Miracle-dz/UniFAR-FTUs huggingface dataset:
Antidiabetic Potential and Chronic Toxicity of Hydroalcoholic Extract of Echeveria subrigida Leaves

Background: Echeveria subrigida extracts have biological activities of human health importance. However, the in vivo effects on glucose levels and chronic toxicity are unknown. Objectives: To analyse the in vivo hypoglycemic and antihyperglycemic effects (50, 100, and 200 mg/kg b.w.) and chronic toxicity (1000 mg/kg b.w. for 270 days) of the hydroalcoholic extract of E. subrigida (HE–Es) in BALB/c mice. Materials and Methods: The HE–Es was analysed by HPLC. Glucose levels were measured to establish the effects on glycemia. Different parameters were registered in the toxicity assay: e.g. , feed consumption, appearance/behaviour, biochemical and haematological parameters and liver and kidney histologies. Results: Quercetin–3-O -β-glucoside and isorhamnetin–3-O -β-glucoside were the main flavonoids in the HE–Es. Glycemia was reduced by the HE–Es (200 mg/kg b.w., 49.1%) and glibenclamide (10 mg/kg b.w., 52%) treatments. Comparing the antihyperglycemic activities, similar reductions were found between HE–Es (100 mg/kg b.w., 29.32%; 200 mg/kg, 28.99%) and acarbose (10 mg/kg b.w., 19.87%) treatments. On the other hand, the HE–Es was innocuous in mice (LD 50 > 1000 mg/kg b.w.), and the results suggested that HE–Es had adaptogenic, and immunostimulant activities. The hepatic and renal histologies were normal; however, male mice showed level zero steatosis that disappeared in the HE–Es satellite after treatment withdrawal. Conclusion: This study reports for the first time the in vivo effects on blood glucose levels of HE–Es and chronic toxicity of a Crassulaceae plant, supporting the antidiabetic potential and safety of HE–Es. Future studies must corroborate the effects of HE–Es in humans, allowing its use in high-value formulations.
Universal Primal-Dual Proximal-Gradient Methods

We propose a primal-dual algorithmic framework for a prototypical constrained convex minimization problem. The new framework aims to trade-off the computational difficulty between the primal and the dual subproblems. We achieve this in our setting by replacing the standard proximal mapping computations with linear minimization oracles in the primal space, which has been the hallmark of the scalable Frank-Wolfe-type algorithms. Our analysis extends Nesterov's universal gradient methods to the primal-dual setting in a nontrivial fashion, and provides optimal convergence guarantees for the objective residual as well as the feasibility gap without having to know the smoothness structures of the problem. As a result, we obtain stronger primal-dual convergence results than the existing Frank-Wolfe-type algorithms for important machine learning problems involving sparsity and low-rank, and also illustrate them numerically.
Emotion Detection From Micro-Blogs Using Novel Input Representation

Emotion is a natural intrinsic state of mind that drives human behavior, social interaction, and decision-making. Due to the rapid expansion in the current era of the Internet, online social media (OSM) platforms have become popular means of expressing opinions and communicating emotions. With the emergence of natural language processing (NLP) techniques powered by artificial intelligence (AI) algorithms, emotion detection (ED) from user-generated OSM data has become a prolific research domain. However, it is challenging to extract meaningful features for identifying discernible patterns from the short, informal, and unstructured texts that are common on micro-blogging platforms like Twitter. In this paper, we introduce a novel representation of features extracted from user-generated Twitter data that can capture users’ emotional states. An advanced approach based on Genetic Algorithm (GA) is used to construct the input representation which is composed of stylistic, sentiment, and linguistic features extracted from tweets. A voting ensemble classifier with weights optimized by a GA is introduced to increase the accuracy of emotion detection using the novel feature representation. The proposed classifier is trained and tested on a benchmark Twitter emotion detection dataset where each sample is labeled with either of the six classes: sadness, joy, love, anger, fear, and surprise. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art classical machine learning-based emotion detection techniques, achieving the highest level of precision (96.49%), recall (96.49%), F1-score (96.49%), and accuracy (96.49%).
Fabrication of Germanosilicate glass optical fibers containing Tm/sup 2+/ ions and their nonlinear optical properties

