Miracle-dz/UniFAR-FTUs huggingface dataset:
M2-M5 systems in = 6 Chern-Simons theory

We study two aspects of M5-branes in N = 6 U(N) × U(N) Chern-Simons gauge theory. We first examine multiple M2-branes ending on a M5-brane. We study Basu-Harvey type cubic equations, fuzzy funnel configurations, and derive the M5-brane tension from the N = 6 theory. We also find a limit in which the above M2-M5 system reduces to a D2-D4 system and we recover the Nahm equation from the N = 6 theory. We then examine domain wall configurations in mass-deformed N = 6 theory with a manifest SU(2) × SU(2) × U(1) global symmetry. We derive tensions of domain walls connecting between arbitrary M5-brane vacua of the deformed theory and observe their consistency with gravity dual expectations.
Impact of BRCA Gene Testing on Ovarian Cancer Management

Ovarian cancer is a diverse disease with several cellular subtypes, the most common of which are high-grade serous ovarian cancer (HGSOC). Ovarian cancer is still primarily treated with chemotherapy and surgery. Recent advances in the hereditary understanding of this disease have revealed that the BRCA gene plays an important role. While only a small percentage of HGSOC patients will have a germline BRCA mutation, many more will have tumor genetic aberrations within BRCA or other homologous recombination proteins. Improved preventative measures and therapeutic development have resulted from genetic screening for these BRCA mutations. This review focuses on BRCA mutations and their relationship to the development of ovarian cancer, as well as future therapeutic targets.
ORAL AND PARENTERAL TO MINIMIZE THE NASAL DELIVERY BY THERMOREVERSIBLE MUCOADHESIVE – A REVIEW

The adhesion between two materials, at least one of which is a mucosal surface, is generally referred to as mucoadhesion. Mucosal medication delivery has drawn a lot of interest during the past few decades. Mucoadhesive dosage forms may be created to provide a regulated rate of drug release for a better therapeutic result by enabling prolonged retention at the site of application. Drug molecules that are not suitable for oral administration, such as those that undergo acid degradation or substantial first-pass metabolism, may benefit from the application of dosage forms to mucosal surfaces. The nature of the mucosal tissue and the physicochemical characteristics of the polymeric formulation are just two examples of the many variables that affect a dosage form's capacity to adhere to mucous membranes. This review article seeks to give a general overview of the numerous mucoadhesion issues, Mucoadhesive materials, factors affecting mucoadhesion, evaluation techniques, and ultimately various Mucoadhesive drug delivery systems (buccal, nasal, ocular, gastro, vaginal, and rectal). the of Mucoadhesive
Recent Advancement in Sleep Technologies: A Literature Review on Clinical Standards, Sensors, Apps, and AI Methods

This is a literature review paper covering state-of-the-art sleep technologies to measure sleep and clinical sleep disorders. This paper addresses an interdisciplinary audience from a variety of subdomains in engineering and medicine. We reviewed 120 scientific papers, 15 commercial mobile apps, and 4 commercial devices. We selected the papers from scientific publishers including Institute of Electrical and Electronics Engineers (IEEE), Nature, Association for Computing Machinery (ACM), Proceedings of Machine Learning Research, Journal of Informatics in Health and Biomedicine, Plos One, PubMed, and Elsevier and Nature digital libraries. We used Google Scholar with keywords including “sleep monitoring”, “sleep monitoring technologies”, “non-contact sleep monitoring”, “mobile apps for sleep monitoring”, “AI in sleep technologies”, and “automated sleep staging. ” The manuscript reviews sleep technologies, including sleep lab technologies such as polysomnography and consumer sleep technologies categorized as ambient room sensors, wearable sensors, bed sensors, mobile apps, and artificial intelligence. We primarily focused on validation and comparison studies of the reviewed technologies. The manuscript also provides an overview of several clinical datasets for sleep staging and taxonomizes the different learning methods. Finally, the manuscript offers our insights and recommendations about the application of the reviewed sleep technologies.
Measuring the lagged effects of advertising goodwill on dynamic promotional efficiency in the automobile industry

