Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrient signaling pathways regulate amyloid clearance and synaptic loss in Alzheimer’s disease

Extra-cellular accumulation of Amyloid-β (Aβ) plaques is causatively associated with Alzheimer’s disease (AD). However, mechanisms that mediate the pre-pathological state of amyloid plaque formation remain elusive. Here, using paired RNAi and kinase inhibitor screens, we discovered that AKT-mediated insulin/nutrient signaling suppresses lysosomal clearance of Aβ and promotes amyloid formation. This mechanism is cell-autonomous and functions in multiple systems, including iPSC-derived human neurons and in vivo. Nutrient signaling regulates amyloid formation via distinct lysosomal functional mechanisms, while enhanced amino acid signaling promotes amyloid formation by transcriptionally suppressing lysosome biogenesis, and high intracellular cholesterol levels suppress lysosomal clearance of amyloid by increasing the number of non-functional lysosomes. The nutrient signaling pathway, present in both neurons and microglia, regulates lysosomal clearance of amyloid and microglia mediated synapse loss, both in vitro and in vivo. Clinically, older hyperlipidemic patients showed less synapse loss through microglia and performed better in cognitive tests. Thus, our results reveal a bi-partite cellular quality control system regulated by the insulinnutrient signaling that in neurons regulates Aβ peptide clearance and in microglia regulates synaptic loss, both processes causally associated with AD. Our results also caution against reducing amyloid through such processes as this might also result in synapse loss.
Decoding and blocking the bacterial lipid flippase MprF yields a novel anti-virulence strategy

The pandemic of antibiotic resistance represents a major human health threat demanding new antimicrobial strategies. MprF is synthase and flippase of the phospholipid lysyl-phosphatidylglycerol that increases virulence and resistance of methicillin-resistant Staphylococcus aureus (MRSA) and other pathogens. With the aim to design an anti-virulence strategy for staphylococci causing minimal antibiotic selection pressure, we developed MprF-targeting monoclonal antibodies which bound and inhibited the MprF flippase. Antibody M-C7.1 targeted a loop in the enigmatic flippase domain that proved to be exposed at both sides of the bacterial membrane, thereby providing mechanistic insights into bacterial lipid translocation. M-C7.1 rendered MRSA susceptible to host antimicrobial peptides and antibiotics such as daptomycin. Moreover, it impaired MRSA survival in human phagocytes and virulence in a murine infection model, which recommends MprF inhibitors for new anti-MRSA approaches. MprF-directed monoclonal antibodies provide a proof of concept for development of precisely targeting anti-virulence drugs which block bacterial defense mechanisms.
Preferred auditory temporal processing regimes and auditory-motor interactions

Decoding the rich temporal dynamics of complex sounds such as speech is constrained by the underlying neuronal processing mechanisms. Oscillatory theories suggest the existence of one optimal perceptual performance regime at auditory stimulation rates in the delta to theta range (<10 Hz), but reduced performance in the alpha range (10-14 Hz) is controversial. Additionally, the widely discussed motor system contribution to timing remains unclear. We measured rate discrimination thresholds between 4-15 Hz, and auditory-motor coupling strength was estimated through auditory-motor synchronization. In a Bayesian model comparison, high auditory-motor synchronizers showed a larger range of constant optimal temporal judgments than low synchronizers, with performance decreasing in the alpha range. This evidence for optimal auditory processing in the theta range is consistent with preferred oscillatory regimes in auditory cortex that compartmentalize stimulus encoding and processing. The findings suggest, remarkably, that increased auditory-motor interaction might extend such an optimal range towards faster rates.
Properdin oligomers adopt rigid extended conformations supporting function

Properdin stabilizes convertases formed upon activation of the complement cascade within the immune system. The biological activity of properdin depends on the oligomerization state, but whether properdin oligomers are rigid and how their structure links to function remains unknown. We show by combining electron microscopy and solution scattering, that properdin oligomers adopt extended rigid and well-defined conformations that are well approximated by single models of apparent n-fold rotational symmetry with dimensions of 23-36 nm. Properdin monomers are pretzel shaped molecules with limited flexibility. In solution, properdin dimers are curved molecules whereas trimers and tetramers are close to being planar molecules. Structural analysis indicates that simultaneous binding through all binding sites to surface linked convertases is unlikely for properdin trimer and tetramers. We show that multivalency alone is insufficient for full activity in a cell lysis assay. Hence, the observed rigid extended oligomer structure is an integral component of properdin function.
Structure of HIV-1 gp41 with its membrane anchors targeted by neutralizing antibodies

