Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Kinase D promotes activity-dependent AMPA receptor endocytosis in hippocampal neurons

AMPA type glutamate receptors (AMPARs) mediate the majority of fast excitatory neurotransmission in the brain. The continuous trafficking of AMPARs into and out of synapses is a core feature of synaptic plasticity, which is considered as the cellular basis of learning and memory. The molecular mechanisms underlying the postsynaptic AMPAR trafficking, however, are still not fully understood. In this work, we demonstrate that the Protein Kinase D (PKD) family promotes basal and activity-induced AMPAR endocytosis in primary hippocampal neurons. Pharmacological inhibition of PKD increased synaptic levels of GluA1-containing AMPARs, slowed down their endocytic trafficking and increased neuronal network activity. By contrast, ectopic expression of constitutive active PKD decreased the synaptic level of AMPARs, while increasing their co-localization with early endosomes. On a molecular level, we identify phosphorylation of the Rab5 effector protein and PKD substrate Rabaptin-5 to be required for promoting activity-dependent AMPAR endocytosis. Our results thus establish an important role for PKD in the regulation of postsynaptic AMPAR trafficking during synaptic plasticity.
The SARS-CoV-2 receptor-binding domain elicits a potent neutralizing response without antibody-dependent enhancement

The SARS-coronavirus 2 (SARS-CoV-2) spike (S) protein mediates entry of SARS-CoV-2 into cells expressing the angiotensin-converting enzyme 2 (ACE2). The S protein engages ACE2 through its receptor-binding domain (RBD), an independently folded 197-amino acid fragment of the 1273-amino acid S-protein protomer. Antibodies to the RBD domain of SARS-CoV (SARS-CoV-1), a closely related coronavirus which emerged in 2002-2003, have been shown to potently neutralize SARS-CoV-1 S-protein-mediated entry, and the presence of anti-RBD antibodies correlates with neutralization in SARS-CoV-2 convalescent sera. Here we show that immunization with the SARS-CoV-2 RBD elicits a robust neutralizing antibody response in rodents, comparable to 100 µg/ml of ACE2-Ig, a potent SARS-CoV-2 entry inhibitor. Importantly, anti-sera from immunized animals did not mediate antibody-dependent enhancement (ADE) of S-protein-mediated entry under conditions in which Zika virus ADE was readily observed. These data suggest that an RBD-based vaccine for SARS-CoV-2 could be safe and effective.
The coordinated localization of mRNA to centrosomes facilitates error-free mitosis

Centrosomes are microtubule-organizing centers required for error-free mitosis and embryonic development. The microtubule-nucleating activity of centrosomes is conferred by the pericentriolar material (PCM), a composite of numerous proteins subject to cell cycle-dependent oscillations in levels and organization. In diverse cell types, mRNAs localize to centrosomes and may contribute to changes in PCM abundance. Here, we investigate the regulation of mRNA localization to centrosomes in the rapidly cycling Drosophila melanogaster embryo. We find that RNA localization to centrosomes is regulated during the cell cycle and developmentally. We identify a novel role for the fragile-X mental retardation protein (FMRP), which localizes to pericentrosomal RNA granules, in the post-transcriptional regulation of centrosomal RNA. Further, the mis-targeting of a model centrosomal mRNA, centrocortin (cen), is sufficient to alter cognate protein localization to centrosomes and impair spindle morphogenesis and genome stability.
Structural and functional analysis of a potent sarbecovirus neutralizing antibody