Abstract Thus far, the data envelopment analysis (DEA) model has not been used to examine the lagged effects of advertising goodwill stock on promotional efficiency. Thus, this study develops a dynamic DEA framework to illustrate the lagged effect of advertising goodwill stock on the dynamic promotional efficiency of major automobile manufacturers in the United States. The advertising goodwill stock in five automobile producers was measured using the econometric model. Subsequently, the advertising goodwill stock was treated as a lagged effect in a dynamic DEA framework. We analyse the empirical results and formulate the following two important findings: First, the results demonstrate that the lagged effect of advertising goodwill stock can lead to changes in the promotional efficiency of advertising. Second, the paper discusses ways that inefficient automobile manufacturers can improve marketing strategy implementation.
Structurally Modified Bioactive Peptide Inhibits SARS-CoV-2 Lentiviral Particles Expression

Coronavirus disease 2019 (COVID-19), the current global pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Various pharmaceuticals are being developed to counter the spread of the virus. The strategy of repurposing known drugs and bioactive molecules is a rational approach. A previously described molecule, Ile-Arg-Trp (IRW), is a bioactive tripeptide that exhibits an ability to boost angiotensin converting enzyme-2 (ACE2) expression in animals and cells. Given the importance of SARS-CoV-2 S receptor binding domain (RBD)-ACE2 interaction in SARS-CoV-2 pathophysiology, we synthesized various IRW analogs intending to mitigate the RBD-ACE-2 interaction. Herein, we describe two analogs of IRW, A9 (Acetyl-Ile-Arg-Trp-Amide) and A14 (Formyl-Ile-Arg-Trp-Amide) which lowered the SARS-CoV-2 S RBD-ACE2 (at 50 µM) in vitro. The free energy of binding suggested that A9 and A14 interacted with the SARS-CoV-2 S RBD more favorably than ACE2. The calculated MMGBSA ΔG of spike binding for A9 was −57.22 kcal/mol, while that of A14 was −52.44 kcal/mol. A14 also inhibited furin enzymatic activity at various tested concentrations (25, 50, and 100 µM). We confirmed the effect of the two potent analogs using SARS-CoV-2 spike protein overexpressing cells. Both peptides lowered the protein expression of SARS-CoV-2 spike protein at the tested concentration (50 µM). Similarly, both peptides, A9 and A14 (50 µM), also inhibited pseudotyped lentiviral particles with SARS-CoV-2 Spike in ACE2 overexpressing cells. Further, the molecular dynamics (MD) calculations showed the interaction of A9 and A14 with multiple residues in spike S1 RBD. In conclusion, novel peptide analogs of ACE2 boosting IRW were prepared and confirmed through in vitro, cellular, and computational evaluations to be potential seed candidates for SARS-CoV-2 host cell binding inhibition.
Probing particle and nuclear physics models of neutrinoless double beta decay with different nuclei

Half-life estimates for neutrinoless double beta decay depend on particle physics models for lepton-flavor violation, as well as on nuclear physics models for the structure and transitions of candidate nuclei. Different models considered in the literature can be contrasted\char22{}via prospective data\char22{}with a ``standard'' scenario characterized by light Majorana neutrino exchange and by the quasiparticle random phase approximation, for which the theoretical covariance matrix has been recently estimated. We show that, assuming future half-life data in four promising nuclei ($^{76}\mathrm{Ge}$, $^{82}\mathrm{Se}$, $^{130}\mathrm{Te}$, and $^{136}\mathrm{Xe}$), the standard scenario can be distinguished from a few nonstandard physics models, while being compatible with alternative state-of-the-art nuclear calculations (at 95% C.L.). Future signals in different nuclei may thus help to discriminate at least some decay mechanisms, without being spoiled by current nuclear uncertainties. Prospects for possible improvements are also discussed.
Sparse-view CT reconstruction based on fusion learning in hybrid domain

