Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Spironolactone Compared with Doxycycline in Moderate Acne in Adult Females: Results of the Multicentre, Controlled, Randomized, Double-blind Prospective and Parallel Female Acne Spironolactone vs doxyCycline Efficacy (FASCE) Study

Acne in adult females is triggered mainly by hormones. Doxycycline is a reference treatment in acne. Spironolactone targets the androgen receptor of sebaceous glands and is prescribed off-label for female adult acne. This multicentre, controlled, randomized, double-blind prospective and parallel study assessed the efficacy of spironolactone compared with doxycycline in adult female acne. A total of 133 women with moderate acne were randomized to receive treatment with: (i) doxycycline and benzoyl peroxide for 3 months followed by a 3-month treatment with its placebo and benzoyl peroxide, or (ii) spironolactone and benzoyl peroxide for 6 months. Successfully treated patients continued with benzoyl peroxide or spironolactone alone for a further 6 months. Primary endpoints were treatment success at month 4 and month 6 with the AFAST score. At all visits, the ECLA score, lesion counts, local and systemic safety and quality of life were assessed. Spironolactone performed better at month 4 and showed a statistically significant better treatment success after 6 months than doxycycline (p = 0.007). Spironolactone was 1.37-times and 2.87-times more successful compared with doxycycline at respective time-points. AFAST and ECLA scores, as well as lesion counts always improved more with spironolactone. Patients’ quality of life was better with spironolactone at month 4 and month 6. Spironolactone was very well tolerated. This is the first study to show that, in female adults with moderate acne, treatment with spironolactone is significantly more successful than doxycycline and very well tolerated. SIGNIFICANCE This clinical study, conducted in 133 adult women with moderate acne, showed, for the first time, that treatment with spironolactone is significantly more effective than doxycycline, and very well tolerated.
Seroprevalence of Hepatitis B virus and human immunodeficiency virus co-infection in pregnant women from Osun State, Nigeria.

INTRODUCTION Hepatitis B virus and human immunodeficiency virus (HBV/HIV) co-infection is a global health concern due to its significant impact on morbidity and mortality. Reports of HBV/HIV co-infections are increasing in Nigeria, but information on the disease burden in pregnant women and its implications on the fetus is scarce. This study aimed to determine the prevalence of HBV/HIV co-infection in pregnant women. In addition, the study identified the risk factors for the disease in pregnant women attending antenatal clinics in Osun State, Nigeria. METHODOLOGY We collected plasma samples from 303 consenting pregnant women and used enzyme-linked immunosorbent assay (ELISA) to test for HBV (HBsAg) and HIV I/II antigens. We obtained demographic and risk factor data on HBV and HIV transmission using a structured questionnaire. RESULTS Our analysis revealed a prevalence of 3.96% for HBV/HIV co-infection in pregnant women. Bivariate analysis indicated a history of blood transfusion, oral or anal sex, and multiple sexual partners may be associated with an increased likelihood of HBV/HIV co-infection in pregnant women. After adjusting for other variables in multivariate analysis, none of these risk factors were significant at the 5% level. In contrast, formal education was a potential preventive factor in this population. CONCLUSIONS Our study provides valuable information on the disease burden of HBV/HIV co-infection in pregnant women in Osun State, Nigeria, highlighting the importance of routine screening for HBV and HIV during antenatal care and emphasizing the importance of implementing preventive measures to reduce the morbidity and mortality associated with HBV/HIV co-infection.
Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery

The GNOM (GN) Guanine nucleotide Exchange Factor for ARF small GTPases (ARF-GEF) is among the best studied trafficking regulators in plants, playing crucial and unique developmental roles in patterning and polarity. The current models place GN at the Golgi apparatus (GA), where it mediates secretion/recycling, and at the plasma membrane (PM) presumably contributing to clathrin-mediated endocytosis (CME). The mechanistic basis of the developmental function of GN, distinct from the other ARF-GEFs including its closest homologue GNOM-LIKE1 (GNL1), remains elusive. Insights from this study largely extend the current notions of GN function. We show that GN, but not GNL1, localizes to the cell periphery at long-lived structures distinct from clathrin-coated pits, while CME and secretion proceed normally in gn knockouts. The functional GN mutant variant GNfewerroots, absent from the GA, suggests that the cell periphery is the major site of GN action responsible for its developmental function. Following inhibition by Brefeldin A, GN, but not GNL1, relocates to the PM likely on exocytic vesicles, suggesting selective molecular associations en route to the cell periphery. A study of GN-GNL1 chimeric ARF-GEFs indicates that all GN domains contribute to the specific GN function in a partially redundant manner. Together, this study offers significant steps toward the elucidation of the mechanism underlying unique cellular and development functions of GNOM.
The Spatial Structure of Opportunity and the Location Dynamics of Housing Mobility Programs

