 ± 7.08) healthy individuals were included in the study. Arterial stiffness measurement was performed to both groups via CFPWV, which is considered the gold standard. Aortic elastic properties (ASI-β and aortic distensibility) were evaluated in both groups. Results The CSF group had significantly higher CFPWV and aortic distensibility values and significantly lower ASI-β values compared to the control group. There was a positive correlation between TIMI frame count (TFC) obtained in all coronary arteries and CFPWV and aortic distensibility, and a negative correlation between TFC and ASI-β. It was determined that CFPWV predicted CSF with 97% specificity and 98% sensitivity at a 7.68 cut-off value (ROC area = 994, p < 0.001). ASI-β was determined to predict CSF with 64% specificity and 47% sensitivity at a 2.98 cut-off value (ROC area = 047, p < 0.001). Aortic distensibility was determined to predict CSF with 76% specificity and 79% sensitivity at a 3.94 cut-off value (ROC area = 706, p < 0.001). Conclusion Arterial stiffness increases in CSF patients, suggesting that CSF is a systemic pathology rather than a local disease and that a systemic cause such as atherosclerosis plays a role in etiology.
Secretory Pathway Kinase FAM20C, a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

Purpose Glioblastoma (GBM) is the most lethal primary cancer in adult central nervous system, and new strategies are desperately needed. The secretory pathway kinase or kinase-like proteins (SPKKPs) have been shown to mediate multiple physiological functions by phosphorylating extracellular proteins and proteoglycans. However, their roles in cancers, especially GBM, remain poorly defined. Methods The least absolute shrinkage and selection operator (LASSO) regression was employed for establishing the SPKKPs signature for IDH wild type (wt) GBM prognosis. Integrative analyses with multiple datasets were employed to identify the core member of this gene family in glioma. The receiver operator characteristic (ROC) curves and immunohistochemistry were further used for evaluating its association with progressive malignancy in glioma and GBM patients’ survival, respectively. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to interpret its functions in GBM, which were further verified in vitro. Results A SPKKPs classifier was constructed with 3 genes of this family. This signature could effectively distinguish IDH wt GBM survival. Family with sequence similarity 20 C (FAM20C) was further identified as the core member of this family in glioma. Elevated FAM20C expression was not only closely correlated with glioma malignancy progression and the mesenchymal subtype of GBM but also indicated unfavorable survival of GBM patients. FAM20C was also found to be associated with the disrupted immune response in GBM microenvironment and was required for the migration of glioma and immune cells. Conclusion These data indicate that the potential of FAM20C serving as a predictive molecule and a therapeutic target for GBM.
Indomethacin augments lipopolysaccharide-induced expression of inflammatory molecules in the mouse brain

Indomethacin and other non-steroidal anti-inflammatory drugs (NSAIDs) are used to relieve pain and fever including during infections. However, some studies suggest that NSAIDs protect against neuroinflammation, while some find no effects or worsening of neuroinflammation. We evaluated the effect of indomethacin alone on in combination with minocycline, a drug that inhibits neuroinflammation, on the expression of transcripts of neuroinflammatory molecules-induced by lipopolysaccharide (LPS) in the brain of mice. Inoculation of male BALB/c mice with LPS induced the expression of the microglia marker ionized calcium binding adaptor molecule protein, mRNA expression of the genes for cytokines interleukin-1beta (Il1b) and tumor necrosis factor-alpha (Tnf) and inducible nitric oxide synthase gene (Nos2), but not Il10, in the brain. Treatment with indomethacin had no significant effect on the cytokines or Nos2 mRNA expression in naïve animals. However, pretreatment with indomethacin increased LPS-induced Nos2 mRNA and inducible nitric oxide (iNOS) protein expression, but had no significant effect on LPS-induced mRNA expression of the cytokines. Minocycline reduced LPS-induced Il1b and Tnf, but not Nos2, mRNA expression. Treatment with indomethacin plus minocycline had no effect on LPS-induced Il1b, Tnf and Nos2 mRNA expression. In conclusion these results show that indomethacin significantly augments LPS-induced Nos2 mRNA and iNOS protein expression in the brain. In the presence of indomethacin, minocycline could not inhibit LPS-induced pro-inflammatory cytokine expression. Thus, indomethacin could exacerbate neuroinflammation by increasing the expression of iNOS and also block the anti-inflammatory effects of minocycline.