SARS-CoV-2 is a newly emerged coronavirus responsible for the current COVID-19 pandemic that has resulted in more than one million infections and 73,000 deaths1,2. Vaccine and therapeutic discovery efforts are paramount to curb the pandemic spread of this zoonotic virus. The SARS-CoV-2 spike (S) glycoprotein promotes entry into host cells and is the main target of neutralizing antibodies. Here we describe multiple monoclonal antibodies targeting SARS-CoV-2 S identified from memory B cells of a SARS survivor infected in 2003. One antibody, named S309, potently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 by engaging the S receptor-binding domain. Using cryo-electron microscopy and binding assays, we show that S309 recognizes a glycan-containing epitope that is conserved within the sarbecovirus subgenus, without competing with receptor attachment. Antibody cocktails including S309 along with other antibodies identified here further enhanced SARS-CoV-2 neutralization and may limit the emergence of neutralization-escape mutants. These results pave the way for using S309 and S309-containing antibody cocktails for prophylaxis in individuals at high risk of exposure or as a post-exposure therapy to limit or treat severe disease.
Establishment of a Human Induced Pluripotent Stem Cell-Derived Neuromuscular Co-Culture Under Optogenetic Control

The failure of the neuromuscular junction (NMJ) is a key component of degenerative neuromuscular disease, yet how NMJs degenerate in disease is unclear. Human induced pluripotent stem cells (hiPSCs) offer the ability to model disease via differentiation toward affected cell types, however, the re-creation of an in vitro neuromuscular system has proven challenging. Here we present a scalable, all-hiPSC-derived co-culture system composed of independently derived spinal motor neurons (MNs) and skeletal myotubes (sKM). In a model of C9orf72-associated disease, co-cultures form functional NMJs that can be manipulated through optical stimulation, eliciting muscle contraction and measurable calcium flux in innervated sKM. Furthermore, co-cultures grown on multi-electrode arrays (MEAs) permit the pharmacological interrogation of neuromuscular physiology. Utilization of this co-culture model as a tunable, patient-derived system may offer significant insights into NMJ formation, maturation, repair, or pathogenic mechanisms that underlie NMJ dysfunction in disease.
The Spatial and Cell-Type Distribution of SARS-CoV-2 Receptor ACE2 in the Human and Mouse Brains

By engaging angiotensin-converting enzyme 2 (ACE2 or Ace2), the novel pathogenic SARS-coronavirus 2 (SARS-CoV-2) may invade host cells in many organs, including the brain. However, the distribution of ACE2 in the brain is still obscure. Here we investigated the ACE2 expression in the brain by analyzing data from publicly available brain transcriptome databases. According to our spatial distribution analysis, ACE2 was relatively highly expressed in some important locations, such as the substantia nigra and the choroid plexus. According to our cell-type distribution analysis, nuclear expression of ACE2 was found in many neurons (both excitatory and inhibitory neurons) and some non-neuron cells (mainly astrocytes, oligodendrocytes and endothelial cells) in human middle temporal gyrus and posterior cingulate cortex. A few ACE2-expressing nuclei were found in a hippocampal dataset, and none were detected in the prefrontal cortex. Except for the additional high expression of Ace2 in the olfactory bulb areas for spatial distribution as well as in the pericytes and endothelial cells for cell-type distribution, the distribution of Ace2 in mouse brain was similar to that in the human brain. Thus, our results reveal an outline of ACE2/Ace2 distribution in the human and mouse brain, which indicates the brain infection of SARS-CoV-2 may be capable of inducing central nervous system symptoms in coronavirus disease 2019 (COVID-19) patients.
Poor sensing maximises microbial fitness when few out of many signals are sensed

