Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Nut Consumption on Blood Lipids and Lipoproteins: A Comprehensive Literature Update

In the present review, we provide a comprehensive narrative overview of the current knowledge on the effects of total and specific types of nut consumption (excluding nut oil) on blood lipids and lipoproteins. We identified a total of 19 systematic reviews and meta-analyses of randomized controlled trials (RCTs) that were available in PubMed from the inception date to November 2022. A consistent beneficial effect of most nuts, namely total nuts and tree nuts, including walnuts, almonds, cashews, peanuts, and pistachios, has been reported across meta-analyses in decreasing total cholesterol (mean difference, MD, −0.09 to −0.28 mmol/L), LDL-cholesterol (MD, −0.09 to −0.26 mmol/L), and triglycerides (MD, −0.05 to −0.17 mmol/L). However, no effects on HDL-cholesterol have been uncovered. Preliminary evidence indicates that adding nuts into the regular diet reduces blood levels of apolipoprotein B and improves HDL function. There is also evidence that nuts dose-dependently improve lipids and lipoproteins. Sex, age, or nut processing are not effect modifiers, while a lower BMI and higher baseline lipid concentrations enhance blood lipid/lipoprotein responses. While research is still emerging, the evidence thus far indicates that nut-enriched diets are associated with a reduced number of total LDL particles and small, dense LDL particles. In conclusion, evidence from clinical trials has shown that the consumption of total and specific nuts improves blood lipid profiles by multiple mechanisms. Future directions in this field should include more lipoprotein particle, apolipoprotein B, and HDL function studies.
Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.
The COVID-19 pandemic and the European Union: politics, policies and institutions

ABSTRACT The COVID-19 pandemic posed unprecedented challenges to the European Union (EU) and its member states. In the EU, health policy competence has been and remains largely with member states. However, faced with a major external crisis, which more or less affected all member states at the same time, the EU developed a framework within which the member states (and their subnational units) could respond together to the crisis. This introductory article to the Special Issue ‘The COVID-19 Pandemic and the European Union,’ briefly examines how EU institutions, policies and politics were affected by the crisis. Contrary to earlier crises, the EU responded speedily and effectively this time around. The EU has become increasingly important in crisis management, in part due to the nature of transboundary crises. The EU proved itself to be a good crisis manager on some dimensions, but certainly not on all. The crisis created momentum for collective action and for fast decision-making, even though the legitimacy of some these actions has been subject to limited public scrutiny.
Uniqueness and linear independence of steering vectors in array space

The study of array processing often involves the manipulation of steering vectors defined in array space. The question of uniqueness and linear independence of steering vectors often arises in the analysis of array processing algorithms and in their formulation. The paper examines the effect of array geometry on the mapping between directions in three‐dimensional space and steering vectors in array space. Conditions on array geometry for uniqueness and linear independence of steering vectors are established for linear, planar, and general arrays.
How a basement fault has controlled Pb- Zn ore deposition within the Zagros Fold-Thrust Belt? a focus on remote sensing approaches

ABSTRACT The Zagros fold and thrust belt as one of the structurally complex areas have been considered chiefly only for hydrocarbon reserves, despite having other mineral deposits like the Surmeh lead and Zinc mine. This study was conducted in the Fars region, one of the structural zones in the Zagros, to detect hydrothermal alteration minerals associated with Pb–Zn mineralization and fracture distribution using ASTER, Landsat 8, and Sentinel 2 satellite imagery. Using different algorithms and approaches, we demonstrate the interesting distribution of Pb-Zn-related mineral deposits controlled by structures on different scales. These methods included the spectral band ratios and spectral angle mapping for alteration detection and manual extraction of structural lineaments (i.e. fault and fractures) from ETM+. Also, an augmented circular scanline method, along with areal sampling, was used to prepare a fracture intensity map of the area. According to the results, the intersection of NW-SE and N-S trending fracture sets are promising areas for Pb-Zn-related mineral exploration. These fracture sets are likely associated with the main Karebas tear fault and its horsetail splay terminal. The findings of this study were validated by field observation of the Pb-Zn deposit and its related mineral associations in the area.
Multivariate logistic compounds

SUMMARY A general method for estimating the proportions of a finite compound distribution is suggested, based on direct estimation of the likelihood ratio. The only distributional assumption is that the log likelihood ratio is linear in the observations and hence the method is robust and applicable to a number of families of distributions. Sample points are required from each component distribution and from the compound. In particular, the method handles continuous and discrete data with equal facility. Only a moderate number of parameters need estimation so that problems in quite high dimensions can be solved. The method can be used to update logistic discriminant functions using data points whose parent populations are unknown.
Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

