Miracle-dz/UniFAR-FTUs huggingface dataset:
Defective autophagy in neurodegeneration: Novel roles for huntingtin and optineurin in regulating autophagosome dynamics

Autophagy is an essential cellular degradative process that has been implicated in the pathogenesis of several neurodegenerative diseases including Huntington's disease and Amyotrophic Lateral Sclerosis (ALS). During autophagy, autophagosomes form around cargo such as mitochondria, and subsequently fuse with lysosomes to acidify and acquire enzymes to degrade internalized cargos. In neurons, constitutive autophagosome biogenesis preferentially occurs at the axon tip, followed by the robust retrograde axonal transport of autophagosomes back to the cell body. The mechanisms regulating both the axonal transport of autophagosomes and the selective degradation of damaged mitochondria have not yet been determined. Here, I report novel roles for huntingtin and optineurin in regulating these dynamics and show that this regulation is disrupted in models of neurodegenerative disease. Using live cell imaging of primary neurons, I demonstrate that huntingtin regulates autophagosome retrograde axonal transport via its interactions with dynein and the motor adaptor protein HAP1 (huntingtin-associated protein 1). Loss of either huntingtin or HAP1 disrupts autophagosome transport. We also find that expression of the polyglutamine expansion in huntingtin (polyQhtt) which leads to Huntington's disease disrupts autophagosome transport, resulting in reduced autophagosome motility and inefficient cargo degradation. These observations support a model in which robust autophagosome transport is required for efficient lysosomal encounters along the axon; inhibition of this transport prevents efficient degradation of internalized cargos. To further explore the mechanism regulating autophagy, I also examined the dynamics of selective mitochondrial degradation during PINK1 (PTEN-induced putative kinase 1)/parkin-dependent mitophagy. These studies identified optineurin as a novel autophagy receptor for damaged mitochondria. Optineurin is recruited to the outer mitochondrial membrane (OMM) following parkin-mediated ubiquitination of OMM proteins. Optineurin binds to ubiquitinated proteins via its UBAN domain, and subsequently recruits the autophagosome protein LC3 via its LC3 interacting region (LIR). This pathway is disrupted by either loss of optineurin or an ALS-associated E478G mutation in optineurin's ubiquitin binding domain, leading to inefficient mitochondrial degradation. Together, these studies provide new insights into the mechanisms driving autophagy and mitophagy, and further demonstrate that defects in autophagy may contribute to pathogenesis in both Huntington's disease and familial ALS. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Neuroscience First Advisor Erika L. Holzbaur This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1165
A Comparative Analysis of Phosphodiesterase 5 Inhibitors and Testosterone in Men with Erectile Dysfunction and Low Testosterone

Problem under investigation: Although guidelines for the management of erectile dysfunction (ED) and testosterone deficiency exists, the treatment type in men with low testosterone is variable. Objective: To compare the effectiveness of mono and combination treatments of oral medications and testosterone therapy in men with ED and low testosterone and evaluate age, systolic blood pressure (SBP), testosterone level and body mass index (BMI) by treatment type. Theoretical framework: The life course health development model was used as the framework for this project. Method: A retrospective chart review at a urology practice in Western New York was performed to evaluate self-reported treatment effectiveness. Descriptive statistics, chi square testing, and analysis of variance were performed to analyze data. Results: The subjects were older (56%), had high SBP (64%), and obese (68%). There was no significance between treatment type and effectiveness, but combination therapy was more effective than monotherapy. There was no significant difference in mean age, SBP, testosterone level and BMI by treatment type. Conclusion: Combination therapy was clinically more effective than monotherapy for ED with low testosterone. There was no significance in mean difference in age, SBP, testosterone level or BMI for treatment type.
Genomic signature and toxicogenomics comparison of polycationic gene delivery nanosystems in human alveolar epithelial A549 cells

Background and the purpose of the study: Of the gene delivery systems, non-viral polycationic gene delivery nanosystems have been alternatively exploited as a relatively safe delivery reagents compared to viral vectors. However, little is known about the genomic impacts of these delivery systems in target cells/tissues. In this study, the toxicogenomics and genotoxicity potential of some selected polycationic lipid/polymer based nanostructures (i.e., Oligofectamine® (OF), starburst polyamidoamine Polyfect® (PF) and diaminobutane (DAB) dendrimers) were investigated in human alveolar epithelial A549 cells. Methods: To study the nature and the ontology of the gene expression changes in A549 cells upon treatment with polycationic nanostructures, MTT assay and microarray gene expression profiling methodology were employed. For microarray analysis, cyanine (Cy3/Cy5) labeled cDNA samples from treated and untreated cells were hybridized on target arrays housing 200 genes. Results and major conclusions: The polycationic nanosystems induced significant gene expression changes belonging to different genomic ontologies such as cell defence and apoptosis pathways. These data suggest that polycationic nanosystems can elicit multiple gene expression changes in A549 cells upon their chemical structures and interactions with cellular/subcellular components. Such impacts may interfere with the main goals of the desired genemedicine.
Contingent Valuation Method (cvm) for the preservation/restoration of three lakes in northern Greece

The preservation/restoration of natural environment is frequently entailing excessive cost (paid by people through taxation) while it is a source of additional income for both, the State and the people, due to tourism. Since the evaluation of this good cannot be in market terms, we apply herein a modified version of the Contingent Valuation Method (CVM), used in Experimental Economics to investigate the significance that people put on this good and how much they might be willing to pay (WTP) for supporting activities concerning the preservation/restoration of three lakes in northern Greece. The WTP dependence on (i) external diseconomies, (ii) the expectations for property values’ rise as a result of the restoration, (iii) the proximity of interviewees’ residence to the lakes, and (iv) the time and money that the interviewees spent to visit the lakes, are estimated by means of Logit, Probit, Logistic and Linear Regression Models.
Scalable task clean-up assignment for multi-agents

This paper describes a group of robots for cleaning a simulated environment and proposes an ecient algorithm for navigation based on Path nding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is e ective in cleaning. In order to test its eciency it was compared the combination of the Path nding A* algorithm and the decision algorithm proposed in this paper with Path nding A* and Euclidean distance, resulted in an improvement in time and distance traveled.
Chicken-specific peptide arrays for kinome analysis: Flight for the flightless

Kinomics, the study of kinase enzymes within an organism, is a rapidly growing field of proteomics. The use of high-throughput technology to study the kinome has enabled researchers to conduct studies of the global signaling environment within an organism. The problem arises when researchers interested in non-human, non-mouse species attempt to use these latest techniques for their species of interest. A recent advancement that has overcome this species problem is the species-specific peptide array. Custom tailored to the species of interest, this high-throughput kinome technology allows researchers to study global cellular signaling events in nearly any organism that uses phosphorylation-mediated signal transduction. Specific to this review is the study of the chicken, which has never been a more important or relevant research species. There are a number of basic biological questions about chickens that can be answered through new experimental techniques. In addition, zoonotic diseases, like avian influenza and Salmonella, which can infect humans through interaction with infected animals, have shown avians to be an important infectious vector. While the significant limitations to the mouse model have become more and more apparent, researchers have turned to alternative species such as chicken which are relatively easy to care for, inexpensive, and are suited to large scale studies. The chicken is an ideal candidate for in ovo developmental studies, as well as models for certain infectious agents. Finally, issues of food safety and agricultural antibiotic use are ever present in the media and public policy discussions. The development of research tools to find safer means of animal production and alternatives to antibiotics are going to be increasingly important research objectives in the years to come.
High-occupancy vehicle lanes on the right: an alternative design for congestion reduction at freeway merge, diverge, and weaving areas

ABSTRACT High-occupancy vehicle (HOV) lanes have been widely deployed in freeways with high demands and traffic congestion. This paper discusses how an alternative design with the HOV lane on the right side of the freeway could effectively mitigate congestion problems at freeway merge, diverge, and weaving areas as compared with the benchmark design of HOV lanes on the left. Simulation experiments show that the alternative design will result in a reduction in average delay by 5% to 10% and increase in throughput by 10% to 30% for all traffic. While the alternative design generally improves performance for the general purpose (GP) lanes, it will degrade HOV lane performance. Nonetheless, HOV lane vehicles enjoy a higher level of service than GP lane vehicles under the same background settings. The study suggests that it is practical to implement the alternative design on freeways, especially for areas with low HOV traffic volumes.
A Mathematical Assessment of the Isolation Tree Method for Outliers Detection in Big Data

In this paper, the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection is presented. We show that the IRF space can be endowed with a probability induced by the Isolation Tree algorithm (iTree). In this setting, the convergence of the IRF method is proved using the Law of Large Numbers. A couple of counterexamples are presented to show that the original method is inconclusive and no quality certificate can be given, when using it as a means to detect anomalies. Hence, an alternative version of IRF is proposed, whose mathematical foundation, as well as its limitations, are fully justified. Finally, numerical experiments are presented to compare the performance of the classic IRF with the proposed one.
Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images

In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for cadastre maps extraction, the errors in form of misalignment is a common problem. In this work, we propose an end-to-end deep learning approach which is able to solve inconsistencies between the input intensity image and the available building footprints by correcting label noises and, at the same time, misalignments if needed. The obtained results demonstrate the robustness of the proposed method to even severely misaligned examples that makes it potentially suitable for real applications, like OpenStreetMap correction.
The sustained post-outburst brightness of Nova Per 2018, the evolved companion, and the long orbital period

Nova Per 2018 (= V392 Per) halted the decline from maximum when it was 2 mag brighter than quiescence and since 2019 has been stable at such a plateau. The ejecta have already fully diluted into the interstellar space. We obtained BVRIgrizY photometry and optical spectroscopy of V392 Per during the plateau phase and compared it with equivalent data gathered prior to the nova outburst. We find the companion star (CS) to be a G9 IV/III and the orbital period to be 3.4118 days, making V392 Per the longest known period for a classical nova. The location of V392 Per on the theoretical isochrones is intermediate between that of classical novae and novae erupting within symbiotic binaries, in a sense bridging the gap. The reddening is derived to be EB − V = 0.72 and the fitting to isochrones returns a 3.6 Gyr age for the system and 1.35 M⊙, 5.3 R⊙, and 15 L⊙ for the companion. The huge Ne overabundance in the ejecta and the very fast decline from nova maximum both point to a massive white dwarf (WD) (MWD ≥ 1.1−1.2 M⊙). The system is viewed close to pole-on conditions and the current plateau phase is caused by irradiation of the CS by the WD still burning at the surface.
Internet of Drones: a Survey on Communications, Technologies, Protocols, Architectures and Services

The Internet of Drones (IoD) recently gained momentum due to its high adaptability to a wide variety of complex scenarios. Indeed, Unmanned Aerial Vehicles (UAVs) can successfully be employed in different application scenarios, like agriculture, search and rescue missions, surveillance systems, mission-critical services, etc., thanks to some technological and practical advantages: high mobility, capability to extend wireless coverage areas, or ability to reach places inaccessible to humans. Moreover, the employment of drones promisingly improves different network architectures performance indexes, i.e., reliability, connectivity, throughput, delay. Nevertheless, the adoption of networks of drones gives rise to several issues related to the unreliability of the wireless medium, batteries lifetime, high mobility degree and frequent topology changes. Moreover, security and privacy issues need to be properly investigated. This explains the very large number of works produced in the recent literature on IoD-related topics. With respect to other surveys on IoD-related topics, this work categorizes the multifaceted aspects of IoD, proposing a classification of the IoD environment. The proposed classification approach develops along two main directions. At a macroscopic level, it follows the structure of the Internet protocol stack, starting from the physical layer and extending to the upper layers, without neglecting crosslayer and optimization approaches. At a finer level, all the most relevant works belonging to each layer of the stack are further classified, according to the different issues peculiar of the layer, and highlighting the most relevant differences with the other surveys present in literature. Finally, a discussion on the main research challenges and possible future directions is carried out, focusing on the open issues and the most promising technologies in the IoD field.
Mechanics of the Scarf Premaxilla-Nasal Suture in the Snout of Lystrosaurus

SANDRA C. JASINOSKI,*1 1 EMILY J. RAYFIELD,2 and ANUSUYA CHINSAMY3; department of Earth Sciences, University of Bristol, Wills Memorial Building, Queen's Road, Bristol, BS8 1RJ, United Kingdom, sandra.j as@hotmail.com; 2Department of Earth Sciences, University of Bristol, Wills Memorial Building, Queen's Road, Bristol, BS8 1RJ, United Kingdom, E.Rayfield@bristol.ac. uk; 3Department of Zoology, University of Cape Town, Private Bag X3, Cape Town 7701, South Africa, Anusuya.Chinsamy-Turan@uct.ac. za
Comparison of populations of the wilt pathogen Ceratocystis albifundus in South Africa and Uganda