An open problem in biology is to understand when particular adaptation strategies of microorganisms are selected during evolution. They range from random, bet-hedging strategies to deterministic, responsive strategies, relying on signalling circuits. We present an evolutionary model that integrates basic statistical physics of molecular circuits with fitness maximisation and information theory. This model provides an explanation for a puzzling observation on responsive strategies: the accuracy with which signalling networks track external signals seems remarkably low. Single cells often distinguish only between 2 to 4 concentration ranges, corresponding to 1 or 2 bits of mutual information between signal and response. Why did evolution lead to such low-fidelity signalling systems? Our theory offers an explanation by taking a novel perspective. It considers the fitness benefit of all signals, including those that are not sensed. We introduce a new concept, ‘latent information’, which captures the mutual information between all non-sensed signals and the optimal response. The theory predicts that it is often evolutionarily optimal to transduce sensed signals noisily when latent information is present. It indicates that fitness can indeed be maximal when the mutual information extracted from sensed signals is not maximal, but rather has a low value of about 1 or 2 bits. Cells likely do not sense all signals because of the fitness cost of expressing idle signalling systems that consume limited biosynthetic resources. Our theory illustrates that as the total available information about the optimal behaviour decreases, the cell should trust the available information less, and gamble more.Surprisingly, microorganisms appear to sense only very few environmental signals (such as nutrients and stresses) compared to the number of conditions they can encounter. Even worse, the signals they do sense are transduced at low fidelity. We study the accuracy of sensing in the situation where multiple signals determine the optimal response, but only few signals are actually sensed. We show that it is in fact to be expected that sensing in the presence of latent information should be imprecise, and that signalling circuits should underperform.
A GATA6-centred gene regulatory network involving HNFs and ΔNp63 controls plasticity and immune escape in pancreatic cancer

Objective Molecular taxonomy of tumours is the foundation of personalised medicine and is becoming of paramount importance for therapeutic purposes. Four transcriptomics-based classification systems of pancreatic ductal adenocarcinoma (PDAC) exist, which consistently identified a subtype of highly aggressive PDACs with basal-like features, including ΔNp63 expression and loss of the epithelial master regulator GATA6. We investigated the precise molecular events driving PDAC progression and the emergence of the basal programme. Design We combined the analysis of patient-derived transcriptomics datasets and tissue samples with mechanistic experiments using a novel dual-recombinase mouse model for Gata6 deletion at late stages of KRasG12D-driven pancreatic tumorigenesis (Gata6LateKO). Results This comprehensive human-to-mouse approach showed that GATA6 loss is necessary, but not sufficient, for the expression of ΔNp63 and the basal programme in patients and in mice. The concomitant loss of HNF1A and HNF4A, likely through epigenetic silencing, is required for the full phenotype switch. Moreover, Gata6 deletion in mice dramatically increased the metastatic rate, with a propensity for lung metastases. Through RNA-Seq analysis of primary cells isolated from mouse tumours, we show that Gata6 inhibits tumour cell plasticity and immune evasion, consistent with patient-derived data, suggesting that GATA6 works as a barrier for acquiring the fully developed basal and metastatic phenotype. Conclusions Our work provides both a mechanistic molecular link between the basal phenotype and metastasis and a valuable preclinical tool to investigate the most aggressive subtype of PDAC. These data, therefore, are important for understanding the pathobiological features underlying the heterogeneity of pancreatic cancer in both mice and human.
MARCH8 inhibits viral infection by two different mechanisms

Membrane-associated RING-CH 8 (MARCH8) inhibits infection with both HIV-1 and vesicular stomatitis virus G-glycoprotein (VSV-G)-pseudotyped viruses by reducing virion incorporation of envelope glycoproteins. The molecular mechanisms by which MARCH8 targets envelope glycoproteins remain unknown. Here, we show two different mechanisms by which MARCH8 inhibits viral infection. Viruses pseudotyped with the VSV-G mutant, in which cytoplasmic lysine residues were mutated, were insensitive to the inhibitory effect of MARCH8, whereas those with a similar lysine mutant of HIV-1 Env remained sensitive to it. Indeed, the wild-type VSV-G, but not its lysine mutant, was ubiquitinated by MARCH8. Furthermore, the MARCH8 mutant, which had a disrupted cytoplasmic tyrosine motif that is critical for intracellular protein sorting, did not inhibit HIV-1 Env-mediated infection, while it still impaired infection by VSV-G-pseudotyped viruses. Overall, we conclude that MARCH8 reduces viral infectivity by downregulating envelope glycoproteins through two different mechanisms mediated by a ubiquitination-dependent or tyrosine motif-dependent pathway.
Porcine reproductive and respiratory syndrome virus dissemination across pig production systems in the United States

