Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying RBBP7 as a Promising Diagnostic Biomarker for BK Virus-Associated Nephropathy

BK virus-associated nephropathy (BKVN) remains a major infectious complication due to powerful immunosuppression in kidney transplant recipients, and its histologic appearance can mimic rejection, leading to diagnostic and treatment dilemmas thus molecular diagnostic methods would be beneficial. We collected gene expression profiles of 169 kidney biopsies taken from BKVN, rejection, and stable functioning allografts, based on single sample gene set enrichment analysis and random forest algorithm, and three hallmark activities associated with DNA damage and proliferation were found to be relatively specific in BKVN. Subsequently, weighted gene co-expression network analysis and support vector machines (SVM) algorithm identified RBBP7 as a robust and promising biomarker with high accuracy in both training and validation cohorts (AUC =0.938, 0.977, respectively). Besides, potential drugs for BKVN treatment such as mepacrine were discovered, which may contribute to targeted antiviral therapy and effective patient management rather than simply reducing the doses of immunosuppressive agents in clinical practice. RBBP7 (retinoblastoma binding protein 7) serves as component of serval complexes that regulate chromatin metabolism and functions in affecting DNA replication and controlling cell proliferation. In this research, upregulation of RBBP7 was found to be associated with the higher infiltration of CD8 naïve T, iTreg, and neutrophil cells and the lower amounts of Th1, central memory T, NKT, CD8 T, and dendritic cells. Moreover, the infiltration of Th1, Th17, and NKT cells was steadily different between BKVN and rejection allografts through immune cell assessment. In conclusion, we identified and verified RBBP7 as a molecular biomarker for BKVN diagnosis, which demonstrated great distinguishing ability with allograft rejection and would support clinical decision-making.
Testing the Relationship Between Referral Marketing, Consumer Perception and Brand Loyalty In Ayurvedic Products Amongst Women

Ayurveda is an ancient medical method that uses a variety of floras and herbs in preventing sickness and maintaining healthy lifestyle in the Indian Sub-continent. Women consumers in India prefer to use ayurvedic products to protect themselves against medical disorders, personal care and beauty products [1]. During the last decade, the demand for ayurvedic products have grown significantly and therefore every business organization in this sector is aspiring to increase market share by understanding the need of the consumer as well as changing consumer perception through referral marketing and brand preferences. The purpose of this study is to identify links among referral marketing, customer perception, and brand loyalty in relation to ayurvedic products. A structured questionnaire was created for the purpose of survey, and a pilot study was conducted to ensure its validity. Working women and housewives, were the respondents from whom the data were collected. A total of 242 valid responses were collected from the respondents. To examine the link between the latent constructs, structural equation modelling was devised for the constructs- referral marketing, consumer perception and brand loyalty. The study found that referral marketing had positive effect on the perception of the consumer at β = 0.36, CR= 2.59, p=0.000, referral marketing on brand loyalty at β = 0.41, CR= 2.09, p<0.05, accepting the hypothesis H1(Referral marketing has a positive effect on consumer perception); and H2 ( Referral marketing has a positive effect on brand loyalty); Consumer perception affects brand loyalty at β = 0.18, CR= 1.95, p = 0.000, accepting this hypothesis H3. *(The paper presented at the 7th International Conference on Embracing Change & Transformation Innovation and Creativity 26-28 May 2022)
Systematic study of AGB stars in the intermediate-age globular clusters in the Magellanic Clouds

We performed systematic infrared observations of the intermediate-age Magellanic Clouds clusters NGC 419, NGC 1783 and NGC 1978. Mid-infrared stars discovered in NGC 419 and NGC 1978 are very red and must be undergoing intense mass loss (comparable to superwinds). They are probably carbon stars but do not seem to show any FIR excesses. Three optically visible carbon stars as well as (at least) 2 near-infrared carbon stars observed with ISOPHOT show 60 J-Lm excesses which may indicate mass loss in the past. It seems that the MIR stars are fainter than the AGB tip luminosity and that their MbolS are close to those of the transition luminosity from M type to C stars. Therefore, these MIR stars may not be in the final stage of the AGB phase. This may suggest that AGB stars lose mass heavily at some other time, possibly during the transition from M type to C stars.
Gas-phase H2O and CO2 toward massive protostars

