Miracle-dz/UniFAR-FTUs huggingface dataset:
How Brand Jealousy is an Antidote for Premium to Pay More in the Presence of Materialism and Brand Attachment?

How Brand Jealousy is an Antidote for Premium to Pay More in the Presence of Materialism and Brand Attachment? Muhammad Waqar Rana, Faisal Mustafa, Muhammad Umer Quddoos, Rab Nawaz Lodhi 1 MS Scholar, Institute of Business & Management, University of Engineering and Technology, Lahore, Pakistan, Email: muhammadwaqarrana@gmail.com 2 Provost, University of Central Punjab, Lahore, Pakistan, Email: faisal.mustafa@ucp.edu. pk 3 Assistant Professor, Bahauddin Zakariya University, Multan, Pakistan, Email: umerattari@bzu.edu. pk 4 Associate Professor, UCP Business School, University of Central Punjab, Lahore, Pakistan, Email: rabnawaz.lodhi@ucp.edu. pk
Euler-Heisenberg Lagrangian Through Krein Regularization

The Euler–Heisenberg effective action at the one-loop for a constant electromagnetic field is derived in Krein space quantization with Ford's idea of fluctuated light-cone. In this work, we present a perturbative but convergent solution of the effective action. Without using any renormalization procedure, the result coincides with the famous renormalized Euler–Heisenberg action.
Quantitative Molecular Structure–Pyrolytic Energy Correlation for Hardwood Lignins

The molecular structures of the milled wood lignins (MWLs) and technical lignins (TLs) obtained from four hardwood species (Eucalyptus urograndis, Eucalyptus nitens, Eucalyptus globulus, and Populus trichocarpa) were quantified by 13C nuclear magnetic resonance (NMR) in tandem with their thermal responses, as obtained by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Both MWLs and TLs showed similar DSC curves with two exothermic peaks (referred to as peaks 1 and 2). Also, maximum temperatures for MWL peaks were higher than for TL peaks, with an average of 20 and 10 °C for the first peak and second peak, respectively. Even though calculated enthalpies for MWLs were higher because of their purities, TLs had reasonable relationships between heat values and lignin substructures. TL had a positive correlation between condensed structures and the enthalpy value between 413 and 428 °C, but negative relationships were found at the latter temperature window for lignin substructures,.
RISK ASSESSMENT OF LISTERIA MONOCYTOGENES IN RTE FOOD BY NANOPARTICLE BASED IMMUNOGENETIC SEPARATION AND RT-PCR

Listeria monocytogenes is an important food borne pathogen responsible for listeriosis, a fatal disease. It is widely distributed in various foods and environmental sources. This pathogen has been isolated from different food products, but milk products surely play a major role in the epidemiology of this food borne disease. More recently, molecular methods were developed that target RNA rather than DNA , such as RT -PCR, real time PCR or nucleic acid sequence-based amplification (NA SBA). In this review, the risk of L.monocytogenes illness associated to the consumption of RTE meat based foods Coimbatore city inhabitants appeared to be relatively low (ranging from 1.7 to 3.4% depending of the risk exposure pathway) and was found to be increasing with the meat consumption level in different socio-economical category of the consumers. And also was to develop a method combining nanoparticle-based immunomagnetic separation (IMS) with real-time PCR for a rapid and quantitative detection of Listeria monocytogenes. Carboxyl modified magnetic nanoparticles were covalently bound with rabbit antiL.monocytogenes via the amine groups. Our results demonstrated that both the use of nanoparticles and the choice of antiL.monocytogenes in our IMNP based IMS in combination with real time PCR has improved the sensitivity of L.monocytogenes d e t ec t ion .
Human γδ T cell sensing of AMPK-dependent metabolic tumor reprogramming through TCR recognition of EphA2