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widespread in the North American pig population. Despite improvements in virus characterization, it is unclear whether PRRSV infections are a product of viral circulation within a farm, within production systems (local) or across production systems (external). Here we examined the dissemination dynamics of PRRSV and the processes facilitating its spread within and among pig farms in three production systems. Overall, PRRSV genetic diversity declined since 2018, while phylodynamic results support frequent transmission across-production systems. We found that PRRSV dissemination occurred mostly through transmission between farms of different production companies, which were predominant for several months, especially from November until May when PRRSV tends to peak in the studied region. Within production systems, dissemination occurred mainly through regular pig flow (from sow to nursery and then to finisher farms); nevertheless, an important flux of PRRSV dissemination from finisher to sow and nursery farms highlighted the importance of downstream farms as sources of the virus. Farms at areas with pig density from 500 to 1000 pig/km2 and farms located at a range within 0.5 km and 0.7 km from major roads were more likely to infect by PRRSV, whereas farms at elevation between 41 and 61 meters and denser vegetation acted as dissemination barriers. Although remains a challenge, there is a need to disentangle the route of PRRSV transmission, results evidenced that dissemination among commercially unrelated pig production systems was intense, reinforcing the importance of farm proximity on PRRSV spread. Thus, consideration of farm location and their geographic characteristics may help to forecast dissemination. The understanding of PRRSV transmission routes has the potential to inform targeted strategies for its prevention and control. Further studies are needed to quantify the relative contribution of PRRSV transmission routes.
Who Loves Virtue as much as He Loves Beauty?: Deep Learning based Estimator for Aesthetics of Portraits

: ”I have never seen one who loves virtue as much as he loves beauty,” Confucius once said. If beauty is more important as goodness, it becomes clear why people invest so much effort in their ﬁrst im-pression. The aesthetic of faces has many aspects and there is a strong correlation to all characteristics of humans, like age and gender. Often, research on aesthetics by social and ethic scientists lacks sufﬁcient labelled data and the support of machine vision tools. In this position paper we propose the Aesthetic-Faces dataset, containing training data which is labelled by Chinese and German annotators. As a combination of three image subsets, the AF-dataset consists of European, Asian and African people. The research communi-ties in machine learning, aesthetics and social ethics can beneﬁt from our dataset and our toolbox. The toolbox provides many functions for machine learning with state-of-the-art CNNs and an Extreme-Gradient-Boosting regressor, but also 3D Morphable Model technologies for face shape evaluation and we discuss how to train an aesthetic estimator considering culture and ethics.
Multi-stream Architecture with Symmetric Extended Visual Rhythms for Deep Learning Human Action Recognition

: Despite the signiﬁcant progress of Deep Learning models on the image classiﬁcation task, it still needs enhancements for the Human Action Recognition task. In this work, we propose to extract horizontal and vertical Visual Rhythms as well as their data augmentations as video features. The data augmentation is driven by crops extracted from the symmetric extension of the time dimension, preserving the video frame rate, which is essential to keep motion patterns. The crops provide a 2D representation of the video volume matching the ﬁxed input size of a 2D Convolutional Neural Network. In addition, multiple crops with stride guarantee coverage of the entire video. We veriﬁed that the combination of horizontal and vertical directions leads do better results than previous methods. A multi-stream strategy combining RGB and Optical Flow information is modiﬁed to include the additional spatiotemporal streams: one for the horizontal Symmetrically Extended Visual Rhythm (SEVR), and another for the vertical one. Results show that our method achieves accuracy rates close to the state of the art on the challenging UCF101 and HMDB51 datasets. Furthermore, we assessed the impact of data augmentations methods for Human Action Recognition and veriﬁed an increase of 10% for the UCF101 dataset.
Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN

: Depth estimation plays a vital role in many computer vision tasks including scene understanding and reconstruction. However, it is an ill-posed problem when it comes to estimating the depth from a single view due to the ambiguity and the lack of cues and prior knowledge. Proposed solutions so far estimate blurry depth images with low resolutions. Recently, Convolutional Neural Network (CNN) has been applied successfully to solve different computer vision tasks such as classiﬁcation, detection, and segmentation. In this paper, we present a simple fully-convolutional encoder-decoder CNN for estimating depth images from a single RGB image with the same image resolution. For robustness, we leverage a non-convex loss function which is robust to the outliers to optimize the network. Our results show that a light simple model trained using a robust loss function outperforms or achieves comparable results with other methods quantitatively and qualitatively and produces better depth information of the scenes with sharper objects’ boundaries. Our model predicts the depth information in one shot with the same input resolution and without any further post-processing steps.
DA-NET: Monocular Depth Estimation using Disparity Maps Awareness NETwork

Estimating depth from 2D images has become an active field of study in autonomous driving, scene reconstruction , 3D object recognition, segmentation, and detection. Best performing methods are based on Convolutional Neural Networks, and, as the process of building an appropriate set of data requires a tremendous amount of work, almost all of them rely on the same benchmark to compete between each other : The KITTI benchmark. However, most of them will use the ground truth generated by the LiDAR sensor which generates very sparse depth map with sometimes less than 5% of the image density, ignoring the second image that is given for stereo estimation. Recent approaches have shown that the use of both input images given in most of the depth estimation data set significantly improve the generated results. This paper is in line with this idea, we developed a very simple yet efficient model based on the U-NET architecture that uses both stereo images in the training process. We demonstrate the effectiveness of our approach and show high quality results comparable to state-of-the-art methods on the KITTI benchmark.
Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3

: Automatic measurement of functional trait data from digitized herbarium specimen images is of great interest as traditionally, scientists extract such information manually, which is time-consuming and prone to errors. One challenging task in the automated measurement process of functional traits from specimen images is the existence of other objects such as scale-bar, color pallet, specimen label, envelopes, bar-code and stamp, which are mostly placed at different locations on the herbarium-mounting sheet and require special detection method. To detect automatically all these objects, we train a model based on an improved YOLO V3 full-regression deep neural network architecture, which has gained obvious advantages in both speed and accuracy through capturing deep and high-level features. We made some improvements to adjust YOLO V3 for detecting object from digitized herbarium specimen images. A new scale of feature map is added to the existing scales to improve the detection effect on small targets. At the same time, we adopted the fourth detection layer by a 4* up-sampled layer instead of 2* to get a feature map with higher resolution deeper level. The experimental results indicate that our model performed better with mAP-50 of 93.2% compared to 90.1% mean IoU trained by original YOLO V3 model on the test set.
Improving Mood for People with Depressive Disorders: Designing and Developing a VR Game

: Mood disorders can have a significant psychological impact on many groups of people. It causes severe disease burden and can lead to many effects that decrease the quality of life of an individual. Video games are popular forms of entertainment which can help improve a person’s mood and decrease their depressive symptoms. In the paper, we design and develop a prototype VR game to help people with depressive disorders improve their mood. We have performed preliminary testing showing encouraging results in improving peoples’ moods.
Localizing Visitors in Natural Sites Exploiting Modality Attention on Egocentric Images and GPS Data

: Localizing the visitors of an outdoor natural site can be advantageous to study their behavior as well as to provide them information on where they are and what to visit in the site. Despite GPS can generally be used to perform outdoor localization, we show that this kind of signal is not always accurate enough in real-case scenarios. On the contrary, localization based on egocentric images can be more accurate but it generally results in more expensive computation. In this paper, we investigate how fusing image-and GPS-based predictions can allow to achieve efﬁcient and accurate localization of the visitors of a natural site. Speciﬁcally, we compare different fusion techniques, including a modality attention approach which is shown to provide the best performances. Results point out that the proposed technique achieve promising results, allowing to obtain the performances of very deep models (e.g., DenseNet) with a less expensive architecture (e.g., SqueezeNet) which employ a memory footprint of about 3 MB and an inference speed of about 25 ms .