Miracle-dz/UniFAR-FTUs huggingface dataset:
Current advances in cell therapeutics: a biomacromolecules application perspective

ABSTRACT Introduction Many chronic diseases have evolved and to circumvent the limitations of using conventional drug therapies, smart cell encapsulating delivery systems have been explored to customize the treatment with alignment to disease longevity. Cell therapeutics has advanced in tandem with improvements in biomaterials that can suitably deliver therapeutic cells to achieve targeted therapy. Among the promising biomacromolecules for cell delivery are those that share bio-relevant architecture with the extracellular matrix and display extraordinary compatibility in the presence of therapeutic cells. Interestingly, many biomacromolecules that fulfil these tenets occur naturally and can form hydrogels. Areas covered This review provides a concise incursion into the paradigm shift to cell therapeutics using biomacromolecules. Advances in the design and use of biomacromolecules to assemble smart therapeutic cell carriers is discussed in light of their pivotal role in enhancing cell encapsulation and delivery. In addition, the principles that govern the application of cell therapeutics in diabetes, neuronal disorders, cancers and cardiovascular disease are outlined. Expert opinion Cell therapeutics promises to revolutionize the treatment of various secretory cell dysfunctions. Current and future advances in designing functional biomacromolecules will be critical to ensure that optimal delivery of therapeutic cells is achieved with desired biosafety and potency.
Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning

The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes is important for assessing visual function correlates and treatment guidance in macular disease. In this study, the “PRE U-net” is introduced as a novel approach for a fully automated fovea centralis detection, addressing the localization as a pixel-wise regression task. 2D B-scans are sampled from each image volume and are concatenated with spatial location information to train the deep network. A total of 5586 OCT volumes from 1,541 eyes were used to train, validate and test the deep learning method. The test data is comprised of healthy subjects and patients affected by neovascular age-related macular degeneration (nAMD), diabetic macula edema (DME) and macular edema from retinal vein occlusion (RVO), covering the three major retinal diseases responsible for blindness. Our experiments demonstrate that the PRE U-net significantly outperforms state-of-the-art methods and improves the robustness of automated localization, which is of value for clinical practice.
Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R1 and R2 * in healthy young adults

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R1), and effective transverse relaxation rate (R2*). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R1, and R2* differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R1 and R2* showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R1 and R2* might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.
An experimental investigation of the grazing flow impedance duct at the University of Florida for acoustic liner applications

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AN EXPERIMENTAL INVESTIGATION OF THE GRAZING FLOW IMPEDANCE DUCT AT THE UNIVERSITY OF FLORIDA FOR ACOUSTIC LINER APPLICATIONS By Brandon L. Bertolucci December 2012 Chair: Louis Cattafesta III Cochair: Mark Sheplak Major: Aerospace Engineering Acoustic liners remain the standard method for reducing environmental noise emanating from aircraft engine nacelles. To aid in liner testing and development, facilities capable of accurately educing the acoustic impedance in the presence of mean flow analogous to an aircraft engine are required. A facility was built to facilitate novel design approaches and studies into fundamental acoustic liner flow physics. This thesis details the design, testing, and implementation of the Grazing Flow Impedance Duct (GFID) as an experimental test bench capable of educing the impedance of an acoustic liner. Each component of the facility is discussed detailing the specific features applicable toward the facility as a grazing flow acoustic test bench. Characterization of both the underlying fluid dynamics and acoustics are tested to determine the abilities and inherent limitations associated with the facility. Comparisons are made to similar facilities. Finally, an acoustic liner provided by NASA is tested under grazing flow conditions and the impedance educed by applying the single mode method with favorable results. The thesis ends with an exploratory investigation of the drag impact of an acoustic liner through three indirect velocity profile methods.
Improved Techniques for Online Review Spam Detection

The rapid upsurge in the number of e-commerce websites, has made the internet, an extensive source of product reviews. Since there is no scrutiny regarding the quality of the review written, anyone can basically write anything which conclusively leads to Review Spams. There has been an advance in the number of Deceptive Review Spams - fictitious reviews that have been deliberately fabricated to seem genuine. In this work, we have delved into both supervised as well as unsupervised methodologies to identify Review Spams. Improved techniques have been proposed to assemble the most effective feature set for model building. Sentiment Analysis and its results have also been integrated into the spam review detection. Some well known classifiers have been used on the tagged dataset in order to get the best performance. We have also used clustering approach on an unlabelled Amazon reviews dataset. From our results, we compute the most decisive and crucial attributes which lead us to the detection of spam and spammers. We also suggest various practices that could be incorporated by websites in order to detect Review Spams.
Automatic Defect Recognition Method of Aluminium Profile Surface Defects