In the synchrotron radiation tomography experiment, sparse-view sampling is capable of reducing severe radiation damages of samples from X-ray, accelerating sampling rate and decreasing total volume of experimental dataset. Consequently, the sparse-view CT reconstruction has been a hot topic nowadays. Generally, there are two types of traditional algorithms for CT reconstruction, i.e., the analytic and iterative algorithms. However, the widely used analytic CT reconstruction algorithms usually lead to severe stripe artifacts in the sparse-view reconstructed images, due to the Nyquist rule is not satisfied. While the more accurate iterative algorithms often result in prohibitively high computational costs and difficulty in selecting production parameters. In this paper, we propose a new hybrid domain method based on fusion learning which contain the image domain and projection domain. In the image domain, we propose a UNet-like network TransCovUNet which contains the Transformer module to consider the global correlation of the extracted features. In the projection domain, we employ a modified Laplacian Pyramid network to recover unmeasured data in the sinogram, which progressively reconstructs the sub-band residuals and can reduce the quantity of network parameters. Subsequently, we employ a deep fusion network to fuse the two reconstruction results at a feature-level, which can merge the useful information of the two reconstructed images. We also compared the performances of those single-domain methods and the hybrid domain method. Experimental results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.
Measuring the “Blue” Face Of Narcissism: Psychometric Properties of Ukrainian Translation of the Hypersensitive Narcissism Scale in Non-Clinical Groups

Aim. Little research has focused on hypersensitive narcissism in Slavic countries. One of the reasons for that is the absence of suitable inventories. The present study aims to make the Ukrainian adapted translation of the Hypersensitive Narcissism Scale (HSNS) and to check for its psychometric properties in a non-clinical sample. Methods. To prepare materials, we first conducted a double-blind translation procedure with further linguistic analysis. The following two empirical studies to collect data for statistical analysis were then made. Results. The data shows adequate internal consistency and scale validity, as well as a three-factor structure (obtained with the principal components analysis [PCA]), in line with the theoretical background. However, the confirmatory factor analysis (CFA) and Cronbach’s alphas analysis yield the unidimensional scale to be the best fit for the Ukrainian version. The data also support evidence for maladaptive social relationship patterns of people with high HSNS scores. Conclusion. We proved vulnerable and grandiose narcissism are qualitatively different phenomena and showed how a cultural context of narcissism manifestation may be reflected through social interactions and self-attitude. HSNS in Ukrainian is a reliable and valid tool for complex psychological personality research among non-clinical adult samples. Practical application. The proposed translation of the HSNS is adapted to be used for research in Ukrainian culture, with Ukrainian-speaking respondents and psychologists’ and psychotherapists’ clients.
REST-FRAME UV SINGLE-EPOCH BLACK HOLE MASS ESTIMATES OF LOW-LUMINOSITY AGNs AT INTERMEDIATE REDSHIFTS

The ability to accurately derive black hole (BH) masses at progressively higher redshifts and over a wide range of continuum luminosities has become indispensable in the era of large-area extragalactic spectroscopic surveys. In this paper, we present an extension of existing comparisons between rest-frame UV and optical virial BH mass estimators to intermediate redshifts and luminosities comparable to the local Hβ reverberation-mapped active galactic nuclei (AGNs). We focus on the Mg ii, C iv, and C iii] broad emission lines and compare them to both Hα and Hβ. We use newly acquired near-infrared spectra from the Fiber-fed Multi-object Spectrograph instrument on the Subaru telescope for 89 broad-lined AGNs at redshifts between 0.3 and 3.5, complemented by data from the AGES survey. We employ two different prescriptions for measuring the emission line widths and compare the results. We confirm that Mg ii shows a tight correlation with Hα and Hβ, with a scatter of ∼0.25 dex. The C iv and C iii] estimators, while showing larger scatter, are viable virial mass estimators after accounting for a trend with the UV-to-optical luminosity ratio. We find an intrinsic scatter of ∼0.37 dex between Balmer and carbon virial estimators by combining our data set with previous high redshift measurements. This updated comparison spans a total of three decades in BH mass. We calculate a virial factor for C iv/C iii] log f C IV / C III ] = 0.87 ? > with an estimated systematic uncertainty of ∼0.4 dex and find excellent agreement between the local reverberation mapped AGN sample and our high-z sample.
Impact of Previous Occupational Exposure on Outcomes of Chronic Obstructive Pulmonary Disease