The HIV-1 gp120/gp41 trimer undergoes a series of conformational changes in order to catalyze gp41-induced fusion of viral and cellular membranes. Here, we present the crystal structure of gp41 locked in a fusion intermediate state by an MPER-specific neutralizing antibody. The structure illustrates the conformational plasticity of the six membrane anchors arranged asymmetrically with the fusion peptides and the transmembrane regions pointing into different directions. Hinge regions located adjacent to the fusion peptide and the transmembrane region facilitate the conformational flexibility that allows high affinity binding of broadly neutralizing anti-MPER antibodies. Molecular dynamics simulation of the MPER Ab-induced gp41 conformation reveals the transition into the final post-fusion conformation with the central fusion peptides forming a hydrophobic core with flanking transmembrane regions. This, thus, suggests that MPER-specific broadly neutralizing antibodies can block final steps of refolding of the fusion peptide and the transmembrane region, which is required for completing membrane fusion.
Own Motivation, Peer Motivation, and Educational Success

I study how motivation shapes own and peers' educational success. Using data from Project STAR, I find that academic motivation in early elementary school, as measured by a standardized psychological test, predicts contemporaneous and future test scores, high school GPA, and college-test taking over and above cognitive skills. Exploiting random assignment of students to classes, I find that exposure to motivated classmates causally affects contemporaneous reading achievement, a peer effect that operates over and above spillovers from classmates' past achievement and socio-demographic composition. However, peer motivation does not affect longer-term educational success, likely because it does not change own motivation.
Diversidad de vertebrados marino costeros de la Provincia Weddelliana en un horizonte basal (Ypresiano, Eoceno) del Alomiembro Cucullaea I, Formación La Meseta, isla Seymour (Marambio), Antártida

The La Meseta Formation crops out in Seymour/Marambio Island, Weddell Sea, northeast of the Antarctic Peninsula and contains one of the world's most diverse assemblages of Weddellian marine/coastal verte-brates of Early Eocene (Ypresian) age. The La Meseta Formation is composed of poorly consolidated, marine sandstones and siltstones which were deposited in a coastal, deltaic and/or estuarine environment. It includes marine invertebrates and vertebrates as well as terrestrial vertebrates and plants. The highly fossiliferous basal horizon (Cucullaeashell bed, Telm 4 of Sadler 1988) of the CucullaeaI Allomember is a laterally extensive shell bed with sandy matrix. The fish remains, including 35 species from 26 families, of the Ypresian Cucullaeabed represent one of the most abundant and diverse fossil vertebrate faunas yet recorded in southern latitudes. Stratigraphic distribution and phylogenetic relationships of the Weddellian sphenisciforms are consistent with a first radiation of this group in the Early Eocene. The first inquestionable archaeocete from Antarctica is recorded in this unit and is referred to a new taxon.
Intranasal Administration of ACIS KEPTIDE™ Prevents SARS-CoV2-Induced Acute Toxicity in K18-hACE2 Humanized Mouse Model of COVID-19: A Mechanistic Insight for the Prophylactic Role of KEPTIDE™ in COVID-19