Automatic defect detection has important implications to intelligent manufacturing which could be used for the precise quality control of different products. However, the diverse aluminium profile surface defects present the characteristics of micro defects and different sizes. Conventional handcrafted-based methods and machine learning-based methods have limited feature expression ability which cause relatively poor detection performance. Recently, with the stronger feature extraction ability, deep learning has got wide applications on defect detection and recognition. Due to the loss information caused by pooling operations, it still exists a certain drawbacks on multi-scale object detection. To address this issue, with the residual neural network (ResNet), a new deep defect recognition network is proposed in this paper for aluminium profile surface defects to construct an end-to-end defect detection scheme. An attention fusion model is proposed to improve the detection precision on multi-scale defects. Experiments show that the proposed defect detection method shows a better detection performance compared with other advanced detection models.
PID Parameter Tuning Based On Adaptive Dynamic Programming

The traditional PID control algorithm requires tuning and optimization to achieve better control performance in the nonlinear time-delay system, which complicates the controller design. We proposed a new self-tuning and optimization algorithm for controller parameters based on Adaptive Dynamic Programming(ADP). The algorithm uses neural networks to approximate the performance index functions and control strategies in dynamic programming to achieve online self-tuning and optimization of control parameters. Using ADP and Genetic algorithms tuning Proportional-Integral-Derivative(PID) control parameters and comparing the results can demonstrate the feasibility and effectiveness of our proposed approach.
A Deep Learning Approach for Multi-copter Detection using mm-Wave Radar Sensors: Application of Deep Learning for Multi-copter detection using radar micro-Doppler signatures

The increasing number of affordable drones and their misuse calls for the need of new multi-copter detection technologies. Such new technologies shall enable the detection of drones flying in restricted regions, for instance in military zones. Therefore, multiple detection techniques have been developed, mainly using camera sensors and digital imaging. Since cameras however suffer the problems of inapplicability in bad weather and low light conditions, radar systems have been recently developed for multi-copter detection using various conventional detection algorithms. Radar systems enable collecting rotor-specific data. Due to the fact that all rotor-objects show similar characteristics in the Radar Doppler spectrogram, i.e. the so called Micro-Doppler signatures, Machine Learning classification techniques on radar collected data enable reaching better and more reliable detection results when compared to the conventional algorithms. This paper introduces a Deep-Learning-based technique that can be used to detect multi-copters. The main idea is making use of the micro-Doppler properties of multi-copter and applying Deep Learning approaches for the automation of the classification. Due to their rotating components, rotor-wing aircrafts induce distinct Radar micro-Doppler signatures. In this work, experimental measurements of radar micro-Doppler signatures for both cases: rotating wing-copters and various other non-rotating objects are detected using continuous wave (CW) Radar. Radar micro-Doppler signature images are collected, and then further processed and used for detecting the various multi-rotor objects. Detection is performed using a trained convolutional neural network (CNN).
Enhancing Local Service Effectiveness Through Purpose-Oriented Networks: The Role of Network Leadership and Structure

The public administration literature has demonstrated that the manner in which a network is led is related to its effectiveness. However, why this relationship occurs and whether it is dependent on external circumstances remain unclear. Relying on Provan and Milward's framework of interorganizational network effectiveness and the neo-institutional approach to local leadership, we propose that the manner in which the local authority leads the local network is related to the network's structure, which, in turn, influences its effectiveness. We also propose that this association is contingent on the characteristics of the local authority in which the network exists. Using a sample of 586 network participants from 68 Israeli networks, we demonstrate that the degree to which the local authority is centralized explains the link between the network's leadership and its effectiveness, but only in local authorities with a high socioeconomic status (SES). In addition, we find that in Israeli Arab municipalities such centralization is counterproductive, impeding the network's effectiveness. We discuss the theoretical and practical implications of these findings.
White Managers, Ethnoracism, and the Production of Black Ethnic Labor Market Disparities

Few have considered the role of White managers in longstanding Black ethnic labor market disparities. Drawing on ethnoracism theory, I conceptualize the previously documented White manager preference for Afro Caribbeans as a form of prejudice that contributes to the relative success of Afro Caribbeans. White managers say they prefer Afro Caribbeans because they work harder and are less racially antagonistic than African Americans. However, using the National Survey of American Life, I show that these populations are virtually indistinguishable in terms of labor quality and racial attitudes. Moreover, net labor quality and racial attitudes, the incomes of English and non-English speaking Afro Caribbeans are greater when working for White managers, but African Americans with White managers receive no greater income than those without a White manager. I conclude with a call for the formal development of a new ontological framework for the study of these kinds of ethnoracially dynamic relationships.
The Phosphatase Bph and Peptidyl-Prolyl Isomerase PrsA Are Required for Gelatinase Expression and Activity in Enterococcus faecalis

In Enterococcus faecalis, gelatinase (GelE) is a virulence factor that is also important for biofilm formation and interactions with other microbes as well as the host immune system. The long-standing model for GelE production is that the Fsr quorum sensing system positively regulates expression of gelE. ABSTRACT Enterococcus faecalis is a common commensal bacterium in the gastrointestinal tract as well as a frequent nosocomial pathogen. The secreted metalloprotease gelatinase (GelE) is an important E. faecalis virulence factor that contributes to numerous cellular activities, such as autolysis, biofilm formation, and biofilm-associated antibiotic resistance. Expression of gelE has been extensively studied and is regulated by the Fsr quorum sensing system. Here, we identify two additional factors regulating gelatinase expression and activity in E. faecalis OG1RF. The Bph phosphatase is required for expression of gelE in an Fsr-dependent manner. Additionally, the membrane-anchored protein foldase PrsA is required for GelE activity, but not fsr or gelE gene expression. Disrupting prsA also leads to increased antibiotic sensitivity in biofilms independent of the loss of GelE activity. Together, our results expand the model for gelatinase production in E. faecalis, which has important implications for fundamental studies of GelE function in Enterococcus and also E. faecalis pathogenesis. IMPORTANCE In Enterococcus faecalis, gelatinase (GelE) is a virulence factor that is also important for biofilm formation and interactions with other microbes as well as the host immune system. The long-standing model for GelE production is that the Fsr quorum sensing system positively regulates expression of gelE. Here, we update that model by identifying two additional factors that contribute to gelatinase production. The biofilm-associated Bph phosphatase regulates the expression of gelE through Fsr, and the peptidyl-prolyl isomerase PrsA is required for production of active GelE through an Fsr-independent mechanism. This provides important insight into how regulatory networks outside of the fsr locus coordinate expression of gelatinase.
Developmental switch in prediction and adaptation to pain in human neonates

Habituation to recurrent non-threatening or unavoidable noxious stimuli is an important aspect of adaptation to pain and indicates the ability of the brain to encode expectation of imminent nociception. However, it is not known whether the newborn brain can predict and habituate to recurrent noxious inputs. We used electroencephalography to investigate changes in cortical microstates, which represent the complex sequential processing of noxious inputs, following repeated clinically-required heel lances in term and preterm infants. Noxious stimulus repetition decreased the engagement of early sensory-related microstates and associated behavioural and physiological responses in term infants, while preterm infants did not show signs of adaptation. Nevertheless, both groups displayed a switch between different microstates at longer latencies. These data suggests that the preterm brain is capable of encoding high-level contextual differences in pain, but cannot update its prediction, which allows for adaptation, emphasising the vulnerability of this population to recurrent pain.
A Lysozyme Murein Hydrolase with Broad-Spectrum Antibacterial Activity from Enterobacter Phage myPSH1140

Bacteriophages and bacteriophage-derived peptidoglycan hydrolases (endolysins) present promising alternatives for the treatment of infections caused by multidrug resistant Gram-negative and Gram-positive pathogens. In this study, Gp105, a putative lysozyme murein hydrolase from Enterobacter phage myPSH1140 was characterized in silico, in vitro as well as in vivo using the purified protein. ABSTRACT Bacteriophages and bacteriophage-derived peptidoglycan hydrolases (endolysins) present promising alternatives for the treatment of infections caused by multidrug resistant Gram-negative and Gram-positive pathogens. In this study, Gp105, a putative lysozyme murein hydrolase from Enterobacter phage myPSH1140 was characterized in silico, in vitro as well as in vivo using the purified protein. Gp105 contains a T4-type lysozyme-like domain (IPR001165) and belongs to Glycoside hydrolase family 24 (IPR002196). The putative endolysin indeed had strong antibacterial activity against Gram-negative pathogens, including E. cloacae, K. pneumoniae, P. aeruginosa, S. marcescens, Citrobacter sp. , and A. baumannii. Also, an in vitro peptidoglycan hydrolysis assay showed strong activity against purified peptidoglycans. This study demonstrates the potential of Gp105 to be used as an antibacterial protein to combat Gram-negative pathogens.
CyTOF-Lec: Single-cell Glycomics Analysis Reveals Glycan Features Defining Cells Differentially Susceptible to HIV