We present a study of gas-phase H2O and CO2 toward a sample of 14 massive protostars with the Short Wavelength Spectrometer (SWS) on board the Infrared Space Observatory (ISO). Modeling of the H2O spectra using a homogeneous model with a constant excitation temperature T_ex shows that the H2O abundances increase with temperature, up to a few times 10^-5 with respect to H2 for the hottest sources (T_ex ~500 K). This is still a factor of 10 lower than the H2O ice abundances observed toward cold sources in which evaporation is not significant (Keane et al. 2001). Gas-phase CO2 is not abundant in our sources. The abundances are nearly constant for T_ex>~100 K at a value of a few times 10^-7, much lower than the solid-state abundances of ~1--3 times 10^-6 (Gerakines et al. 1999). For both H2O and CO2 the gas/solid ratio increases with temperature, but the increase is much stronger for H2O than for CO2, suggesting a different type of chemistry. In addition to the homogeneous models, a power law model has been developed for one of our sources, based on the physical structure of this region as determined from submillimeter data by van der Tak et al. (1999). The resulting H2O model spectrum gives a good fit to the data.
Potential Association of Hypernatremia With Mortality in Patients With Acute Kidney Injury and COVID-19

Background The outcome of acute kidney injury (AKI) in patients with COVID-19 and the factors associated with its outcome, including mortality, are understudied among the Indian population. Objective The objective of this study is to determine the outcome of AKI in a cohort of patients with COVID-19 admitted to medical wards and associated intensive care unit (ICU) and the factors associated with its outcome, including mortality. Method This is a retrospective study of patients with COVID-19 and AKI admitted to a tertiary care hospital. A total of 1765 patients were admitted to a hospital with COVID-19 between March 23, 2021, and June 30, 2021, during the second wave of the pandemic chiefly attributed to SARS-Co-V-2 lineage B.1. 617. Patients with AKI for whom a nephrology call was sought for management (N=60) were included. Measurements carried out were the stage of AKI, co-morbidities, ICU admission, mechanical ventilation, lab parameters, and mortality. We classified AKI by comparing the highest to lowest recorded serum creatinine in hospital and staged AKI based on the Kidney Disease: Improving Global Outcomes (KDIGO) system. We further developed stepwise logistic regression models to find independent factors associated with mortality. Results Out of the 1765 patients hospitalized with COVID-19, a total of 60 (3.4%) patients with AKI were referred to nephrology for management. The observed mortality in this cohort was 41/60 (68.3%). AKI stage 3 was observed to be the most common (78.3%). Based on a univariate analysis of association, age, chronic kidney disease, admission to ICU, the requirement for vasopressor and ventilation, lactate dehydrogenase (LDH) DH, liver function tests (LFT), hypernatremia, and leucocytosis were associated with the mortality of patients (p<0.05) with AKI and COVID-19 infection. Multivariate analysis using logistic regression led to the identification of hypernatremia (OR 5.24 {0.95-42.31}) and multiple co-morbidities (OR 2.59 {1.03-8.75}, p<0.07) as potential factors independently associated with mortality. Conclusion The study indicates the potential association of hypernatremia with mortality in AKI, along with the simultaneous presence of multiple co-morbidities with COVID-19. As the statistical power of the association is weak, we are claiming the association as potential only. It needs to be confirmed in other larger studies.
Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality management. A series of deep learning (DL) models that suit computer vision tasks, namely the convolutional neural networks (CNNs), are used to predict the compressive strength of ready-mixed concrete. To demonstrate the efficacy of computer vision-based prediction, its effectiveness using imaging numerical data was compared with that of the deep neural networks (DNNs) technique that uses conventional numerical data. Various DL prediction models were compared and the best ones were identified with the relevant concrete datasets. The best DL models were then optimized by fine-tuning their hyperparameters using a newly developed bio-inspired metaheuristic algorithm, called jellyfish search optimizer, to enhance the accuracy and reliability. Analytical experiments indicate that the computer vision-based CNNs outperform the numerical data-based DNNs in all evaluation metrics except the training time. Thus, the bio-inspired optimization of computer vision-based convolutional neural networks is potentially a promising approach to predict the compressive strength of ready-mixed concrete.
Single-Circulating Tumor Cell Whole Genome Amplification to Unravel Cancer Heterogeneity and Actionable Biomarkers