Segmentation is an essential process in image because of its wild application such as image analysis, medical image analysis, pattern reorganization, etc. Color and texture are most significant low-level features in an image. Normally, color-textured image segmentation consists of two steps: (i) extracting the feature and (ii) clustering the feature vector. This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix (ICICM). Then, Extended- Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image. Experimental results show that the proposed hybrid approach could obtain better cluster quality and segmentation results compared to state-of-art image segmentation algorithms.
Results to aid applications of ellipsoidal state bounds

Results to assist in the application of the ellipsoidal bounds provided by standard state-bounding algorithms are presented. They include derived bounds on scalar state-dependent quantities and the state values which determine them, tests of intersection and inclusion of ellipsoids, measures of how much an ellipsoid may be changed without altering its inclusion in another, and an ellipsoidal inner bound for the set reachable in the worst case from an ellipsoidal set by ellipsoidally bounded forcing, in a linear system. Approximations are suggested for the most computationally demanding result. Ways in which these results might be employed in aerospace interception problems are discussed to illustrate their utility.
Investigating attentional tunneling through a flexible experimentation environment and eye tracking

Although attentional tunneling as a phenomenon is at least known since the late 1970ies, it is still an area of high research interest, since it bears connections to current and future applications in head-up and head-down displays. For example, it is still not fully answered to what degree highly dynamic scenarios influence the pilot's ability to keep up with routine tasks, and vice versa, when and whether dynamic scene changes stay unnoticed under high workload. In order to further investigate attentional tunneling a generic experimentation environment was set up. The core of the environment is DLR's flexible sensor simulation suite (F3S). This simulation software can be installed on specialized simulation platforms, for example a Vision Station, as well as on standard workstations and can be tuned to a simple view simulation with different levels of realism. It allows for a full and dynamic control of experimental scenarios, for example possible changes in the environment. For larger scenarios several platforms can be coupled to enable the investigation of team situations. As one of its key features the set-up includes a full eye-tracking solution that is further capable of recording dynamic areas of interest. Within a first experiment with a student sample F3S was used as a simple view simulation combined with synthetic approach scenarios. Subjects were asked to detect changes whilst flying highway-in-the-sky approaches with a head-up display. At the same time eye gaze positions where tracked. This novel approach to the investigation of attentional tunneling can prove that an environmental change, even though visually perceived, is not necessarily cognitively processed at the same time.
Using Mask R-CNN for Underwater Fish Instance Segmentation as Novel Objects: A Proof of Concept

Instance Segmentation in general deals with detecting, segmenting and classifying individual instances of objects in an image. Underwater instance segmentation methods often involve aquatic animals like fish as the things to be detected. In order to train deep learning models for instance segmentation in an underwater environment, rigorous human annotation in form of instance segmentation masks with labels is usually required, since the aquatic environment poses challenges due to dynamic background patterns and optical distortions.However, annotating instance segmentation masks on images is especially time- and cost-intensive compared to classification tasks.Here we show an unsupervised instance learning and segmentation approach that introduces a novel class, e.g., ""fish"" to a pre-trained Mask R-CNN model using its own detection and segmentation capabilities in underwater images.Our results demonstrate a robust detection and segmentation of underwater fish in aquaculture without the need for human annotations.This proof of concept shows that there is room for novel objects within trained instance segmentation models in the paradigm of supervised learning.
Disease-modifying therapies for relapsing/active secondary progressive multiple sclerosis – a review of population-specific evidence from randomized clinical trials