A γδ TCR senses metabolic tumor reprogramming through recognition of EphA2. Sensing metabolic shifts Human γδ T cells play a critical role in surveillance against infection and cancer and use various mechanisms to sense metabolic changes associated with these assaults. Harly et al. describe a role for the stress ligand ephrin type-A receptor 2 (EphA2), which is up-regulated on cancer cells and is recognized by the human Vγ9Vδ1 T cell receptor (TCR) together with ephrin A. EphA2 up-regulation was mediated by metabolic changes in cancer cells caused by AMP-activated protein kinase (AMPK) activity. Increased coexpression of EphA2 and AMPK in tumor tissues was also linked to greater T cell infiltration in human colorectal cancer tissue. These findings suggest that human γδ T cells can sense changes in stress ligands and trigger responses caused by metabolic changes associated with infection or cancer. Human γδ T cells contribute to tissue homeostasis and participate in epithelial stress surveillance through mechanisms that are not well understood. Here, we identified ephrin type-A receptor 2 (EphA2) as a stress antigen recognized by a human Vγ9Vδ1 TCR. EphA2 is recognized coordinately by ephrin A to enable γδ TCR activation. We identified a putative TCR binding site on the ligand-binding domain of EphA2 that was distinct from the ephrin A binding site. Expression of EphA2 was up-regulated upon AMP-activated protein kinase (AMPK)–dependent metabolic reprogramming of cancer cells, and coexpression of EphA2 and active AMPK in tumors was associated with higher CD3 T cell infiltration in human colorectal cancer tissue. These results highlight the potential of the human γδ TCR to cooperate with a co-receptor to recognize non–MHC-encoded proteins as signals of cellular dysregulation, potentially allowing γδ T cells to sense metabolic energy changes associated with either viral infection or cancer.
A FORMATION SCENARIO FOR THE DISK OF SATELLITES: ACCRETION OF SATELLITES DURING MERGERS

The Disk of Satellites (DoS) observed in the Andromeda galaxy is a thin and extended group of satellites, nearly perpendicular to the disk plane, that share a common direction of rotation about the center of Andromeda. Although a DoS is also observed in the Milky Way galaxy, the prevalance of such structures in more distant galaxies remains controversial. Explanations for the formation of such DoSs vary widely from filamentary infall, or flattening due to the potential field from the large-scale structure, to galaxy interactions in a Mondian paradigm. Here we present an alternative scenario—during a merger, a galaxy may bring its own satellite population when merging with another galaxy. We demonstrate how, under the correct circumstances, during the coalescence of the two galaxies, the satellite population can be spread into an extended, flattened structure, with a common direction of rotation about the merger remnant. We investigate the key parameters of the interaction and the satellite population that are required to form a DoS in this scenario.
ANTI-NUTRIENT PROFILE OF DIFFERENT CHENOPODIUM CULTIVARS LEAVES

Chenopodium album belongs to family Amaranthaceae commonly known as pigweed or goose foot or bathua (in Hindi) is a weedy plant found all over world and has gained renowned popularity these days due to its high nutritional composition especially amino acids. In the present study the leaves of four Chenopodium album cultivars were analysed for the anti-nutritional composition viz. tannins, simple phenols, total phenols, phytic acid, oxalates, flavonoids, alkaloids, Trypsin Inhibitor Activity (TIA), phytic acid and phytate phosphorus using standard methods. The results of present investigation showed that the maximum total phenol, simple phenol and tannin content were in Ec 1 cultivar i.e. 304.98, 101.007 and 203.91 mg GAE/100g respectively whereas minimum was present in Ec 2 cultivar i.e. 224.99, 72.50 and 152.49 mg GAE/100g respectively. The TIA content ranged between 0.11-0.17 TIU/mg whereas the saponin content ranged between 0.027-0.867g/100g. The highest TIA was present in Ec 2 cultivar whereas the saponin content was highest in Ec 1 cultivar. The highest phytic acid content was present in Ec 2 cultivar (268.33mg/100g) whereas lowest in Ec 1 cultivar (238.33mg/100g). Similarly the maximum phytate phosphorus content was present in Ec 2 cultivar and minimum in Ec 1 cultivar. The alkaloid content in Chenopodium cultivar leaves ranged between 1.27-1.67mg/100g whereas flavonoid content ranged between 220.0-406.67mg/100g respectively. The maximum oxalate content was there in Ic 1 cultivar (518.42mg/100g) and minimum in Ec 1 cultivar (394.19 mg/100g) respectively. Certain anti-nutrients among these besides being an anti-nutrient also exert positive health effect especially phenolic compounds, flavonoids, alkaloids thus can also utilized for pharmaceutical purpose.
Learning-based Point Cloud Registration: A Short Review and Evaluation