Previously, we have demonstrated that ACIS KEPTIDE™, a chemically modified peptide, selectively binds to ACE-2 receptor and prevents the entry of SARS-CoV2 virions in vitro in primate kidney Cells. However, it is not known if ACIS KEPTIDE™ attenuates the entry of SARS-CoV2 virus in vivo in lung and kidney tissues, protects health, and prevent death once applied through intranasal route. In our current manuscript, we demonstrated that the intranasal administration of SARS-CoV2 (1*106) strongly induced the expression of ACE-2, promoted the entry of virions into the lung and kidney cells, caused acute histopathological toxicities, and mortality (28%). Interestingly, thirty-minutes of pre-treatment with 50 μg/Kg Body weight ACIS normalized the expression of ACE-2 via receptor internalization, strongly mitigated that viral entry, and prevented mortality suggesting its prospect as a prophylactic therapy in the treatment of COVID-19. On the contrary, the peptide backbone of ACIS was unable to normalize the expression of ACE-2, failed to improve the health vital signs and histopathological abnormalities. In summary, our results suggest that ACIS is a potential vaccine-alternative, prophylactic agent that prevents entry of SARS-CoV2 in vivo, significantly improves respiratory health and also dramatically prevents acute mortality in K18-hACE2 humanized mice. Highlights ACIS KEPTIDE stimulates the internalization of ACE-2 receptor (Fig. 2) and buffers the membrane localization of ACE-2 receptors (Fig. 2, 6 & 8). Intranasal inoculation of SARS-CoV2 upregulates the expression of ACE-2 in lung epithelium (Fig.6) and kidney tubular cells (Fig.8). ACIS KEPTIDE normalizes the expression of ACE-2 in the kidney tubular cells of virus-treated K18-hACE2mice (Fig. 8). ACIS KEPTIDE™ completely prevents the entry of SARS-CoV2 in Bronchiolar epithelium (Fig.6), alveolar parenchyma (Fig. 6), and kidney tubular cells (Fig.8). ACIS KEPTIDE™ improves the pulmonary (Fig. 5) and renal pathological changes (Fig. 7) caused by the SARS-CoV2 virus insult. Intranasal administration of 0.05% Beta-propiolactone (βPL)-inactivated SARS-CoV2 (1 *106) causes significant death (28%) in K18-hACE2 humanized mice after 24 hrs of intranasal inoculation (Supplemental videos) suggesting that SARS-CoV2 does not require its infective properties and genetic mechanism to be functional to cause mortality. The peptide backbone of ACIS KEPTIDE™ provides much less and insignificant protection in the prevention of pathological changes in Lungs (Fig.5 & 6) and Kidney (Fig.7 & 8). Peptide failed to normalize the upscaled expression of ACE-2 in kidney tubular cells (Fig.8) of SARS-CoV2-treated K18-hACE2 mice.
A Ubiquitous and Accurate Floor Estimation System Using Deep Representational Learning

Location-based services have undergone massive improvements over the last decade. Despite intense efforts in industry and academia, a pervasive infrastructure-free localization is still elusive. Towards making this possible, cellular-based systems have recently been proposed due to the wide-spread availability of the cellular networks and their support by commodity cellphones. However, these systems only consider locating the user in a 2D single floor environment, which reduces their value when used in multi-story buildings. In this paper, we propose CellRise, a deep learning-based system for floor identification in multi-story buildings using ubiquitous cellular signals. Due to the inherent challenges of leveraging the large propagation range and the overlap in the signal space between horizontal and vertical user movements, CellRise provides a novel module to generate floor-discriminative representations. These representations are then fed to a recurrent neural network that learns the sequential changes in signals to estimate the user floor level. Additionally, CellRise incorporates different modules that improve the deep model's generalization against avoiding overtraining and noise. These modules also permit CellRise to generalize to floors completely unseen during training. We have implemented and evaluated CellRise using two different buildings with a side-by-side comparison with the state-of-the-art floor estimation techniques. The results show that CellRise can accurately estimate the exact user's floor 97.7% of the time and within one floor error 100% of the time. This is better than the state-of-the-art systems by at least 17.9% in floor identification accuracy. In addition, we show that CellRise has robust performance in various challenging conditions.
Ad-hocBusPoI: Context Analysis of Ad-hoc Stay-locations from Intra-city Bus Mobility and Smartphone Crowdsensing

Public city bus services across various developing cities inhabit multiple stay-locations on the routes due to ad-hoc bus stops to provide on-demand passenger boarding and alighting services. Characterizing these stay-locations is essential to correctly develop models for bus transit patterns used in various digital navigation services. In this poster, we create a deep learning-driven methodology to characterize ad-hoc stay-locations over bus routes based on crowd-sensing contextual information. Experiments over 720km of bus travel data in a semi-urban city in India indicate promising results from the model in terms of good detection accuracy.
ILRUN Downregulates ACE2 Expression and Blocks Infection of Human Cells by SARS-CoV-2