Although the understanding of secondary progressive multiple sclerosis (SPMS) is evolving, early detection of relapse-independent progression remains difficult. This is further complicated by superimposed relapses and compensatory mechanisms that allow for silent progression. The term relapsing multiple sclerosis (RMS) subsumes relapsing-remitting multiple sclerosis (RRMS) and SPMS with relapses. The latter is termed ‘active’ SPMS, for which disease-modifying therapies (DMTs) approved for either RMS or active SPMS can be used. However, the level of evidence supporting efficacy and safety in SPMS differs between drugs approved for RMS and SPMS. Our review aims to identify current evidence from published clinical trials and European public assessment reports from the marketing authorization procedure on the efficacy, especially on progression, of DMTs approved for RMS and SPMS. To identify relevant evidence, a literature search has been conducted and European public assessment reports of DMTs approved for RMS have been screened for unpublished data specific to SPMS. Only two clinical trials demonstrated a significant reduction in disability progression in SPMS study populations: the EXPAND study for siponimod, which included a typical SPMS population, and the European study for interferon (IFN)-beta 1b s.c. , which included patients with very early and active SPMS. Both DMTs also achieved significant reductions in relapse rates. Ocrelizumab, cladribine, ofatumumab, and ponesimod are all approved for RMS – ocrelizumab, ofatumumab, and ponesimod based on an RMS study, cladribine based on an RRMS study. Data on efficacy in SPMS are only available from post hoc analyses of very small subgroups, representing only up to 15% of the total study population. For these DMTs, approval for RMS, including active SPMS, was mainly based on the assumption that the reduction in relapse rate observed in patients with RRMS can also be applied to SPMS. Based on that, the potential of these drugs to reduce relapse-independent progression remains unclear.
AN IMPROVED MARKOV CHAIN APPROXIMATION METHODOLOGY: DERIVATIVES PRICING AND MODEL CALIBRATION

This paper presents an improved continuous-time Markov chain approximation (MCA) methodology for pricing derivatives and for calibrating model parameters. We propose a generalized nonequidistant grid model for a general stochastic differential equation, and extend the proposed model to accommodate a jump component. Because the prices of derivatives generated by the MCA models are sensitive to the setting of the chain's state space, we suggest a heuristic determination of the grid spacing such that the Kolmogorov–Smirnov distance between the underlying distribution and the MCA distribution is minimized. The continuous time setting allows us to introduce semi-analytical formulas for pricing European and American style options. The numerical examples demonstrate that the proposed model with a nonequidistant grid setting provides superior results over the equidistant grid setting. Finally, we present the MCA maximum likelihood estimator for a jump-diffusion process. The encouraging results from the simulation and empirical studies provide insight into calibration problems in finance where the density function of a jump-diffusion model is unknown.
Posterior Circulation Stroke Secondary to Basilar Artery Thrombosis With a Fatal Outcome

Stroke is a regularly encountered emergency by emergency physicians, categorized based on the culprit artery and diagnosed based on non-contrast computerized tomography (CT) brain, which is supported by clinical examination that can be treated intravenously by thrombolytic agents or mechanical thrombectomy. Here we present one such case, which was brought to the emergency room with symptoms of posterior circulation stroke within 8 hours and underwent mechanical thrombectomy.
Mussel-Based Biomimetic Strategies in Musculoskeletal Disorder Treatment: From Synthesis Principles to Diverse Applications

Abstract Musculoskeletal disorders are the second leading cause of disability worldwide, posing a huge global burden to the public sanitation system. Currently, tissue engineering-based approaches act as effective strategies, which are, however, challenging in limited application scenarios. Mussel-based biomimetic materials, exhibit numerous unique properties such as intense adhesion, biocompatibility, moisture resistance, and injectability, to name only a few, and have attracted extensive research interest. In particular, featuring state-of-the-art properties, mussel-inspired biomaterials have been widely explored in innumerable musculoskeletal disorder treatments including osteochondral defects, osteosarcoma, osteoarthritis, ligament rupture, and osteoporosis. Nevertheless, a comprehensive and timely discussion of their applications in musculoskeletal disorders is insufficient. In this review, we emphasize on (1) the main categories and characteristics of mussel foot proteins and their fundamental mechanisms for the spectacular adhesion in mussels; (2) the diverse synthetic methods and modification of various polymers; and (3) the emerging applications of mussel-biomimetic materials, the future perspectives, and challenges, especially in the area of musculoskeletal disorder. We envision that this review will provide a unique and insightful perspective to improve the development of a new generation of mussel biomimetic strategies.
Deciphering Diets and Lifestyles of Prehistoric Humans through Paleoparasitology: A Review

