Miracle-dz/UniFAR-FTUs huggingface dataset:
A Risk–Benefit Analysis of First Nation’s Traditional Smoked Fish Processing

First Nations (FN) communities have traditionally used smoke to preserve fish for food security purposes. In this study, an assessment of chemical and microbiological food safety, together with nutritional quality, was conducted on fish preserved using traditional smoke processing. High-molecular-weight polycyclic aromatic hydrocarbons (PAH) residues accounted for only 0.6% of the total PAH in traditionally fully smoked salmon, and Benzo(a)pyrene (B(a)P) was not detected in the FN smoked or commercial smoked fish, respectively. The antimicrobial activity of the solvent extracts derived from smoked fish towards Listeria innocua was very low but detectable. The practice of using full and half-smoked processing for fish reduced all of the fatty acid concentrations and also minimized the further loss of essential omega-3 fatty acids to a greater extent than non-smoked fish during storage (p < 0.05). This finding corresponded to lower (p < 0.05) lipid oxidation in smoked fish. We conclude that the benefits of reducing lipid oxidation and retaining essential fatty acids during storage, together with a potentially significant reduction in Listeria contamination, are notable benefits of traditional smoke processing. Although B(a)P was not detected in FN smoked fish, attention should be given to controlling the temperature and smoking period applied during this processing to minimize potential long-term risks associated with PAH exposure.
On Local Polynomial Modelling of the Additive Risk Model

The additive risk model provides an alternative modelling technique for failure time data to the proportional hazards model. In this article, we consider the additive risk model with a nonparametric risk effect. We study estimation of the risk function and its derivatives with a parametric and an unspecified baseline hazard function respectively. The resulting estimators are the local likelihood and the local score estimators. We establish the asymptotic normality of the estimators and show that both methods have the same formula for asymptotic bias but different formula for variance. It is found that, in some special cases, the local score estimator is of the same efficiency as the local likelihood estimator though it does not use the information about the baseline hazard function. Another advantage of the local score estimator is that it has a closed form and is easy to implement. Some simulation studies are conducted to evaluate and compare the performance of the two estimators. A numerical example is used for illustration.
Electrochemical Detection of Olivetol Based on Poly(L-Serine) Film Layered Copper Oxide Modified Carbon Paste Electrode (p-L-Serine/CuO/CPE)

Olivetol is an important polyphenol compound and intermediate in the synthesis of cannabinoids possessing many types of biological activities. A facile electrochemical sensor for olivetol was fabricated based on p-L-serine, and copper oxide (CuO) nanoparticles modified carbon paste electrode (p-L-serine/CuO/CPE). The proposed p-L-serine/CuO/CPE was applied to the electrochemical detection of olivetol by cyclic voltammetry (CV) and differential pulse voltammetric (DPV). Through the characterizations of materials and modified electrodes, the p-L-serine/CuO/CPE exhibited enhanced electrochemical signals for olivetol compared to bare CPE and CuO/CPE in both CV and DPV methods. Under the optimized conditions, the proposed p-L-serine/CuO/CPE showed a good quantitative analysis ability and a wide analysis range from 20 to 100 μmol L−1 of olivetol with a limit of detection of 1.04 μmol L−1. Based on the reproducibility, repeatability, and stability exhibited by this fabricated sensor and the cheap and accessible raw materials, the p-L-serine/CuO/CPE became a novel determination choice for olivetol in the electrochemical method with the advantages of being cost-effective and convenient.
The Correct Probability Distribution for the Phase Dispersion Minimization Periodogram

The phase dispersion minimization (PDM) statistic is a popular method for searching for nonsinusoidal pulsations. The method involves comparison of the PDM value with the Fisher-Snedecor F distribution to assess the significance of pulsations. Later, it was demonstrated that the PDM statistic does not follow the F distribution and that the sensitivity of the method in its original form, based on the F distribution, is poor. In the present paper we demonstrate that the PDM statistic follows a beta distribution, which we designate PDM*. We find that the significance of a given detected pulsation according to the relevant beta distribution is higher than the corresponding significance obtained using the F distribution. In addition, we demonstrate that all methods relying on phase binning and variance estimates related to χ2 are equivalent for a given sampling, binning, and weighting pattern. We conclude by observing that with the invention of the high-performance Fourier series method, based on orthogonal projections, the original motivation for the use of phase binning for nonsinusoidal signals has weakened considerably.
Video-Based Behaviorally Coded Movement Assessment for Adolescents with Intellectual Disabilities: Application in Leg Dribbling Performance