∗ Point cloud registration is an important task for range scan alignment, pose estimation, and localization. Traditional point cloud registration methods rely on hand-craft descriptors, which are sometimes not so descriptive and make the pose solver easy to fail because of false matchings. Recently, many researchers seek to improve the traditional method by deep learning-based approach. In this paper, we summarize the main pipeline of point cloud registration in traditional and learning-based approaches. Then we review some of the recent start-of-art methods, mainly in the end-to-end learning approach. We also review the criteria used to evaluate the registration performance and give complete testing results, some of which are not provided by those papers.
RL-BIN++: Overcoming Binary Instrumentation Challenges in the Presence of Obfuscation Techniques and Problematic Features

This paper improves upon an earlier binary rewriter we designed called RL-Bin. Unlike static rewrites, which are inherently non-robust, RL-Bin uses a dynamic design and thus is more robust. However, although RL-Bin works for most compiled binaries, real-world features commonly found in obfuscated binaries are still not handled. The features include anti-disassembly, dynamically modified code, anti-rewriting, anti-debugging, and code convention violation. This paper presents RL-Bin++, an improved version of RL-Bin, that handles various problematic real-world features, thus correctly rewriting for nearly all benign binaries. We demonstrate that RL-Bin++ can efficiently instrument heavily obfuscated binaries (overhead averaging 2.76x, compared to 4.11x, and 5.31x overhead for DynamoRIO and Pin, which are comparable or lower overheads. However, the main achievement is that we achieved this while maintaining the low overhead of RL-Bin for unobfuscated binaries (only 1.05x). This makes RL-Bin++ the only robust binary instrumentation solution capable of being deployed in live systems since the overhead of DynamoRIO (1.16x), and Pin (1.29x) for unobfuscated binaries is too high for use in live systems.
PubTrends: a scientific literature explorer

With an ever-increasing number of scientific papers published each year, it becomes more difficult for researchers to explore unfamiliar or fast-growing research areas. This greatly inhibits the potential for cross-disciplinary research. When approaching a new subject, a researcher often starts with a search of relevant papers in a dedicated search engine - Google Scholar, Scopus, etc. Besides the search by title, author names, or keywords, these services also provide the user with basic statistics such as number of citations of the paper. However, it is hard to organize the information from these papers, especially with the current rise in publication numbers. For instance, regarding the latest COVID-19 epidemic, thousands of papers were published in the first months. Bibliometrics methods [1] allow for structuring the information in papers by highlighting the most frequent keywords in the selected area. Also, the number of citations is widely used to rank papers according to their estimated scientific impact, citation and co-citation networks for groups of papers may reflect the underlying structure of the research field [2]. Papers are more likely to be co-cited if they belong to closely related research topics, and network analysis allows extracting meaningful clusters representing different scientific directions. Another approach for structuring the information is based on the methods for natural language processing. In particular, we have shown that citation context can be used for summarization of papers and, subsequently, research topics [3]. To our knowledge, no freely available tools exist that combine different approaches for structuring information in research papers in an easy-to-use web service. Most bibliometrics tools require manual preparation of papers dataset, and most of the summarization methods are lacking ready-to-use implementation. We present PubTrends - a scientific publication exploratory tool capable of analyzing the intellectual structure of a research field and similar papers analysis. The service is available at https://pubtrends.net and works with papers from the PubMed [4] database of biomedical texts. Within search results for a given query or a list of paper ids, it shows the most cited papers, frequent keywords, most relevant authors, and journals. We combine bibliometrics methods for citation information analysis and natural language processing algorithms to compute similarity between papers, followed by topics extraction with clustering. Integrated viewer for citation graph and paper similarity network with rich capabilities of visualization and filtering allows for quick navigation through the different aspects of the research field. Finally, we apply deep learning methods to interactively generate automated literature reviews. In addition to implementation details and examples, we demonstrate that topic extraction algorithms produce relevant results by comparison with topics extracted from selected review papers from Nature Reviews journals.
SPEAR: self-supervised post-training enhancer for molecule optimization