Abstract: Background Occupational exposures have been regarded as a risk factor for the development of chronic obstructive pulmonary disease (COPD). However, there is little knowledge regarding the effect of occupational exposure on the treatment outcomes of COPD. Therefore, the aim of this study was to evaluate the question of whether occupational exposure can have a potential impact on COPD outcomes. Methods: Information regarding self-reported occupational exposure for 312 patients with COPD from the Korean Obstructive Lung Disease (KOLD) Cohort were included. A comparison of the rate of acute exacerbation, annual lung function change, and quality of life according to the presence or absence of occupational exposure was performed. Results: Seventy-six patients (24.4%) had experienced occupational exposure; chemical materials were most common. At enrollment, a higher COPD-specific version of the St. George Respiratory Questionnaire total score (39.7 ± 18.8 vs. 33.1 ± 17.6, p = 0.005) and a higher exacerbation history in the past year (30.3% vs. 17.5%, p = 0.017) were observed for patients with occupational exposure compared to those without occupational exposure. Furthermore, in the follow-up period, after adjusting for potential confounders, a higher frequency of acute exacerbation (odd ratio, 1.418; 95% confidence interval, 1.027–1.956; p = 0.033) and a more rapid decline in forced expiratory volume in 1 s (p = 0.009) was observed for COPD patients with occupational exposure compared to those without occupational exposure. Conclusions: In the KOLD cohort, worse outcomes in terms of exacerbation rate and change in lung function were observed for COPD patients with occupational exposure compared to those without occupational exposure. These findings suggest that occupational exposure not only is a risk factor for COPD but also might have a prognostic impact on COPD.
Absorbing phase transition in a four-state predator–prey model in one dimension

The model of competition between densities of two different species, called predator and prey, is studied on a one-dimensional periodic lattice, where each site can be in one of the four states, say, empty, or occupied by a single predator, or occupied by a single prey, or by both. Along with the pairwise death of predators and growth of prey, we introduce an interaction where the predators can eat one of the neighboring prey and reproduce a new predator there instantly. The model shows a non-equilibrium phase transition into an unusual absorbing state where predators are absent and the lattice is fully occupied by prey. The critical exponents of the system are found to be different from those of the directed percolation universality class and they are robust against addition of explicit diffusion.
The Theory of Critical Phenomena: An Introduction to the Renormalization Group

Here is a much-needed basic text that covers a vital area in physics for beginning graduate students. The successful calculation of critical exponents for continuous phase transitions is one of the main achievements of theoretical physics over the last quarter-century. This was achieved through the use of scaling and field theoretic techniques which have since become standard equipment in many areas of physics, especially quantum field theory. This book provides a thorough introduction to these techniques, one that assumes only a sound undergraduate background in physics and mathematics. Continuous phase transitions are introduced, then the necessary statistical mechanics is summarized, followed by standard models, some exact solutions and techniques for numerical simulations. The real space renormalization group and mean field theory are next explained and illustrated. The last eight chapters cover the Landau-Ginzburg model, from physical motivation, through diagrammatic perturbation theory and renormalization to the renormalization group and the calculation of critical exponents above and below the critical temperature.
The Spitzer Local Volume Legacy (LVL) Global Optical Photometry

We present the global optical photometry of 246 galaxies in the Local Volume Legacy (LVL) survey. The full volume-limited sample consists of 258 nearby (D <11 Mpc) galaxies whose absolute Biband magnitude span a range of i9.6 < MB < i20.7 mag. A composite optical (UBV R) data set is constructed from observed UBV R and SDSS ugriz imaging, where the ugriz magnitudes are transformed into UBV R. We present photometry within three galaxy apertures defined at UV, optical, and IR wavelengths. Flux comparisons between these apertures reveal that the traditional optical R25 galaxy apertures do not fully encompass extended sources. Using the larger IR apertures we find color-color relationships where later-type spiral and irregular galaxies tend to be bluer than earlier-type galaxies. These data provide the missing optical emission from which future LVL studies can construct the full panchromatic (UV-optical-IR) spectral energy distributions.
PREDICTION OF DEPRESSION USING MACHINE LEARNING TOOLS TAKING CONSIDERATION OF OVERSAMPLING