There is no doubt that the current rapid global spread of COVID-19 has had significant and far-reaching impacts on our health and economy and will continue to do so. Research in emerging infectious diseases, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is growing rapidly, with new breakthroughs in the understanding of host-virus interactions to assist with the development of innovative and exciting therapeutic strategies. ABSTRACT The human protein-coding gene ILRUN (inflammation and lipid regulator with UBA-like and NBR1-like domains; previously C6orf106) was identified as a proviral factor for Hendra virus infection and was recently characterized to function as an inhibitor of type I interferon expression. Here, we have utilized transcriptome sequencing (RNA-seq) to define cellular pathways regulated by ILRUN in the context of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of Caco-2 cells. We find that inhibition of ILRUN expression by RNA interference alters transcription profiles of numerous cellular pathways, including upregulation of the SARS-CoV-2 entry receptor ACE2 and several other members of the renin-angiotensin aldosterone system. In addition, transcripts of the SARS-CoV-2 coreceptors TMPRSS2 and CTSL were also upregulated. Inhibition of ILRUN also resulted in increased SARS-CoV-2 replication, while overexpression of ILRUN had the opposite effect, identifying ILRUN as a novel antiviral factor for SARS-CoV-2 replication. This represents, to our knowledge, the first report of ILRUN as a regulator of the renin-angiotensin-aldosterone system (RAAS). IMPORTANCE There is no doubt that the current rapid global spread of COVID-19 has had significant and far-reaching impacts on our health and economy and will continue to do so. Research in emerging infectious diseases, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is growing rapidly, with new breakthroughs in the understanding of host-virus interactions to assist with the development of innovative and exciting therapeutic strategies. Here, we present the first evidence that modulation of the human protein-coding gene ILRUN functions as an antiviral factor for SARS-CoV-2 infection, likely through its newly identified role in regulating the expression of SARS-CoV-2 entry receptors ACE2, TMPRSS2, and CTSL. These data improve our understanding of biological pathways that regulate host factors critical to SARS-CoV-2 infection, contributing to the development of antiviral strategies to deal with the current SARS-CoV-2 pandemic.
COVID-19 Detection System using Recurrent Neural Networks

Lately, an immense amount of work has been done by people working on the frontlines, such as hospitals, clinics, and laboratories, alongside researchers and scientists who are also making considerable efforts in the fight against the COVID-19 epidemic. Due to the unconscionable dissemination of the disease, the implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the basics of Automatic Speech Recognition (ASR) and deep learning algorithms. In this study, we highlight the importance of speech signal processing in the process of early screening and diagnosing the COVID-19 virus by utilizing the Recurrent Neural Network (RNN) and specifically its significant well-known architecture, the Long Short-Term Memory (LSTM) for analyzing the acoustic features of cough, breathing, and voice of the patients. Our results show a low accuracy in the voice test compared to both coughing and breathing sound samples. Moreover, our results are preparatory, and there is a possibility to enhance the accuracy of the voice tests by expanding the data set and targeting a larger group of healthy and infected people.
Improved droop control based on multi-stage lead-lag compensation

The droop control and virtual synchronous generator control are currently the two most popular inverter control strategies, but both of these control strategies cannot be satisfied at the same time: (1) Frequency stability in stand-alone (SA) mode. (2) In the grid-connected (GC) mode, the output active power quickly follows the input power command. (3) Anti-interference ability of inverter output frequency in gridconnected mode. In response to these deficiencies, this paper proposes an improved droop control based on multi-stage leadlag compensation. This control strategy adds a lag link to the frequency output link of the traditional droop control to provide virtual inertia for the system; multi-stage lead lag compensation is added to the active power output section to improve the dynamic characteristics in the grid-connected mode. In the GC state, the improved droop control effectively reduces the power impact when the power command changes, and accelerates the power response speed. Enhanced the anti-interference ability of output frequency. In SA state, improved droop control can provide sufficient inertia and damping characteristics. Finally, the simulation results verify the effectiveness of the proposed improved droop control.
Highly Efficient and Scalable Multi-hop Ride-sharing