The field of single-cell analysis has advanced rapidly in the last decade and is providing new insights into the characterization of intercellular genetic heterogeneity and complexity, especially in human cancer. In this regard, analyzing single circulating tumor cells (CTCs) is becoming particularly attractive due to the easy access to CTCs from simple blood samples called “liquid biopsies”. Analysis of multiple single CTCs has the potential to allow the identification and characterization of cancer heterogeneity to guide best therapy and predict therapeutic response. However, single-CTC analysis is restricted by the low amounts of DNA in a single cell genome. Whole genome amplification (WGA) techniques have emerged as a key step, enabling single-cell downstream molecular analysis. Here, we provide an overview of recent advances in WGA and their applications in the genetic analysis of single CTCs, along with prospective views towards clinical applications. First, we focus on the technical challenges of isolating and recovering single CTCs and then explore different WGA methodologies and recent developments which have been utilized to amplify single cell genomes for further downstream analysis. Lastly, we list a portfolio of CTC studies which employ WGA and single-cell analysis for genetic heterogeneity and biomarker detection.
Heliospheric Current Sheet North-South Asymmetry Since 1926

A method for forming an image comprising the steps of: (a) imagewise exposing a light-sensitive material comprising a support having thereon a light-sensitive layer containing an image-forming material encapsulated in microcapsules comprising a polymer; (b) superposing thereon an image-receiving material comprising a support having thereon an image-receiving layer in contact with the outermost layer of said light-sensitive material; (c) applying sufficient pressure to said superposed materials to imagewise rupture at least part of said microcapsules, to transfer an image from said light-sensitive layer to said image-receiving layer, and to adhere said light-sensitive material and said image-receiving material to form a unitary structure; at least one of said support for said light-sensitive material and said support for said image-receiving material being transparent.
Characterization of Pectin from Grape Pomace: A Comparison of Conventional and Pulsed Ultrasound-Assisted Extraction Techniques

The yield, physicochemical and rheological parameters of grape pomace pectin (Fetească Neagră and Rară Neagră) obtained by conventional extraction (CE) were compared to those acquired by pulsed ultrasound-assisted extraction (PUAE). Extraction temperature (70–90 °C), pH (1–3) and time (1–3 h) were considered as independent variables for CE, while amplitude (20–100%), pH (1–3) and time (20–60 min) for PUAE. The optimal conditions for maximum yield and physicochemical parameters of pectin samples extracted by CE were temperature of 90 °C, pH 1.9 for 164 min (9.96% yield, 79.91 g/100 g of galacturonic acid (GalA) content, 81.28% of degree of esterification (DE) and 5.52 × 104 g/mol of molecular weight (Mw) for Fetească Neagră (FN) pectin; 11.08% yield, 80.05 g/100 g of GalA content, 80.86% of DE and 5.59 × 104 g/mol of Mw for Rară Neagră (RN) pectin), while for PUAE they were amplitude of 100%, pH 1.8 for 60 min (8.83% yield, 80.24 g/100 g of GalA content, 81.07% of DE and 4.19 × 104 g/mol of Mw for FN pectin; 8.94% yield, 78.64 g/100 g of GalA content, 80.04% of DE and 4.23 × 104 g/mol of Mw for RN pectin). The yield and physicochemical parameters of CE pectin were higher than PUAE pectin. The FT-IR spectra of pectin samples revealed the occurrence of polysaccharide compound, while rheology characteristics confirming its application in different food products.
The Use of Genetic Algorithms in Multilayer Mirror Optimization