Title of Dissertation: THE SPATIAL STRUCTURE OF OPPORTUNITY AND THE LOCATION DYNAMICS OF HOUSING MOBILITY PROGRAMS Elijah Knaap, Doctor of Philosophy, 2016 Dissertation directed by: Professor Casey Dawkins, Department of Urban Studies and Planning In recent decades, federal housing policy has increasingly relied on housing vouchers that facilitate greater mobility among low income families and allow them to move into “high opportunity” neighborhoods. As housing policy has embraced this goal, however, a number of questions remain in the development of more equitable policy; among them, three are particularly poignant. First, how can the spatial dimensions of opportunity be measured and visualized? Can valid measurements of spatial opportunity be constructed, and if so, how? Second, how can housing mobility programs help low-income families access communities of opportunity? Finally, how do housing voucher recipients of different races sort into neighborhoods, and how can housing policies use these behavioral insights to generate more equitable outcomes? In this dissertation, I address each of these questions in turn through three empirical studies; in the first, I construct a conceptually and statistically valid measurement model of spatial opportunity using data from the Baltimore metropolitan region. I use this model to explain why current techniques are awed, and how modern quantitative methods can help introduce flexibility into opportunity analyses, leading to better, more nuanced policy prescriptions. In the second study, I develop a multilevel model to assess the longitudinal success of housing voucher programs in the Baltimore region. Using 10 years of data from Baltimore’s voucher programs, I examine how residential trajectories differed among household who received different types of housing assistance. I find that stark differences exist between recipients of different types of vouchers, and also among different racial groups. In the third study, I use a series of disaggregate discrete choice models to study the residential sorting patterns of Baltimore’s voucher holders. I find that voucher holders are sensitive to both dwelling unit characteristics, and neighborhood characteristics, but that the strongest sorting factor, by far, is neighborhood racial composition. Together, these studies show how the geography of opportunity can be measured and displayed, how black and white housing voucher recipients have starkly different access to the types of spatial capital that facilitates socioeconomic mobility, and how voucher programs can be redesigned to help foster racial and spatial equity. THE SPATIAL STRUCTURE OF OPPORTUNITY AND THE LOCATION DYNAMICS OF HOUSING MOBILITY PROGRAMS
Bionomics of vector-borne diseases in sites adjacent to lakes Victoria and Baringo in Kenya

iii DECLARATION . v PUBLICATION, MANUSCRIPTS AND CONFERENCES FROM THIS RESEARCH WORK . vi ACKNOWLEDGMENTS . viii TABLE OF CONTENTS . ix LIST OF FIGURES . xiii LIST OF TABLES . xv CHAPTER ONE .
STUDY ON A PRECISION IMPROVEMENT OF RUNOFF PREDICTION BY HYPE MODEL USING POLYGONAL IMPERVIOUS AREA RATIO DATA IN AN URBANIZED AREA

A HYPE (HYdrological Prediction for the Environment) model considering impervious land cover in an urbanized watershed is set up to improve prediction precision of storm runoff. The set-up of the model is based on the Urban Atlas that is polygonal land cover data with impervious information. Firstly accurate impervious area ratios in a target watershed are calculated using the Urban Atlas, and the ratios are set to the HYPE model. Secondly, appropriate parameters to describe hydrological property in an urbanized area are implemented for the HYPE model. The model was applied for the watershed including urbanized area of Svedala, Sweden. It was demonstrated that impervious information obtained by polygonal impervious area ratio data contributed precision improvement of runoff prediction by HYPE model in urbanized area.
Energy Efficient Uplink Transmission in CR OFDMA Network Under Fading Channel usingPricing Technique

This project investigates the sensing order problem for multi-user and multi-channel cognitive radio networks. The energy-efficient resource allocation in orthogonal frequency division multiple access-based cognitive radio (CR) technologies present great flexibility and practicability for future green wireless communications. To increase the energy efficiency and guarantee the primary transmitter’s quality-of service requirement using pricing factor. A linear pricing technique was introduced to handle both the inter-ST coupling Spectrum competition among secondary users and intra-ST coupling the relationship between available bandwidth and spectrum allocation. Simulation results demonstrate that the proposed strategy brings high EE. Additionally, by adjusting the pricing factors, a various performance can be achieved by the STs with different priorities. A distributed algorithm is devised to harvest the multi resource and multiuser gains, and the energy-efficient JSAPC is transformed to 1-D pricingfactor profile searching. Keywords— pricing factor, linear pricing technique, orthogonal frequency division multiple access (OFDMA), Cognitive radio networks (CRNs), energy efficiency (EE), JSPAC algorithm.
Gene Finding