Germanosilicate glass optical fibers incorporated with the Tm/sup 2+/ ions were fabricated to enhance optical nonlinearity by providing a strong reduction environment based on the solution doping technique in the modified chemical vapor deposition (MCVD) process. The incorporation of the Tm/sup 2+/ ions into the fiber core was identified by the electron paramagnetic resonance (EPR) spectrum in the fiber preform, and the absorption and emission properties between 350 and 1600 nm of the Tm/sup 2+/ ions in optical fibers and the fiber preform. A strong broad absorption band due to the Tm/sup 2+/ ions appeared from 350 to /spl sim/900 nm, and a broad emission from /spl sim/600 to /spl sim/1050 nm and the other emission from /spl sim/1050 to /spl sim/1300 nm, which were not shown in the Tm/sup 3+/ ions, were found upon Ar-ion laser pumping at 515 nm. Both absorption and emission results confirm that the Tm/sup 2+/ ions in the germanosilicate glass have the 4f-5d energy band from 350 to /spl sim/900 nm and the 4f-4f energy level at /spl sim/1115 nm. Also, the resonant nonlinearity at /spl sim/1310 and /spl sim/1530 nm due to the Tm/sup 2+/ ions in the fiber was measured upon the 515 nm optical pumping by using a long-period fiber grating (LPG) pair method. The nonlinear refractive index n/sub 2/ at /spl sim/1310 and /spl sim/1530 nm was found to be /spl sim/4/spl times/10/sup -15/ m/sup 2//W, where 70% and 30% of the n/sub 2/ are attributed to the nonradiative transitions and the radiative transitions, respectively.
Interpretation of unresolved transition arrays in the soft-x-ray spectra of highly ionized molybdenum and palladium

Previously published theoretical formulas for the mean wavelengths and spectral widths of unresolved transition arrays are applied to transitions of the types $3{d}^{N+1}\ensuremath{-}3{d}^{N}4p$ and $3{d}^{N+1}\ensuremath{-}3{d}^{N}4f$ observed in highly ionized spectra of Mo and Pd. Agreement with experiment is excellent and opens the way to the quantitative interpretation of the intensities.
Surfing the Waves: Differences in Hospitalised COVID-19 Patients across 4 Variant Waves in a Belgian University Hospital

The unprecedented COVID-19 pandemic took the form of successive variant waves, spreading across the globe. We wanted to investigate any shift in hospitalised patients’ profiles throughout the pandemic. For this study, we used a registry that collected data automatically from electronic patient health records. We compared clinical data and severity scores, using the National Institute of Health (NIH) severity scores, from all patients admitted for COVID-19 during four SARS-CoV-2 variant waves. Our study concluded that patients hospitalised for COVID-19 showed very different profiles across the four variant waves in Belgium. Patients were younger during the Alpha and Delta waves and frailer during the Omicron period. ‘Critical’ patients according to the NIH criteria formed the largest fraction among the Alpha wave patients (47.7%), while ‘severe’ patients formed the largest fraction among Omicron patients (61.6%). We discussed host factors, vaccination status, and other confounders to put this into perspective. High-quality real-life data remain crucial to inform stakeholders and policymakers that shifts in patients’ clinical profiles have an impact on clinical practice.
Influence of various design parameters on the quality of optimal shape design in topology optimization analysis

The paper presents the discrete structural topology optimization problem. The analysis was made for the formulation of optimal design with a compliance minimization (as the objective functional) of statically loaded continuum structures with constraints forced on mass of the structure. Two different algorithms were compared (one taken from the literature and the second originally prepared) in order to study influence of design parameters formulation on the calculations results. Optimal topologies were presented and discussed for several 2D examples, with various optimization process parameters in order to get the best results in the shortest possible time. Other goal of presented research is to find out the possibilities of applying proposed algorithm in the designing process of bridge girders. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Psychometric properties of the English language version of the C-BiLLT evaluated in typically developing Canadian children

PURPOSE: This study aimed to 1) investigate the convergent and discriminant validity, internal consistency, and test-retest reliability of the Canadian English version of the Computer-Based instrument for Low motor Language Testing (C-BiLLT-CAN), and 2) explore feasibility of the C-BiLLT assessment for children with cerebral palsy (CP) and complex communication needs in the Canadian health care context. METHODS: Eighty typically developing children between 1.5 and 8.5 years of age completed the C-BiLLT-CAN, the Peabody Picture Vocabulary Test-IV (PPVT-4), the receptive language sub-test of the New Reynell Developmental Language Scales (NRDLS), and/or the Raven’s 2. Correlations between raw scores were calculated for estimates of convergent and discriminant validity. Internal consistency was calculated for all items and separately for items pertaining to vocabulary and grammar. To calculate the standard error of measurement (SEM) and intraclass correlation coefficient (ICC), 33 participants were re-tested with the C-BiLLT within three weeks. Feasibility was explored with nine participants with CP. RESULTS: C-BiLLT-CAN’s convergent validity was good to excellent (Spearman’s rho > 0.78) and discriminant validity was higher than hypothesized (Spearman’s rho > 0.8). Internal consistency (Cronbach’s alpha = 0.96), test-retest reliability (ICC > 0.9), and measurement error (SEM < 5%) were excellent. The feasibility study could not be fully completed due to the COVID-19 pandemic. Preliminary data demonstrated some technical and practical barriers for using the C-BiLLT in children with CP in Canada. CONCLUSION: The C-BiLLT-CAN showed good to excellent psychometric properties in a sample of typically developing children, indicating that it is an adequate test for measuring language comprehension in English-speaking Canadian children. Further research is needed to investigate the feasibility of the C-BiLLT-CAN in children with CP.
Aligning Comments to News Articles on a Budget