On-demand ride-sharing services such as Uber and Lyft have gained tremendous popularity over the past decade, largely driven by the omnipresence of mobile devices. Ride-sharing services can provide economic and environmental benefits such as reducing traffic congestion and vehicle emissions. Multi-hop ride-sharing enables passengers to transfer between vehicles within a single trip, which significantly extends the benefits of ride-sharing and provides ride opportunities that are not possible otherwise. Despite its advantages, offering real-time multi-hop ride-sharing services at large scale is a challenging computational task due to the large combination of vehicles and passenger transfer points. To address these challenges, we propose exact and approximation algorithms that are scalable and achieve real-time responses for highly dynamic ride-sharing scenarios in large metropolitan areas. Our experiments on real-world datasets show the benefits of multi-hop ride-sharing services and demonstrate that our proposed algorithms are more than two orders of magnitude faster than the state-of-the-art. Our approximation algorithms offer a comparable trip quality to our exact algorithm, while improving the ride-sharing request matching time by another order of magnitude.
Self-Organizing Maps to Assess Rehabilitation Progress of Post-Stroke Patients

The scarcity of adequate rehabilitation and treatment centers for post-stroke patients, a relatively common disease among the Egyptian populace, and the lack of awareness and trained physiotherapists, causes many patients to forgo treatment until they are transported to the hospital. Even then, the high cost of treatment will impede most rehabilitation attempts to those who survive. Thankfully, rehabilitation robotics can be used to replace the need for trained physiotherapists. This paper uses the Myo armband as a rehabilitation assessment device, tracking the progress of Post-Stroke patients and comparing them with healthy subjects. By taking a total of 60 samples from 3 healthy subjects and using self-organizing maps, a clustering system that can differentiate between regular and irregular motions using kinematic data with less than 10% error was produced.
On the restricted size Ramsey number for P3 versus dense connected graphs

Let F , G and H be simple graphs. A graph F is said a ( G,H )-arrowing graph if in any red-blue coloring of edges of F we can find a red G or a blue H . The size Ramsey number of G and H , ŕ ( G , H ), is the minimum size of F . If the order of F equals to the Ramsey number of G and H , r ( G,H ), then the minimum size of F is called the restricted size Ramsey number of G and H , r * ( G,H ). The Ramsey number of G and H , r ( G,H ), is the minimum order of F . In this paper, we study the restricted size number involving a P 3 . The value of r * ( P 3 , K n ) has been given by Faudree and Sheehan. Here, we examine r * ( P 3 , H ) where H is dense connected graph.
SODA²:Salient Object Detection With Structure-Adaptive & Scale-Adaptive Receptive Field

Salient objects with complex shapes and arbitrary sizes are generally hard to detect, especially in cluttered background and complex scenes. Noticing the deficiency of single-feature-based methods, recent methods fused multiple features from Deep Neural Networks (DNNs) and obtained better performance thanks to the hierarchical feature representations. However, existing works processed the features on each semantic level with convolutions of certain fixed scale and regular sampling locations, deteriorating the predicted salient objects with complex shapes and arbitrary sizes. To tackle these problems, in this article, we propose a novel dual-adaptive salient object detection algorithm with structure-adaptive and scale-adaptive receptive field (abbreviated as SODA2). In particularly, the deformable convolution is introduced to transform the kernels’ sampling locations by augmenting extra offsets. Upon the structure-adaptive features on different semantic level, novel spatial context-aware modules (SCAMs) are devised to capture multi-scale contexts with dilated convolutions of deliberately designed dilation rates and kernel sizes. To further emphasize on saliency-relevant semantic information (e.g., humans), we proposed a region-of-interest-aware channel attention module (ROI-CAM) to redistribute the feature maps on different channels. Extensive experiments have been carried out on five challenging datasets and our algorithm performs favorably compared with state-of-the-art algorithms, validating the efficiency of the proposed models.