Gene finding refers to identifying stretches of sequences (genes) in genomic DNA that are biologically functional. (Figure 1. shows the structure of a protein-coding gene in eukaryotes.) Gene finding is crucial in understanding the genome of a species. Figure 1: A protein-coding gene. (This picture is from Sanja Rogic's lecture slides, " Computational Gene Finding ") Approaches Computational methodology for finding genes in a genome has evolved significantly over the last 20 years. Many approaches have been proposed to find genes in both prokaryotes
Saccharomyces cerevisiae CellWall Remodeling in the Absence of Knr4 and Kre6 Revealed by Nano-FourierTransform Infrared Spectroscopy

The cell wall integrity (CWI) signaling pathway regulates yeast cell wall biosynthesis, cell division, and responses to external stress. The cell wall, comprised of a dense network of chitin, β-1,3- and β-1,6- glucans, and mannoproteins, is very thin, <100 nm. Alterations in cell wall composition may activate the CWI pathway. Saccharomyces cerevisiae, a model yeast, was used to study the role of individual wall components in altering the structure and biophysical properties of the yeast cell wall. Near-field Fourier transform infrared spectroscopy (nano-FT-IR) was used for the first direct, spectrochemical identification of cell wall composition in a background (wild-type) strain and two deletion mutants from the yeast knock-out collection: kre6Δ and knr4Δ. Killer toxin resistant 6 (Kre6) is an integral membrane protein required for biosynthesis of β-1,6-glucan, while Knr4 is a cell signaling protein involved in the control of cell wall biosynthesis, in particular, biosynthesis and deposition of chitin. Complementary spectral data were obtained with far-field (FF)-FT-IR, in transmission, and with attenuated total reflectance (ATR) spectromicroscopy with 3–10 μm wavelength-dependent spatial resolution. The FF-FT-IR spectra of cells and spectra of isolated cell wall components showed that components of the cell body dominated transmission spectra and were still evident in ATR spectra. In contrast, the nano-FT-IR at ∼25 nm spatial resolution could be used to characterize the yeast wall chemical structure. Our results show that the β-1,6-glucan content is decreased in kre6Δ, while all glucan content is decreased in the knr4Δ cell wall. The latter may be thinner than in wild type, since not only are mannan and chitin detectable by nano-FT-IR, but also lipid membranes and protein, indicative of cell interior.
Bacterial surface display of human lectins in Escherichia coli

Abstract Lectin‐glycan interactions sustain fundamental biological processes involved in development and disease. Owing to their unique sugar‐binding properties, lectins have great potential in glycobiology and biomedicine. However, their relatively low affinities and broad specificities pose a significant challenge when used as analytical reagents. New approaches for expression and engineering of lectins are in demand to overcome current limitations. Herein, we report the application of bacterial display for the expression of human galectin‐3 and mannose‐binding lectin in Escherichia coli. The analysis of the cell surface expression and binding activity of the surface‐displayed lectins, including point and deletion mutants, in combination with molecular dynamics simulation, demonstrate the robustness and suitability of this approach. Furthermore, the display of functional mannose‐binding lectin in the bacterial surface proved the feasibility of this method for disulfide bond‐containing lectins. This work establishes for the first time bacterial display as an efficient means for the expression and engineering of human lectins, thereby increasing the available toolbox for glycobiology research.
Revisiting Dynamic Processes and Relaxation Mechanisms in a Heterocyclic Glass-Former: Direct Observation of a Transient State

Despite decades of studies, a clear understanding of near-Tg phenomena remains challenging for glass-forming systems. This review delves into the intricate molecular dynamics of the small, heterocyclic thioether, 6-methyl-2,3-dihydro-1,4-benzodithiine (MeBzS2), with a particular focus on its near-Tg cold crystallization and relaxation mechanisms. Investigating isothermal crystallization kinetics at various temperatures reveals a significant interplay between its molecular dynamics and recrystallization from a supercooled liquid. We also identify two independent interconversion paths between energetically privileged conformers, characterized by strained transition states. We demonstrate that these spatial transformations induce substantial alterations in the dipole moment orientation and magnitude. Our investigation also extends to the complex salt PdCl2(MeBzS2), where we observe the transient conformers directly, revealing a direct relationship between their abundance and the local or macroscopic electric field. The initially energetically privileged isomers in an undisturbed system become less favored in the presence of an external electric field or ions, resulting even in an unexpected inversion of states. Consequently, we confirm the intramolecular character of secondary relaxation in MeBzS2 and its mechanism related to conformational changes within the heterocyclic ring. The research is based on the combination of broadband dielectric spectroscopy, X-ray diffraction, and quantum density functional theory calculations.
Transcutaneous auricular vagus nerve stimulation modulates the processing of interoceptive prediction error signals and their role in allostatic regulation