Disagreement among text annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms for natural language processing. Using only high agreement annotations introduces another challenge: the data imbalance problem. We study this challenge within the problem of relating user comments to the content of a news article. We show that traditional techniques for learning from imbalanced data, such as oversampling, using weighted loss functions, or assigning weak labels using crowdsourcing, may not be sufficient for modeling complex temporal relationships between news articles and user comments. In this study, we propose a framework for aligning comments and articles 1) from imbalanced news data characterized with 2) different degrees of annotator agreement, under 3) a constrained budget for human labeling and computing resources. Within the framework, we propose a Semi-Automatic Labeling solution based on Human-AI collaboration. We compare our proposed technique with traditional data imbalance handling techniques and synthetic data generation on the article-comment alignment problem, where the goal is to determine a category of an article-comment pair that represents how relevant the comment is to the article. Finding an effective and efficient solution is essential because it is time-consuming and prohibitively costly to manually label a sufficiently large amount of article-comment pairs based on the semantic understanding of an article and its comments. We discover that the Human-AI collaboration outperforms all alternative techniques by 17% of article-comment alignment accuracy. When there is no time or budget for re-labeling some article-comment pairs, we found that synonym augmentation is a reasonable alternative. We also provide a detailed analysis of the effect of humans in the loop and the use of unlabeled data.
Perceived knowledge needs of occupational therapists for evaluating seniors with cognitive impairments

Abstract Aims This study describes the knowledge and tool needs identified by occupational therapists to improve their evaluation of older adults facing cognitive impairments and identify their preferred strategies for knowledge translation. Methods A descriptive qualitative study design was used. Four focus groups were conducted with 16 occupational therapists working with cognitively impaired older adults in institutional and community care settings. An inductive approach was used to analyze the thematic content of the interviews. Results Occupational therapists identified a need for up-to-date knowledge of cognitive systems and disorders and knowledge related to legal capacity and protection regimes. A need for short standardized performance-based tests was also identified. Preferred knowledge translation strategies included a combination of formal learning activities and peer interactions. Conclusion Occupational therapists require an up-to-date knowledge base and specialized tools to assess this population. Strategies to translate existing standardized performance-based instruments into practice may contribute to meeting these needs.
Excellent Responsivity and Low Dark Current Obtained With Metal-Assisted Chemical Etched Si Photodiode

Metal-assisted chemical etched (MACE; also known as MacEtch or MCCE) nanostructures are utilized widely in the solar cell industry due to their excellent optical properties combined with a simple and cost-efficient fabrication process. The photodetection community, on the other hand, has not shown much interest toward MACE due to its drawbacks, including insufficient surface passivation, increased junction recombination, and possible metal contamination, which are especially detrimental to p-n photodiodes. Here, we aim to change this by demonstrating how to fabricate high-performance MACE p-n photodiodes with above 90% external quantum efficiency (EQE) without external bias voltage at 200–1000 nm and dark current less than 3 nA/cm2 at −5 V using industrially applicable methods. The key is to utilize an induced junction created by an atomic layer deposited (ALD) highly charged Al2O3 thin film that simultaneously provides efficient field-effect passivation and full conformality over the MACE nanostructures. Achieving close to ideal performance demonstrates the vast potential of MACE nanostructures in the fabrication of high-performance low-cost p-n photodiodes.
Separation of Methanolic Leaf Extracts of Three Rutaceous Plants by Capillary Electrophoresis and High-Performance Liquid Chromatography Methods