The molecular optimization task is to generate molecules that are similar to a target molecule but with better chemical properties. Deep Generative Models (DGMs) have shown initial success in automatic molecule optimization. However, the training of DGMs often suffers from limited labeled molecule pairs due to the ad-hoc and restricted molecule pair construction. To solve this challenge and leverage the entire unpaired molecule database, we propose Self-Supervised Post-training EnhAnceR method (SPEAR) to enhance any graph-based DGMs for molecule optimization. SPEAR mines molecular structure knowledge and learns the molecule generation procedure in a purely self-supervised fashion. Unlike most self-supervised deep learning models that rely on pre-training for better molecule representation, the SPEAR method is applied as post-processing step to enhance molecule optimization during inference time for DGMs without additional training. Our SPEAR model can be efficiently incorporated into any DGM model as part of the inference procedure. We evaluated SPEAR against several state-of-the-art DGMs, SPEAR successfully improved the performance of all DGMs and obtained 5--21% relative improvement over its corresponding DGM models in terms of success rate.
On the Deployment of Default Routes in Inter-domain Routing

In this paper, we argue that the design of a responsible Internet requires a clear understanding of the current state of deployment. This work sheds light on default routing in the Internet, a routing strategy that reduces control but may help to increase availability when forwarding packets. We revisit and extend two common methodologies based on active measurements to increase coverage and accuracy. Our results show larger differences in the results between the methodologies. We confirm that default route deployment strongly correlates with the customer cone size of autonomous systems and that smaller networks are more likely to deploy a default route. Our data will help to better assess the deployment of other protocols such as RPKI route origin filtering.
Effects of the TNFRSF11B Mutation Associated With Calcium Pyrophosphate Deposition Disease in Osteoclastogenesis in a Murine Model

The gene TNFRSF11B encodes for osteoprotegerin (OPG) and was recently identified as the CCAL1 locus associated with familial calcium pyrophosphate deposition disease (CPDD). While the CCAL1 OPG mutation (OPG‐XL) was originally believed to be a gain‐of‐function mutation, loss of OPG activity causes arthritis‐associated osteolysis in mice, which is likely related to excess subchondral osteoclast formation and/or activity. The purpose of the present study was to further explore the effect of OPG‐XL in osteoclastogenesis.
Hybrid Reverse Monte Carlo and electron phase contrast image simulations of amorphous silicon with and without paracrystals

Atomic networks of as-implanted and relaxed amorphous silicon solids were simulated using a Hybrid Reverse Monte Carlo algorithm constrained by high-resolution electron diffraction data. No significant structural distinction was observed between the two forms of amorphous silicon. A nanometer-sized crystallite was inserted into the as-implanted structure, to model medium-range order due to paracrystals, and the atomic network was energetically relaxed whilst maintaining consistency with experiment. Experimental pair–pair correlations were then simulated using a stochastic generalised Debye sum of fourth order. The idealised pair–pair correlation calculations were not able to readily distinguish between models with and without paracrystals. On the other hand, wave mechanical simulations surprisingly showed that paracrystals could be experimentally imaged using phase contrast transmission electron microscopy and/or nanoscale electron diffraction on a contemporary aberration-corrected microscope.
Non-tidal loading of the Baltic Sea in Latvian GNSS time series