Abstract It has recently been suggested that predictive processing principles may apply to interoception, defined as the processing of hormonal, autonomic, visceral, and immunological signals. In the current study, we aimed at providing empirical evidence for the role of cardiac interoceptive prediction errors signals on allostatic adjustments, using transcutaneous auricular vagus nerve stimulation (taVNS) as a tool to modulate the processing of interoceptive afferents. In a within‐subject design, participants performed a cardiac‐related interoceptive task (heartbeat counting task) under taVNS and sham stimulation, spaced 1‐week apart. We observed that taVNS, in contrast to sham stimulation, facilitated the maintenance of interoceptive accuracy levels over time (from the initial, stimulation‐free, baseline block to subsequent stimulation blocks), suggesting that vagus nerve stimulation may have helped to maintain engagement to cardiac afferent signals. During the interoceptive task, taVNS compared to sham, produced higher heart‐evoked potentials (HEP) amplitudes, a potential readout measure of cardiac‐related prediction error processing. Further analyses revealed that the positive relation between interoceptive accuracy and allostatic adjustments—as measured by heart rate variability (HRV)—was mediated by HEP amplitudes. Providing initial support for predictive processing accounts of interoception, our results suggest that the stimulation of the vagus nerve may increase the precision with which interoceptive signals are processed, favoring their influence on allostatic adjustments.
Generative AI for Secure Physical Layer Communications: A Survey

Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, traditional AI approaches frequently struggle, primarily due to their limited capacity to dynamically adjust to the evolving physical attributes of transmission channels and the complexity of contemporary cyber threats. This adaptability and analytical depth are precisely where GAI excels. Therefore, in this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We first emphasize the importance of advanced GAI models in this area, including Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs). We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity. Furthermore, we also present future research directions focusing model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication, highlighting the multifaceted potential of GAI to address emerging challenges in secure physical layer communications and sensing.
What is the focus of XAI in UI design? Prioritizing UI design principles for enhancing XAI user experience

With the widespread application of artificial intelligence(AI), the explainable AI (XAI) field has undergone a notable resurgence. In this background, the importance of user experience in XAI has become increasingly prominent. Simultaneously, the user interface (UI) serves as a crucial link between XAI and users. However, despite the existence of UI design principles for XAI, there is a lack of prioritization based on their significance. This will lead practitioners to have a vague understanding of different design principles, making it difficult to allocate design space reasonably and emphasize design focal points. This paper aims to prioritize four design principles, providing clear guidance for UI design in XAI. Initially, we conducted a lightweight summary to derive five user experience standards for non-expert users in XAI. Subsequently, we developed four corresponding webpage prototypes for the four design principles. Nineteen participants then interacted with these prototypes, providing ratings based on five user experience standards, and We calculated the weights of the design principles. Our findings indicate that, for non-expert users,"sensitivity"is the optimal UI design principle (weight = 0.3296), followed by"flexibility"(weight = 0.3014). Finally, we engage in further discussion and summarization of our research results, and present future works and limitations.
Learning to Retrieve for Job Matching

Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting standardized entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming development of query models. In this paper, we discuss applying learning-to-retrieve technology to enhance LinkedIns job search and recommendation systems. In the realm of promoted jobs, the key objective is to improve the quality of applicants, thereby delivering value to recruiter customers. To achieve this, we leverage confirmed hire data to construct a graph that evaluates a seeker's qualification for a job, and utilize learned links for retrieval. Our learned model is easy to explain, debug, and adjust. On the other hand, the focus for organic jobs is to optimize seeker engagement. We accomplished this by training embeddings for personalized retrieval, fortified by a set of rules derived from the categorization of member feedback. In addition to a solution based on a conventional inverted index, we developed an on-GPU solution capable of supporting both KNN and term matching efficiently.
A Post-Newtonian Analysis of Regularized 4D-EGB Theory: Complete Set of PPN Parameters and Observational Constraints

We performed a post-Newtonian analysis of the regularized four-dimensional Einstein-Gauss-Bonnet gravitational theory (4D-EGB). The resulting metric differs from the classical parametrized post-Newtonian (PPN) formalism in that a new gravitational potential arises from the integration of the approximate field equations. We also investigated the conserved quantities and equations of motion for massive bodies and light rays to a certain degree. By computing the predicted periastron advance rate in a binary system, we obtained an observational constraint that is stronger than those of previous analyses. Although the usual 10 PPN parameters can still be derived within the PPN framework, an extra parameter is needed to account for the full post-Newtonian tests.
Explain to Question not to Justify

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. In this paper, we separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). We also argue that the area of RED XAI is currently under-explored and hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.