Depression is a psychiatric condition characterized by a persistent sense of sadness and dullness. It is also known as a severe burdensome problem or clinical sorrow, and it impacts how a person feels, thinks, and behaves and triggers a slew of emotional and physical issues. Various components are liable for this issue, and many related sicknesses are expanding because of this infection. It is not just at risk for well-being perils, yet also produces perilous social offense, like self-destruction and family misuse. In this study, we used machine learning methods such as Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB). We also used accuracy, precision, recall, and F1-score to survey the exhibition assessment of arrangement results. These machine learning algorithms developed and analyzed Confusion matrices through data augmentation to assess the classification performance. This study used machine learning technologies to predict depression and revealed the significance of the trait. Then we have tried to utilize an oversampling technique that shows the distinction in model execution. Indeed, we wanted to see how well the recommended machine learning algorithms performed before and after rebalancing standardized data. In our suggested framework, the RF classifier performed better with 89% accuracy and 90% precision than other models.
Combining Ability and Testcross Performance for Carotenoid Content of S2 Super Sweet Corn Lines Derived from Temperate Germplasm

Understanding the impact of gene action and combining ability on targeted traits in a hybrid breeding program is imperative. The objective of this study was to estimate the genetic effect and combining ability of S2 super sweet corn lines on carotenoid content. Twenty S2 lines were crossed with two hybrid testers by using the line × tester mating design. Regarding parents, forty hybrids and two commercial checks were evaluated for carotenoid content across two different growing seasons between 2019 and 2020. The result indicated that the non-additive gene action governed the inheritance of carotenoid content. Several promising S2 lines for individual carotenoids were identified; only L20 possessed different and positive GCA values for all observed carotenoids. Moreover, genotype T2 was a promising tester to identify superior lines for creating biofortified sweet corn hybrids. Testcross hybrids with satisfactory performance, desirable SCA estimates, and involving at least one of the pairwise parents with positive and high GCA were successfully defined. Hybrid T2 × L20 had high lutein, zeaxanthin, β-cryptoxanthin, and total carotenoids contents (ranging from 12.58 to 74.01 µg/g of dry weight), whereas hybrid T2 × L9 showed the highest content of β-carotene (4.19 µg/g of dry weight). We propose that high GCA and line at least one of the pairwise parents be included in indirect selections for the hybrid breeding of high-carotenoid sweet corn.
Disentangling of composite spectra.

A novel technique for the reconstruction of the individual spectra of the components of well detached binaries is presented. The method requires a set of composite spectra at different phases. It is based on a singular value decomposition (SVD), the appropriate method of treating a linear problem which is both over determined and rank deficient. The algorithm also yields precise radial velocities, even at phases where the lines are severely overlapping, e.g. near conjunction. This is a great advantage for the construction of radial velocity curves, especially the determination of system velocities and orbit eccentricities. The quality of the reconstructed spectra is demonstrated using CCD spectra of the B0.5 type binary V453 Cygni
3-D SAR Autofocusing With Learned Sparsity

Inevitable inaccuracies of 3-D synthetic aperture radar (3-D SAR) imaging geometry may cause undesired blurs in reconstructed images. Recent advances show impressive results in integrating error estimation into sparse imaging. However, the concept is still challenging in 3-D SAR due to the cumbersome high-dimensional processing. To address this problem, we propose a model-driven 3-D SAR autofocusing network with learned sparsity (AFLS-Net) by applying the recent emerging deep unfolding technique. In our scheme, we first construct a kernel-based observation model with consideration of motion-induced phase errors, which avoids the memory-consuming matrix calculations in the conventional matrix–vector form. Then, a joint sparse imaging and autofocusing algorithm is derived based on the framework of block coordinate descent. In addition, by mapping the computational steps, the AFLS-Net is designed to further improve the autofocusing accuracy and efficiency in which a shallow two-path convolutional neural network (CNN) is embedded to explore the implicit sparse prior, by which the reconstruction accuracy can be improved. Meanwhile, the batchwise autofocusing module is designed to obtain a robust estimation by jointly optimizing subcost functions associated with a batch of independent measurements. Finally, the methodology is validated in both simulations and laboratory 3-D SAR experiments. The experimental results suggest that the proposed method obtains better autofocusing quality compared to other comparison baselines in reconstructing 3-D SAR images from incomplete and error-polluted echoes.