Introduction: In the phytochemical analysis of various multi component mixtures, Capillary electrophoresis (CE) method was considered as the best alternative tool due to high efficiency resolution separations, low solvent consumption and less maintenance costs. Materials and Methods: This study uses CE and high-performance liquid chromatography (HPLC) methods to evaluate the major components of methanolic leaf extract of Rutaceous plants. And also, to assess the purity of the components in isolated fractions and most importantly for the qualitative identification of the individual components of extracts based upon the relative retention time and UV profile. Results: The separation of the compounds in HPLC was attained by using ACE-5-C 18 (250 x 4.6 mm) with a flow rate of 1.5 ml/min, with the UV detection at 254 nm and CE was equipped with a UV diode-array detector and bare, fused silica capillaries (56 cm 50 μm) with micro extended light path were used for the separations. Conclusion: Our study reveals that CE technique is a simple, economical and convenient method and shown high-resolution for multi-components found in plant extracts compared with HPLC profiles.
Improved cationic stoichiometry and insulating behavior at the interface of LaAlO3/SrTiO3 formed at high oxygen pressure during pulsed-laser deposition

Medium-energy ion spectroscopy, MEIS, and scanning transmission electron microscopy, STEM, were used to correlate the atomic structure of LaAlO3/SrTiO3 interfaces with their electrical properties. Interfaces were prepared at high (5×10− 2 mbar) and low (10− 4 mbar) oxygen pressure by pulsed-laser deposition. The high-oxygen-pressure heterostructures were insulating for all thicknesses while the low-oxygen-pressure ones became metallic for thicknesses above 4 unit cells. MEIS data show enhancement of the Sr surface peak and suppression of the La one in interfaces prepared at low oxygen pressure, which is interpreted as a La-Sr intermixing. The effect was considerably smaller in high-oxygen-pressure samples. Analysis of high-angle annular-dark-field STEM images of the LAO films also indicates intermixing between La and Sr in low-oxygen-pressure samples, supporting MEIS data. Our results reveal the important role of oxygen pressure on the formation of the interface electron gas.
Spectral Irradiance Calibration in the Infrared. XVI. Improved Accuracy in the Infrared Spectra of the Secondary and Tertiary Standard Calibration Stars

We have increased the spectral resolution and accuracy of the absolute infrared spectral flux for a subset of stellar standards created by Cohen and his collaborators in previous papers in this series. We combined the moderate-resolution (λ/Δλ ∼ 400) spectroscopy obtained on infrared standard stars by the Short Wavelength Spectrometer on the Infrared Space Observatory with high-quality photometry that is tied to our recent absolute calibration from the Midcourse Space Experiment. New spectra were created for 33 stars, 9 of which are Cohen et al. secondary standards and another 20 of which are tertiary standards for which Cohen adopted spectral templates. The effective temperatures and angular diameters for the stars are derived from the absolute spectra and compare favorably with independent measures of these quantities in the literature. The present spectra display systematic differences with those of Cohen and colleagues in that they have 4%-7% lower fluxes in the 1-4 μm spectral region. Our spectra remove the A-K star calibration bias recently noted in the calibration of the Spitzer Space Telescope camera.
Bolstering the Public Health Workforce: Recruitment and Retention of Public Health Majors

Undergraduate public health programs play a dual role in training the next generation of public health professionals while equipping nonmajors with basic public health knowledge. Our study aimed to understand if introductory public health courses were effective for recruitment and retention of majors. We used 5 years of institutional data to explore the public health course-taking and major enrollment patterns for students who enrolled in two introductory public health courses at a research-intensive, minority-serving institution. For students who enrolled in Principles of Public Health (PH1), 73% went on to take one or more additional public health courses. Trends show that most took Case Studies in Public Health Practice (PH2) after PH1 during their first 2 years in college, and the majority of nonmajors who took PH1 in their second year enrolled in PH2 within one quarter. Seventy-two percent of students who enrolled in both PH1 and PH2 graduated in public health regardless of their major at entry to the university, and 77% of public health graduates joined through a change of major. Importantly, 19% of all students took at least one public health course. Enrollment in both PH1 and PH2 may be important for major recruitment and retention, and efforts to encourage students to consider or stay in the public health major during these courses should be considered. Our analyses highlight that introductory public health courses provide critical opportunities for students to learn about public health and may serve as gateways to the major and possibly a public health career.
Latent Variables and Propensity Score Matching

This paper examines how including latent variables can benefit propensity score matching. A researcher can estimate, based on theoretical presumptions, the latent variable from the observed manifest variables and can use this estimate in propensity score matching. This paper demonstrates the benefits of such an approach and compares it with a method more common in econometrics, where the manifest variables are directly used in matching. We intuit that estimating the propensity score on the manifest variables introduces a measurement error that can be limited when estimating the propensity score on the estimated latent variable. We use Monte Carlo simulations to test how various matching methods behave under distinct circumstances found in practice. Also, we apply this approach to real data. Using the estimated latent variable in the propensity score matching increases the efficiency of treatment effect estimators. The benefits are larger for small samples, for non-linear processes, and for a large number of the manifest variables available, especially if they are highly correlated with the latent variable.
Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.