High-parameter single-cell phenotyping has enabled in-depth classification and interrogation of immune cells, but to date has not allowed for glycan characterization. Here, we develop CyTOF-Lec as an approach to simultaneously characterize many protein and glycan features of human immune cells at the single-cell level. We implemented CyTOF-Lec to compare glycan features between different immune subsets from blood and multiple tissue compartments, and to characterize HIV-infected cell cultures. Using bioinformatics approaches to distinguish preferential infection of cellular subsets from viral-induced remodeling, we demonstrate that HIV upregulates the levels of cell surface fucose and sialic acid in a cell- intrinsic manner, and that memory CD4+ T cells co-expressing high levels of fucose and sialic acid are highly susceptible to HIV infection. Sialic acid levels were found to distinguish memory CD4+ T cell subsets expressing different amounts of viral entry receptors, pro-survival factors, homing receptors, and activation markers, and to play a direct role in memory CD4+ T cells’ susceptibility to HIV infection. The ability of sialic acid to distinguish memory CD4+ T cells with different susceptibilities to HIV infection was experimentally validated through sorting experiments. Together, these results suggest that HIV remodels not only cellular proteins but also glycans, and that glycan expression can differentiate memory CD4+ T cells with vastly different susceptibility to HIV infection.
Distinct evolutionary trajectories of SARS-CoV-2-interacting proteins in bats and primates identify important host determinants of COVID-19

The COVID-19 pandemic is caused by SARS-CoV-2, a novel coronavirus that spilled from the bat reservoir. Despite numerous clinical trials and vaccines, the burden remains immense, and the host determinants of SARS-CoV-2 susceptibility and COVID-19 severity remain largely unknown. Signatures of positive selection detected by comparative functional-genetic analyses in primate and bat genomes can uncover important and specific adaptations that occurred at virus-host interfaces. Here, we performed high-throughput evolutionary analyses of 334 SARS- CoV-2 interacting proteins to identify SARS-CoV adaptive loci and uncover functional differences between modern humans, primates and bats. Using DGINN (Detection of Genetic INNovation), we identified 38 bat and 81 primate proteins with marks of positive selection. Seventeen genes, including the ACE2 receptor, present adaptive marks in both mammalian orders, suggesting common virus-host interfaces and past epidemics of coronaviruses shaping their genomes. Yet, 84 genes presented distinct adaptations in bats and primates. Notably, residues involved in ubiquitination and phosphorylation of the inflammatory RIPK1 have rapidly evolved in bats but not primates, suggesting different inflammation regulation versus humans. Furthermore, we discovered residues with typical virus-host arms-race marks in primates, such as in the entry factor TMPRSS2 or the autophagy adaptor FYCO1, pointing to host-specific in vivo important interfaces that may be drug targets. Finally, we found that FYCO1 sites under adaptation in primates are those associated with severe COVID-19, supporting their importance in pathogenesis and replication. Overall, we identified functional adaptations involved in SARS- CoV-2 infection in bats and primates, critically enlightening modern genetic determinants of virus susceptibility and severity. Key findings: Evolutionary history of 334 SARS-CoV-2 interacting proteins (VIPs) in bats and primates identifying how the past has shaped modern viral reservoirs and humans – results publicly-available in an online resource. Identification of 81 primate and 38 bat VIPs with signatures of adaptive evolution. The common ones among species delineate a core adaptive interactome, while the ones displaying distinct evolutionary trajectories enlighten host lineage-specific determinants. Evidence of primate specific adaptation of the entry factor TMPRSS2 pointing to its host- specific in vivo importance and predicting molecular interfaces. FYCO1 sites associated with severe COVID-19 in human (GWAS) display hallmarks of ancient adaptive evolution in primates, highlighting its importance in SARS-CoV-2 replication or pathogenesis and differences with the bat reservoir. Identification of adaptive evolution in the bat’s multifunctional RIPK1 at residues that may differentially regulate inflammation.