Ceratocystis albifundus is an important fungal pathogen of Acacia mearnsii trees in South Africa. In a previous study, a high level of gene diversity was demonstrated in a South African population of C. albifundus . This, together with the occurrence of the pathogen on native Protea species and its exclusive occurrence in South Africa, led to the hypothesis that C. albifundus is probably native to that country. More recently, C. albifundus has been reported from A. mearnsii in south-western Uganda. The aim of this study was to compare the populations of C. albifundus from Uganda and South Africa based on genetic diversity, population structure and possible gene flow. This was achieved using codominant microsatellite markers developed for the closely related species Ceratocystis fimbriata . Available isolates for comparison were from six different areas of South Africa and six jungle stands in Uganda. Eight of the 11 available markers amplified loci in C. albifundus . Gene diversity was higher in the Ugandan population, but genotypic diversity was greater for the South African isolates. There were no common genotypes between the two populations and they shared only 22% of the total alleles. The populations were genetically isolated from each other and highly substructured within. There was no association between isolates collected from the same geographic locations, and gene flow between the two populations was low. Results suggest that C. albifundus was probably not introduced into Uganda from South Africa but rather that an ancestral population, yet to be discovered, is the source of both populations.
The impact of unresolved magnetic spots on high-precision radial velocity measurements

The Doppler method of exoplanet detection has been extremely successful, but suffers from contaminating noise from stellar activity. In this work a model of a rotating star with a magnetic field based on the geometry of the K2 star Epsilon Eridani is presented and used to estimate its effect on simulated radial velocity measurements. A number of different distributions of unresolved magnetic spots were simulated on top of the observed large-scale magnetic maps obtained from eight years of spectropolarimetric observations. The radial velocity signals due to the magnetic spots have amplitudes of up to 10 m s$^{-1}$, high enough to prevent the detection of planets under 20 Earth masses in temperate zones of solar type stars. We show that the radial velocity depends heavily on spot distribution. Our results emphasize that understanding stellar magnetic activity and spot distribution is crucial for detection of Earth analogues.
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration

Popularity bias is a well-known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail items overall. The effectiveness of these approaches, however, has not been assessed in multistakeholder environments where in addition to the users who receive the recommendations, the utility of the suppliers of the recommended items should also be considered. In this paper, we propose the concept of popularity calibration which measures the match between the popularity distribution of items in a user's profile and that of the recommended items. We also develop an algorithm that optimizes this metric. In addition, we demonstrate that existing evaluation metrics for popularity bias do not reflect the performance of the algorithms when it is measured from the perspective of different stakeholders. Using music and movie datasets, we empirically show that our approach outperforms the existing state-of-the-art approaches in addressing popularity bias by calibrating the recommendations to users' preferences. We also show that our proposed algorithm has a secondary effect of improving supplier fairness.
Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion

In a broad range of computer vision applications, the purpose of Low-rank matrix approximation (LRMA) models is to recover the underlying low-rank matrix from its degraded observation. The latest LRMA methods - Robust Principal Component Analysis (RPCA) resort to using the nuclear norm minimization (NNM) as a convex relaxation of the non-convex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We use a more flexible model, namely the Weighted Schatten p-Norm Minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives a better approximation to the original low-rank assumption but also considers the importance of different rank components. In this paper, a comparison of the low-rank recovery performance of two LRMA algorithms- RPCA and WSNM is brought out on occluded human facial images. The analysis is performed on facial images from the Yale database and over own database , where different facial expressions, spectacles, varying illumination account for the facial occlusions. The paper also discusses the prominent trends observed from the experimental results performed through the application of these algorithms. As low-rank images sometimes might fail to capture the details of a face adequately, we further propose a novel method to use the image-histogram of the sparse images thus obtained to identify the individual in any given image. Extensive experimental results show, both qualitatively and quantitatively, that WSNM surpasses RPCA in its performance more effectively by removing facial occlusions, thus giving recovered low-rank images of higher PSNR and SSIM.
KPRNet: Improving projection-based LiDAR semantic segmentation

Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation to achieve a better accuracy in the task of segmenting LiDAR scans. KPRNet improves the convolutional neural network architecture of 2D projection methods and utilizes KPConv to replace the commonly used post-processing techniques with a learnable point-wise component which allows us to obtain more accurate 3D labels. With these improvements our model outperforms the current best method on the SemanticKITTI benchmark, reaching an mIoU of 63.1.