This paper describes the genetic algorithm applied to multilayer mirror optimization. An explanation of how genetic algorithms work is given and how this algorithm was applied to the design of bifunctional mirrors for the IMAGE Mission. This paper also discusses some findings that contradict previous design rules for multilayer mirrors. Some of these are: aperiodic mirrors performing better than periodic mirrors and an oxide producing a better design than a mirror with just elements as materials. Using the genetic algorithm, the best mirror design found for the IMAGE Mission was an aperiodic Y2O3/Al 16 layer stack on SiO2. This design had a predicted reflectivity of 36% at 304 A and . 2% at 584 A.
A row space method for solving a system of linear equations

A new algorithm is presented for computing a direct solution to a system of consistent linear equations. It produces a minimum norm particular solution, a generalized inverse (of type {124}), and a null space projection operator. In addition, the algorithm permits an online formulation so that computations may proceed as the data become available. The algorithm does not require the solution of a triangular system of equations, nor does it rely on block partitioned matrices.
What limits Muslim communities’ access to nature? Barriers and opportunities in the United Kingdom

Recreation in protected areas can greatly contribute to health and wellbeing, but there exist significant demographic disparities in protected area use across Europe and North America. Minority ethnic groups, in particular, are often underrepresented in protected areas due to a variety of cultural, economic, and discriminatory barriers. Muslims are one of the fastest growing minority ethnic communities in Western countries but have received little study in the context of protected areas. More research is therefore needed to understand protected area exclusion as experienced by Muslim communities. Through 14 in-depth interviews with Muslim community leaders in the United Kingdom, we explored the socio-cultural barriers and opportunities that contribute to the accessibility of protected areas for Muslims. As the majority of our participants were women, this study addresses the underrepresentation of diverse female voices in research on Muslims and leisure and foregrounds Muslim women as agents in their recreational lives. We found that a wide variety of factors can inhibit access, cumulatively resulting in several layers of exclusion. Primary barriers included a lack of inclusive imagery, insufficient facilities for social gathering, prior instances of discrimination, the perceived whiteness of protected areas, and unfamiliarity with these spaces. Several barriers related to the wilderness ideology that is embedded across many aspects of protected area marketing, design, and management as it does not embrace collectivist aspects of Muslim cultures. The level of “naturalness” associated with protected areas, however, did not emerge as a barrier. We also identified many opportunities, including the stewardship role of humans depicted within Islam. Lastly, we discuss the management implications for protected areas that emerge from our results. This research demonstrates that to foster a sense of belonging for Muslim communities, protected area managers must consider many socio-cultural dimensions of accessibility, holistically engage with Muslim communities, and embrace diverse worldviews relating to the human–nature relationship.
Representation and regularity for the Dirichlet problem for real and complex degenerate Hessian equations

We consider the Dirichlet problem for positively homogeneous, degenerate elliptic, concave (or convex) Hessian equations. Under natural and necessary conditions on the geometry of the domain, with the $C^{1,1}$ boundary data, we establish the interior $C^{1,1}$-regularity of the unique (admissible) solution, which is optimal even if the boundary data is smooth. Both real and complex cases are studied by the unified (Bellman equation) approach.
Regression Models for Haplotype-Based Association Studies