Measuring motor performance in individuals with intellectual disabilities (ID) is quite challenging. The objective of this study was to compare the motor performances of individuals with ID and those with typical development (TD) during soccer dribbling through video-based behavior-coded movement assessment along with a wearable sensor. A cross-sectional research design was adopted. Adolescents with TD (N = 25) and ID (N = 29) participated in the straight-line and zigzag soccer dribbling tests. The dribbling performance was videotaped, and the footage was then analyzed with customized behavior-coding software. The coded parameters were the time for movement completion, the number of kicks, blocks, steps, the number of times the ball went out of bounds, the number of missed cones, and the trunk tilt angle. Participants with ID exhibited significantly poorer performance and demonstrated greater variances in many time and frequency domain parameters. It also revealed that participants with ID kicked with both feet while dribbling, whereas those with TD mainly used the dominant foot. The present findings demonstrated how the ID population differed from their peers in lower-extremity strategic control. The customized video-based behavior-coded approach provides an efficient and effective way to gather behavioral data and calculate performance parameter statistics in populations with intellectual disabilities.
Circulating Fibroblast Growth Factor-23 Levels Can Predict Rapid Kidney Function Decline in a Healthy Population: A Community-Based Study

Background: Fibroblast growth factor-23 (FGF-23) associates with decreased kidney function in patients with chronic kidney disease (CKD). However, the correlation between circulating FGF-23 levels and the rate of renal function decline in healthy individuals is largely unknown. We aimed to evaluate the predictive performance of FGF-23 for rapid kidney function decline (RKFD) in a community-based study. Methods: A total of 2963 people residing in northern Taiwan were enrolled from August 2013 to May 2018 for an annual assessment of kidney function for five years. The baseline estimated glomerular filtration rates (eGFR) were calculated using the 2009 and 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, which aggregates the values of serum creatinine and cystatin C (eGFRcr-cys). The outcome was RKFD—a 15% decrease in estimated glomerular filtration rate (eGFR) within the first four years, and a reduction in eGFR without improvement in the 5th year. A generalized additive model (GAM) was used to determine the cut-off value of FGF-23 to predict RKFD. Results: The incidence of RKFD was 18.0% (114/634). After matching for age and sex at a 1:1 ratio, a total of 220 subjects were analyzed. eGFRcr-cys was negatively correlated with total vitamin D level but seemed irrelevant to FGF-23. Multivariable logistic regression analysis showed that FGF-23, eGFRcr-cys, and urine albumin-to-creatinine ratio (UACR) were independent predictors of the possibility of RKFD. FGF-23 showed the best predictive performance for RKFD (AUROC: 0.803), followed by baseline eGFRcr-cys (AUROC: 0.639) and UACR (AUROC: 0.591). From the GAM, 32 pg/mL was the most appropriate cut-off value of FGF-23 with which to predict RKFD. The subgroup and sensitivity analyses showed consistent results that high-FGF-23 subjects had higher risks of RKFD. Conclusions: Circulating FGF-23 level could be a helpful predictor for RKFD in this community-based population.
Dual-Tree Complex Wavelet Input Transform for Cyst Segmentation in OCT Images Based on a Deep Learning Framework

Optical coherence tomography (OCT) represents a non-invasive, high-resolution cross-sectional imaging modality. Macular edema is the swelling of the macular region. Segmentation of fluid or cyst regions in OCT images is essential, to provide useful information for clinicians and prevent visual impairment. However, manual segmentation of fluid regions is a time-consuming and subjective procedure. Traditional and off-the-shelf deep learning methods fail to extract the exact location of the boundaries under complicated conditions, such as with high noise levels and blurred edges. Therefore, developing a tailored automatic image segmentation method that exhibits good numerical and visual performance is essential for clinical application. The dual-tree complex wavelet transform (DTCWT) can extract rich information from different orientations of image boundaries and extract details that improve OCT fluid semantic segmentation results in difficult conditions. This paper presents a comparative study of using DTCWT subbands in the segmentation of fluids. To the best of our knowledge, no previous studies have focused on the various combinations of wavelet transforms and the role of each subband in OCT cyst segmentation. In this paper, we propose a semantic segmentation composite architecture based on a novel U-net and information from DTCWT subbands. We compare different combination schemes, to take advantage of hidden information in the subbands, and demonstrate the performance of the methods under original and noise-added conditions. Dice score, Jaccard index, and qualitative results are used to assess the performance of the subbands. The combination of subbands yielded high Dice and Jaccard values, outperforming the other methods, especially in the presence of a high level of noise.
1 W at 490 nm on a compact micro-optical bench by single-pass second harmonic generation

We demonstrate a compact 1 W laser module at 490 nm using a Distributed Bragg Reflector tapered diode laser in single-path second harmonic generation (SHG) configuration. The frequency conversion is performed with a 3 cm periodically poled MgO:LiNbO3 crystal on a micro-optical bench having a footprint of 2.5 cm3. 1 W blue light could be achieved at a pump power of about 9.5 W resulting in an optical conversion efficiency of about 10 %. The output power stability is better than ± 2% and the blue laser beam shows an excellent beam quality of M2σ = 1.2 in vertical and M2σ = 2 in lateral direction, respectively.
Erectile Dysfunction: Pharmacological Pathways with Understudied Potentials

Erectile dysfunction (ED) is a public health concern worldwide. In the past, it was perceived as a phenomenon attributed to age advancement. However, more individuals are affected every year that do not fall under that age criterion. Epidemiological research revealed that this abnormality has an association with endothelial dysfunction connected to several cardiovascular (CV) risk factors. Currently, ED is interpreted as a clinical marker for future adverse events and not only as a present health issue that negatively affects the quality of life. The management of ED involves lifestyle modifications, therapeutic optimization for comorbid conditions, and pharmacological and psychosexual therapy. Phosphodiesterase type 5 (PDE5) inhibitors are the first-line pharmacological agents to be prescribed for such a condition. Nonetheless, other pharmacological pathways and agents remain underinvestigated or were investigated at some stage. This review aimed to present to future researchers interested in this field with some pharmacological agents that showed favorable effects on a limited number of studies on human subjects or experimental models.
Discrete source contributions to small-scale anisotropies of the microwave background

The temperature fluctuations of the microwave background due to unresolved, randomly distributed sources have been computed for a wide interval of angular scales (from a few arcseconds to several tens of degrees) and for wavelengths ranging from several centimeters to below 1 mm. For wavelenghts greater than 6 cm, the present estimates follow almost directly from the observed radio source counts. By properly taking into account all of the available data, the uncertainties in the extrapolations have been reduced to smaller scales and higher frequencies. Only within a relatively narrow frequency range (from about 20 to 200 GHz) the contribution of discrete sources to the temperature fluctuations on scales of less that 1 deg is expected to be Delta T/T less than 0.00001. The effect of clustering that might strongly enhance fluctuations at least on some angular scales is noted. The most sensitive data on Delta T/T at 6 cm provide significant constraints on the correlation function of radio sources. 62 refs.
Impact of COVID-19 Epidemic on Psycho-Oncological Distress in Neuro-Oncological Patients

Up to 40% of neuro-oncological patients already deal with high levels of distress under conventional circumstances. Due to COVID-19, pandemic hospital visitor rules have been restricted and patients did not receive the same level of supporting caregiver network as before COVID. The aim of the present study was to analyse the impact of the COVID pandemic on the prevalence of distress, anxiety and depression in neuro-oncological patients. Patients admitted for brain tumour surgery were screened regarding distress, anxiety and depression. Furthermore, aspects of patients’ quality of life and clinical data were covered. Retrospectively available data of patients treated pre-pandemic (group A) and throughout the COVID-19 pandemic (group B) were statistically analysed using Chi-square tests and independent-sample t-tests, and regression analysis was performed to support statistical findings. Data from 110 patients were available. In all, 48 patients were included pre-COVID-19 and 62 during the COVID-19 pandemic. The authors found no significant difference between pre-COVID-19 prevalence of distress (p = 0.112), anxiety (p = 0.385) or depression (p = 0.084). Regression analyses additionally did not show any significant influence of COVID-19 on the above analysed parameter. Analyses of our cohort’s data could not underline the negative impact of COVID-19 restrictions, shortcuts of professional and remodelled caregiver support on psycho-oncological outcomes.
Antimicrobial Prescribing before and after the Implementation of a Carbapenem-Focused Antimicrobial Stewardship Program in a Greek Tertiary Hospital during the COVID-19 Pandemic

Background: Irrational use of antimicrobials poses a significant risk for public health by aggravating antimicrobial resistance. The aim of this repeated point prevalence survey (PPS) was to evaluate the impact of a carbapenem-focused antimicrobial stewardship program (ASP) on overall antimicrobial use and quality of antimicrobial prescribing during the COVID-19 pandemic. Methods: All adult inpatients in the University Hospital of Heraklion in Greece were audited twice, before and after the implementation of the ASP, in October 2019 and October 2020, respectively. Patient characteristics, indications and diagnoses for antimicrobial administration, antimicrobials prescribed, and compliance with treatment guidelines were recorded. Results: Of 743 adult inpatients on the days of the two surveys, 398 (53.6%) were on antimicrobials for 437 diagnoses. Following implementation of the ASP, there was substantial decrease in the utilization of carbapenems (4.9% of all antibacterials prescribed in the second PPS compared to 10.3% in the first PPS). A significant improvement was observed for all indicators of the quality of antimicrobial prescribing. Conclusions: Our study demonstrated a positive impact of an ASP implementation during the first stages of the COVID-19 pandemic on reducing the use of last-line antimicrobials and improving overall quality of antimicrobial prescribing.
Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest

In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human–computer interaction), etc. Such emotions could be detected from various means, such as information integration from facial expressions, gestures, speech, etc. Though such physical depictions contribute to emotion detection, EEG (electroencephalogram) signals have gained significant focus in emotion detection due to their sensitivity to alterations in emotional states. Hence, such signals could explore significant emotional state features. However, manual detection from EEG signals is a time-consuming process. With the evolution of artificial intelligence, researchers have attempted to use different data mining algorithms for emotion detection from EEG signals. Nevertheless, they have shown ineffective accuracy. To resolve this, the present study proposes a DNA-RCNN (Deep Normalized Attention-based Residual Convolutional Neural Network) to extract the appropriate features based on the discriminative representation of features. The proposed NN also explores alluring features with the proposed attention modules leading to consistent performance. Finally, classification is performed by the proposed M-RF (modified-random forest) with an empirical loss function. In this process, the learning weights on the data subset alleviate loss amongst the predicted value and ground truth, which assists in precise classification. Performance and comparative analysis are considered to explore the better performance of the proposed system in detecting emotions from EEG signals that confirms its effectiveness.
Comparing water use patterns of sand‐binding species in an alpine semi‐arid desert

The processes of species obtaining water resources are crucial to evaluate the adaptability of plantations, which can affect the establishment and survival of restored ecosystems and functions of water‐limited ecosystems. However, there are still limitations in understanding water use patterns of different four plantations (Populus sylvestris, Salix cheliophe, Hippophae rhamnoides and Artemisia ordosica) in the Qinghai Lake basin. Stable isotopes of xylem water of four individual species were analysed at different dunes located at the eastern shore of Qinghai Lake in 2018 and 2019. The purpose of this study was to explore potential water sources used by different life form plants, to identify whether the soil moisture content and root distribution determined the plants water use patterns. Results from the Bayesian mixing model MixSIAR showed that all species mainly uses three levels of soil water, but they extracted soil water from different layers in different growing seasons among species. All species primarily depended on water from upper soil layers at the early growing stage, but P. sylvestris and S. cheliophe also absorbed much proportion middle and deep soil layers' water in dry year of 2019. All four species shift to use middle or deep soil layer water rather than shallow water in July with the most rainfall and soil water content (SWC). However, it was only used by P. sylvestris and H. rhamnoides in 2018. In all, seasonal water use pattern of different species was affected by SWC, soil texture and root distribution. Compared with moratorium plantations such as S. cheliophe, mixed afforestation of P. sylvestris and H. rhamnoides maximized of soil water sources absorbed by the plants. The study can shed light on plant–water relationships to facilitate the woody species for afforestation and desertification restoration management in the semi‐arid desert ecosystem.
Pontrjagin duality and digital signal processing

All of the Fourier transforms encountered in signal processing can be regarded as instances of a single universal object-the Fourier transform associated with an abstract abelian group. We show how this way of looking at things leads to a natural and simple account of the similarities between the properties of the Fourier transforms associated with different types of signal and how it sheds a new light on the duality between sampling and aliasing. >
An Introduction to Spinors and Geometry with Applications in Physics

There is now a greater range of mathematics used in theoretical physics than ever. The aim of this book is to introduce theoretical physicists, of graduate student level upwards, to the methods of differential geometry and Clifford algebras in classical field theory. Recent developments in particle physics have elevated the notion of spinor fields to considerable prominence, so that many new ideas require considerable knowledge of their properties and expertise in their manipulation. It is also widely appreciated now that differential geometry has an important role to play in unification schemes which include gravity. All the important prerequisite results of group theory, linear algebra, real and complex vector spaces are discussed. Spinors are approached from the viewpoint of Clifford algebras. This gives a systematic way of studying their properties in all dimensions and signatures. Importance is also placed on making contact with the traditional component oriented approach. The basic ideas of differential geometry are introduced emphasising tensor, rather than component, methods. Spinor fields are introduced naturally in the context of Clifford bundles. Spinor field equations on manifolds are introduced together with the global implications their solutions have on the underlying geometry. Many mathematical concepts are illustrated using field theoretical descriptions of the Maxwell, Dirac and Rarita-Schwinger equations, their symmetries and couplings to Einsteinian gravity. The core of the book contains material which is applicable to physics. After a discussion of the Newtonian dynamics of particles, the importance of Lorentzian geometry is motivated by Maxwell's theory of electromagnetism. A description of gravitation is motivated by Maxwell's theory of electromagnetism. A description of gravitation in terms of the curvature of a pseudo-Riemannian spacetime is used to incorporate gravitational interactions into the language of classical field theory. This book will be of great interest to postgraduate students in theoretical physics, and to mathematicians interested in applications of differential geometry in physics.