Parasites have affected and coevolved with humans and animals throughout history. Evidence of ancient parasitic infections, particularly, reside in archeological remains originating from different sources dating to various periods of times. The study of ancient parasites preserved in archaeological remains is known as paleoparasitology, and it initially intended to interpret migration, evolution, and dispersion patterns of ancient parasites, along with their hosts. Recently, paleoparasitology has been used to better understand dietary habits and lifestyles of ancient human societies. Paleoparasitology is increasingly being recognized as an interdisciplinary field within paleopathology that integrates areas such as palynology, archaeobotany, and zooarchaeology. Paleoparasitology also incorporates techniques such as microscopy, immunoassays, PCR, targeted sequencing, and more recently, high-throughput sequencing or shotgun metagenomics to understand ancient parasitic infections and thus interpret migration and evolution patterns, as well as dietary habits and lifestyles. The present review covers the original theories developed in the field of paleoparasitology, as well as the biology of some parasites identified in pre-Columbian cultures. Conclusions, as well as assumptions made during the discovery of the parasites in ancient samples, and how their identification may aid in better understanding part of human history, ancient diet, and lifestyles are discussed.
Cultivation Using Coir Substrate and P or K Enriched Fertilizer Provides Higher Resistance to Drought in Ecologically Diverse Quercus Species

Nursery cultivation practices can be modified to increase resistance to water stress in forest seedlings following field establishment, which may be increasingly important under climate change. We evaluated the morphological (survival, growth) and physiological (chlorophyll fluorescence, leaf water potential) responses to water stress for three ecologically diverse Quercus species (Q. robur, Q. pubescens, and Q. ilex) with varying traits resulting from the combination of growing media (peat, coir) and fertilization (standard, P-enriched, K-enriched). For all species under water stress, seedlings grown in coir had generally higher growth than those grown in peat. Seedlings fertilized with P performed better, particularly for survival; conversely, K fertilization resulted in inconsistent findings. Such results could be explained by a combination of factors. P fertilization resulted in higher P accumulation in seedlings, while no K accumulation was observed in K fertilized seedlings. As expected, the more drought-sensitive species, Q. robur, showed the worst response, while Q. pubescens had a drought resistance equal or better to Q. ilex despite being classified as intermediate in drought resistance in Mediterranean environments.
Early neurological deterioration in patients with acute ischemic stroke: a prospective multicenter cohort study

Background: There is still no precise knowledge of the causes of progression in patients with acute ischemic stroke (AIS), and we are unable to predict patients at risk. Objective: To explore the frequency, predictive factors, and the prognosis of early neurological deterioration (END) in patients with AIS Methods: In this prospective multicenter observational study, we assessed patients with AIS admitted to 18 hospitals in Henan, China. We defined END as an increase of ⩾2 points in total National Institutes of Health Stroke Scale (NIHSS) score or ⩾1 point in the motor items of the NIHSS within 7 days after admission. Risk factors were analyzed using multivariate logistic regression models. Prognosis was evaluated using the modified Rankin Scale (mRS), with poor prognosis defined as mRS 3–6. Results: A total of 9114 patients with AIS within 24 h of symptom onset were enrolled in the study. END occurred in 1286 (14.1%) patients. The highest incidence (62.5%) of END occurred within 24 h after admission. After adjusting potential confounders, age, body mass index, waist–hip ratio, systolic blood pressure, baseline NIHSS, disabled at baseline, history of atrial fibrillation, diabetes mellitus, intracranial arterial stenosis, infarct location in the lenticulostriate artery area and cerebral watershed, neutrophils, lymphocytes, uric acid, and triglycerides were identified as independent predictors for END. END was significantly associated with poor prognosis at 90 days, and the adjusted OR was 1.74 (95% CI: 1.53–1.97). Conclusion: One in seven hospitalized patients with AIS may experience END within 24 h of onset. The highest incidence of END occurred within 24 h of admission and decreased steeply with time. Easily identifiable risk factors predict END and could help understand the causal mechanisms and thereby prevent END.
The Potential Impact of Artificial Intelligence on Healthcare Spending

The potential of artificial intelligence (AI) to simplify existing healthcare processes and create new, more efficient ones is a major topic of discussion in the industry. Yet healthcare lags other industries in AI adoption. In this paper, we estimate that wider adoption of AI could lead to savings of 5 to 10 percent in US healthcare spending—roughly $200 billion to $360 billion annually in 2019 dollars. These estimates are based on specific AI-enabled use cases that employ today’s technologies, are attainable within the next five years, and would not sacrifice quality or access. These opportunities could also lead to non-financial benefits such as improved healthcare quality, increased access, better patient experience, and greater clinician satisfaction. We further present case studies and discuss how to overcome the challenges to AI deployments. We conclude with a review of recent market trends that may shift the AI adoption trajectory toward a more rapid pace.