In this paper, we provide an overview of statistical models for haplotype-based association studies, and summarize their features based on the design matrix. We classify the design matrix into the two types: direct and indirect. For these two kinds of matrices, we present and compare characteristics using a simple hypothetical example, and a real data set. The motivation behind this study was to provide practitioners with an improved understanding, to facilitate the informed selection of the appropriate haplotype-based model and to improve the interpretability of the models.
Regulation of Amylose Content by Single Mutations at an Active Site in the Wx-B1 Gene in a Tetraploid Wheat Mutant

The granule-bound starch synthase I (GBSSI) encoded by the waxy gene is responsible for amylose synthesis in the endosperm of wheat grains. In the present study, a novel Wx-B1 null mutant line, M3-415, was identified from an ethyl methanesulfonate-mutagenized population of Chinese tetraploid wheat landrace Jianyangailanmai (LM47). The gene sequence indicated that the mutated Wx-B1 encoded a complete protein; this protein was incompatible with the protein profile obtained using sodium dodecyl sulfate–polyacrylamide gel electrophoresis, which showed the lack of Wx-B1 protein in the mutant line. The prediction of the protein structure showed an amino acid substitution (G470D) at the edge of the ADPG binding pocket, which might affect the binding of Wx-B1 to starch granules. Site-directed mutagenesis was further performed to artificially change the amino acid at the sequence position 469 from alanine (A) to threonine (T) (A469T) downstream of the mutated site in M3-415. Our results indicated that a single amino acid mutation in Wx-B1 reduces its activity by impairing its starch-binding capacity. The present study is the first to report the novel mechanism underlying Wx-1 deletion in wheat; moreover, it provided new insights into the inactivation of the waxy gene and revealed that fine regulation of wheat amylose content is possible by modifying the GBSSI activity.
Anesthetic efficacies of buccal with palatal injection versus buccal with intra-septal injection in permanent maxillary first molars of pediatric patients

The high success rate of dental treatment is dependent on the cooperation of pediatric patients during procedures. Dental treatment often causes pain, particularly in children. The factors in providing treatment to pediatric patients include the characteristics and location of the tooth, profoundness of the anesthesia including the type of local anesthetic, and cooperation of the patient. Previous studies have examined several techniques to successfully achieve profound pulpal anesthesia in maxillary permanent teeth. The dentist should select the injection technique to be used based on patient needs. In children, either buccal with palatal injections or buccal with intra-septal injections may be used to anesthetize the permanent maxillary first molar. Buccal with palatal injections are commonly used prior to routine maxillary dental procedures. Currently, there are only a few studies on the employment of buccal with intra-septal injections to anesthetize permanent maxillary first molars in pediatric patients. This review will focus on efficacy of buccal with palatal versus buccal with intra-septal pulpal anesthesia of the permanent maxillary first molars in pediatric patients and aim to determine which technique should be used during routine dental procedures.
Feature extraction techniques for cognitive stimuli-based electroencephalogram signals: an experimental analysis

Over the past decades, brain-computer interface (BCI) has gained a lot of attention in various fields ranging from medicine to entertainment, and electroencephalogram (EEG) signals are widely used in BCI. Brain-computer interface made human-computer interaction possible by using information acquired from EEG signals of the person. The raw EEG signals need to be processed to obtain valuable information which could be used for communication purposes. The objective of this paper is to identify the best combination of features that could discriminate cognitive stimuli-based tasks. EEG signals are recorded while the subjects are performing some arithmetical based mental tasks. Statistical, power, entropy, and fractional dimension (FD) features are extracted from the EEG signals. Various combinations of these features are analyzed and validated using random forest classifier, K-nearest neighbors (KNN), multilayer perceptron, linear discriminant analysis, and support vector machine. The combination of entropy-FD features gives the highest accuracy of 90.47% with the KNN algorithm when compared to individual entropy and FD features which achieves 79.36% with random forest classifier, multilayer perceptron, and 82.53% with linear discriminant analysis, respectively. Our results show that the hybrid of entropy-FD features with KNN classifier can efficiently classify the cognition-based stimuli.