Abstract The objective of this study is to investigate the effect of the Baltic Sea non-tidal loading in the territory of Latvia using observations of the GNSS continuously operating reference stations (CORS) of LatPos, EUPOS®-Riga, EPN and EstPos networks. The GNSS station daily coordinate time series obtained in a double-difference (DD) mode were used. For representation of the sea level dynamics, the Latvian tide gauge records were used. Performed correlation analysis is based on yearly data sets of these observations for the period from 2012 up to 2020. The approach discloses how the non-tidal loading can induce variations in the time series of the regional GNSS station network. This paper increases understanding of the Earth’s surface displacements occurring due to the non-tidal loading effect in Latvia, and is intended to raise the importance and necessity of improved Latvian GNSS time series by removing loading effects.
Combination Treatment With Remdesivir and Ivermectin Exerts Highly Synergistic and Potent Antiviral Activity Against Murine Coronavirus Infection

The recent COVID-19 pandemic has highlighted the urgency to develop effective antiviral therapies against the disease. Murine hepatitis virus (MHV) is a coronavirus that infects mice and shares some sequence identity to SARS-CoV-2. Both viruses belong to the Betacoronavirus genus, and MHV thus serves as a useful and safe surrogate model for SARS-CoV-2 infections. Clinical trials have indicated that remdesivir is a potentially promising antiviral drug against COVID-19. Using an in vitro model of MHV infection of RAW264.7 macrophages, the safety and efficacy of monotherapy of remdesivir, chloroquine, ivermectin, and doxycycline were investigated. Of the four drugs tested, remdesivir monotherapy exerted the strongest inhibition of live virus and viral RNA replication of about 2-log10 and 1-log10, respectively (at 6 µM). Ivermectin treatment showed the highest selectivity index. Combination drug therapy was also evaluated using remdesivir (6 µM) together with chloroquine (15 µM), ivermectin (2 µM) or doxycycline (15 µM) – above their IC50 values and at high macrophage cell viability of over 95%. The combination of remdesivir and ivermectin exhibited highly potent synergism by achieving significant reductions of about 7-log10 of live virus and 2.5-log10 of viral RNA in infected macrophages. This combination also resulted in the lowest cytokine levels of IL-6, TNF-α, and leukemia inhibitory factor. The next best synergistic combination was remdesivir with doxycycline, which decreased levels of live virus by ~3-log10 and viral RNA by ~1.5-log10. These results warrant further studies to explore the mechanisms of action of the combination therapy, as well as future in vivo experiments and clinical trials for the treatment of SARS-CoV-2 infection.
The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma

Background Transcriptome analysis of cancer tissues has been instrumental in defining tumor subtypes, diagnostic signatures and cancer regulatory networks. Cancer transcriptomes are still predominantly analyzed at the level of gene expression. Few studies have addressed transcript-level variations, and most of these only looked at splice variants. Previously we introduced a k-mer based, reference-free method, DE-kupl, that performs differential analysis of RNA-seq data at the k-mer level, which enables distinguishing RNAs differing by a single nucleotide. Here we evaluate the significance of differential events discovered by this method in two independent lung adenocarcinoma RNA-seq datasets (N=583 and N=154). Results Focusing on differential events in a tumor vs normal setting, we found events in endogenous repeats, alternative splicing and polyadenylation sites, long non-coding RNAs, retained introns and unmapped RNAs. Replicability was highly significant for most event classes (assessed by comparing to events shared between unrelated tumors). Overall about 160,000 differential k-mer contigs were shared between datasets, including a large set of sequences from hypervariable genes such as immunoglobulins, SFTP and mucin genes. Most interestingly, we identified a set of novel tumor-specific long non-coding RNAs in intergenic and intronic regions. We found that expressed endogenous transposons defined two major groups of patients (high/low repeat expression) with distinct clinical characteristic. A number of repeats, intronic RNAs and lincRNA achieved strong patient stratification in univariate or multivariate survival models. Finally, using antigen presentation prediction, we identified 55 contigs predicted to produce recurrent tumor-specific antigens. Conclusions K-mer based RNA-seq analysis enables description of cancer transcriptomes at nucleotide precision, independently of prior transcript annotation. Application to lung cancer data uncovered events stemming from a wide variety of transcriptional and postranscriptional mechanisms. Among those events, a significant subset was replicable between cohorts, thus constituting novel RNA hallmarks of cancer. The code is available at: https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort.