Miracle-dz/UniFAR-FTUs huggingface dataset:
Disorders of bilirubin and lipid metabolism

Background: Unconjugated hyperbilirubinemia, a feature of neonatal jaundice or Crigler-Najjar syndrome, can lead to neurotoxicity and even death. We previously demonstrated that unconjugated bilirubin (UCB) can be eliminated via transintestinal excretion in Gunn rats, a model of unconjugated hyperbilirubinemia, and that this is stimulated by enhancing fecal fatty acid excretion. Since transintestinal excretion also occurs for cholesterol (TICE), we hypothesized that increasing fecal cholesterol excretion and/or TICE could also enhance fecal UCB disposal and subsequently lower plasma UCB concentrations. Methods: To determine whether increasing fecal cholesterol excretion could ameliorate unconjugated hyperbilirubinemia, we treated hyperbilirubinemic Gunn rats with ezetimibe (EZE), an intestinal cholesterol absorption inhibitor, and/or a Liver X Receptor (LXR)- and Farnesoid X receptor (FXR)-agonist (T0901317 (T09) and obeticholic acid (OCA), respectively), known to stimulate TICE. Results: We found that EZE treatment alone or in combination with T09 or OCA increased fecal cholesterol disposal but did not lower plasma UCB levels. Conclusion: These findings do not support a link between the regulation of transintestinal excretion of cholesterol and bilirubin. Furthermore, induction of fecal cholesterol excretion is not a potential therapy for unconjugated hyperbilirubinemia.
Operating System Concepts

From the Publisher: This best-selling book, now in its fifth edition, provides a solid theoretical foundation for understanding operating systems. Authors Abraham Silberschatz and Peter Galvin discuss key concepts that are applicable to a variety of systems. They also present a large number of examples taken from common operating systems, including WindowsNT and Solaris 2. This book teaches general principles in operating systems while giving the teacher and students the flexibility to choose the implementation system.
Quantifying Glial-Glial Interactions In Drosophila Using Automated Image Analysis

Imaging is an immensely powerful tool in biomedical research. Technological advances in the last half century have led to the development of new tools for image analysis, with major strides being made in the last 20 years especially with machine and deep learning. However, researchers still often hit a bottleneck during the image analysis phase of their projects that often leads to delays and sometimes even limits the scope of their studies. In this thesis I demonstrate some of the issues that arise while quantifying images to answer a biological question by using a dataset of fly central nervous system images to elucidate interactions between different cells. I present an overview of the types of methods that can be used to perform this analysis including a discussion of their advantages and disadvantages. Finally, I present steps for creating and validating an automated image analysis pipeline that was used to analyze a large section of the fly ventral nerve cord, akin to the spinal cord. Automating image quantifying allowed us to maximize the size of the dataset analyzed, which revealed subtle patterns in cell-cell interactions that would not have been uncovered with manual quantification of a smaller dataset.
Evaluation of an On-Campus Program Bridging the Theory-Practice Gap in Occupational Therapy Education: Student Perspective

Abstract The transfer of learning from an academic setting to fieldwork and vice versa is not straightforward and occupational therapy students often report this as challenging. In acknowledgement of this, the fieldwork placement learning structure within the Bachelor and Master occupational therapy entry-level programs at a higher education institute was redesigned to enhance links between taught content at university and fieldwork placement. The aim of this study was to explore occupational therapy students’ perspectives of this new educational initiative. The students attended university for a set number of days over the course of their first and second fieldwork placements. The sessions were focused on assisting students to make connections between theory taught at university to the practice setting and allow time for reflection and consolidation of learning. One hundred and fifteen occupational therapy students completed an anonymized online questionnaire to evaluate their experiences consisting of open and closed questions. Qualitative data was analyzed using thematic analysis and Likert Scale data was analyzed descriptively. Three key themes were identified from the qualitative information: reflection, support, and understanding and knowledge. The students reported the sessions provided time for reflection, which enabled them to link theory to practice more clearly and peer support was highly regarded. The introduction of university days during fieldwork education was overall positively received by students. This educational approach helped students to better understand theory and promoted reflection on practice experiences. This may be a useful approach to bridge the gap between theory and practice.
University of Birmingham Robot task planning and explanation in open and uncertain worlds

A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The ﬁrst idea is to organize the robot’s knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge e ﬀ ects the robot’s actions can have: epistemic e ﬀ ects (I believe X because I saw it) and assumptions (I’ll assume X to be true). By combining the knowledge layers with the models of knowledge e ﬀ ects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in ﬁve di ﬀ erent experiments on object search, mapping, and room categorization.
Comparative study of dissolved fluorescent matter in four West-European estuaries

INTRODUCTION P. Berger a, R. W. P. M. Laane b, A. G. Ilahude •, M. Ewald a, P. Courtot ca Groupe d'Océanographie physicochimique, L.A. CNRS no 348, Laboratoire de Chimie-Physique A, Université de Bordeaux I, 33405 Talence Cedex, France. b Netherlands Institute for Sea Research, PO Box 59, 1790 AB Den Burg, Texel, the Netherlands. c Laboratoire d'Océanographie Chimique, Université de Bretagne Occidentale, 29283 Brest Cedex, France.
The Relationship Between Yoga Practice Dosage and Perceived Stress, Anxiety, and Self-reported Health Among Breast Cancer Survivors Who Practice Yoga

The aim of this anonymous online study was to explore the yoga practice of breast cancer survivors to determine if yoga dosage (frequency and duration of practice) was related to stress, anxiety, and self-reported health in female survivors. Participants were recruited from online breast cancer support groups during a 3-month period (June-September 2019). Demographic information, stage and treatment of breast cancer, and frequency of yoga participation, including a home yoga practice were reported. Measures include the Perceived Stress Scale, State-Trait Anxiety Inventory, and self-reported health. Cumulative yoga dosage was calculated. Thirty-five women participated (mean age = 55 years) and were divided into low and high yoga dosage groups based on the sample distribution. Low dosage of yoga was operationally defined as a cumulative dosage that fell within the lowest quartile (≤25%). Findings indicated no difference between the low-dosage group compared with the high-dosage group for perceived stress and self-reported health; but the high dosage yoga group had lower state anxiety scores (P < .05). All participants reported good self-reported health. These findings contribute to our knowledge of the psychosocial aspects of breast cancer survivorship and raise questions for further research quantifying the therapeutic dosing of yoga practice.
Association of Alcohol Consumption With CD4 Recovery After Antiretroviral Therapy Initiation in St. Petersburg, Russia

Supplemental Digital Content is Available in the Text. Background: Delayed CD4 recovery after initiating antiretroviral therapy (ART) is a novel potential mechanism by which alcohol consumption leads to increased morbidity and mortality in people with HIV. We hypothesized that alcohol consumption at ART initiation is associated with slower CD4 recovery. Methods: We retrospectively analyzed 2 pooled longitudinal alcohol/HIV cohorts (2014–2019) in St. Petersburg, Russia. Eligible participants initiated the first ART during parent studies; had alcohol consumption assessed by the blood biomarker, phosphatidylethanol (PEth), at the last research visit before ART initiation; and had ≥1 CD4 count measurement before and after initiating ART. Participants were stratified by low, moderate, and high PEth (<8, 8–80, and >80 ng/mL, respectively). We used random-effects piecewise linear regression models to estimate CD4 recovery, defined as CD4 count change per 30 days after ART initiation, by the alcohol group. Results: Of 60 eligible participants, median age was 34 years and 28% were female. The median pre-ART PEth in the low, moderate, and high PEth groups were <8, 23, and 232 ng/mL, respectively. After starting ART, the CD4 count increased by 13.60 cells/mm3/mo (95% CI: 0.33 to 26.87) with low PEth, 0.93 cells/mm3/mo (95% CI: −6.18 to 8.04) with moderate PEth, and 2.33 cells/mm3/mo (95% CI: −3.44 to 8.09) with high PEth. Conclusions: Among Russians with HIV, we observed faster CD4 recovery after ART initiation in those with low alcohol consumption compared with those with moderate and high alcohol consumption, as assessed by PEth. This analysis provides further evidence for the possible value of alcohol reduction interventions for people with HIV who are initiating ART.
Study on GNSS Constellation Combination to Improve the Current and Future Multi-GNSS Navigation Performance

In the case of satellite navigation positioning, the shielding of satellite signals is determined by the environment of the region at which a user is located, and the navigation performance is determined accordingly. The accuracy of user position determination varies depending on the dilution of precision (DOP) which is a measuring index for the geometric characteristics of visible satellites; and if the minimum visible satellites are not secured, position determination is impossible. Currently, the GLObal NAvigation Satellite system (GLONASS) of Russia is used to supplement the navigation performance of the Global Positioning System (GPS) in regions where GPS cannot be used. In addition, the European Satellite Navigation System (Galileo) of the European Union, the Chinese Satellite Navigation System (BeiDou) of China, the Quasi-Zenith Satellite System (QZSS) of Japan, and the Indian Regional Navigation Satellite System (IRNSS) of India are aimed to achieve the full operational capability (FOC) operation of the navigation system. Thus, the number of satellites available for navigation would rapidly increase, particularly in the Asian region; and when integrated navigation is performed, the improvement of navigation performance is expected to be much larger than that in other regions. To secure a stable and prompt position solution, GPSGLONASS integrated navigation is generally performed at present. However, as available satellite navigation systems have been diversified, finding the minimum satellite constellation combination to obtain the best navigation performance has recently become an issue. For this purpose, it is necessary to examine and predict the navigation performance that could be obtained by the addition of the third satellite navigation system in addition to GPS-GLONASS. In this study, the current status of the integrated navigation performance for various satellite constellation combinations was analyzed based on 2014, and the navigation performance in 2020 was predicted based on the FOC plan of the satellite navigation system for each country. For this prediction, the orbital elements and nominal almanac data of satellite navigation systems that can be observed in the Korean Peninsula were organized, and the minimum elevation angle expecting signal shielding was established based on Matlab and the performance was predicted in terms of DOP. In the case of integrated navigation, a time offset determination algorithm needs to be considered in order to estimate the clock error between navigation systems, and it was analyzed using two kinds of methods: a satellite navigation message based estimation method and a receiver based method where a user directly performs estimation. This simulation is expected to be used as an index for the establishment of the minimum satellite constellation for obtaining the best navigation performance.
MEASURING THE TECHNICAL PERFORMANCE OF A SECURITY OPERATIONS CENTER

Forsberg, Joonas Measuring the Technical Performance of a Security Operations Center Jyväskylä: University of Jyväskylä, 2022, 81 pp. Cyber Security, Master’s Thesis Supervisor(s): Frantti, Tapio This research examines the current state of the performance indicators and other metrics used to measure the technical performance of a Security Operations Center (SOC), as based on empirical experience, the current methods for measuring the technical performance of different types of SOCs are inadequate. Without properly constructed performance indicators or metrics, it is difficult to evaluate the actual performance of a SOC, which makes it difficult to assess the concrete impact a SOC has in terms of overall cyber defence capabilities. Design Science methodology is used as the research methodology in this research. The outcome of the research is a design science artifact, a novelty metric selection framework, that can be used to construct metrics to measure the technical and non-technical performance of a SOC. The design science artifact was successfully demonstrated by constructing five metrics that can be, as such, adopted by different types of SOCs to improve the technical performance measurement capabilities of their threat detection capabilities. The original hypothesis is supported by the literature reviewed within the research, as the commonly mentioned metrics revolved mostly around operational activities. Furthermore, the research concluded that the current methodologies to construct metrics and the commonly deployed metrics are inadequate to measure the technical performance of a SOC. The literature outlined a limited amount of technical performance metrics, but the ones evaluated, were considered to be invalid according to the metric selection framework. The design science artifact and the metrics utilized to demonstrate the metric provide means for SOCs to construct metrics and measure their technical performance, but further research around the subject is required to enable comprehensive industry-standard measurement capabilities to emerge.
Balancing User Privacy and Personalization∗

Privacy restrictions imposed by browsers such as Safari and Chrome limit the quality of individual-level data used in personalization algorithms. This paper investigates the consequences of these privacy restrictions on consumer, seller and platform outcomes using data from Wayfair, a large US-based online retailer. Large-scale randomized experiments indicate that personalization increases seller and platform revenue and leads to better consumer-product matches with 10% lower post-purchase product returns and 2.3% higher repeat purchase probability. Privacy restrictions can distort these benefits because they limit platforms’ ability to personalize. To evaluate privacy restrictions of interest, we (i) re-train the platform’s personalization algorithm with lower-quality data and generate counterfactual recommendations, and (ii) next, we simulate consumers’ search and purchase behavior under counterfactual recommendations using structural modeling. We find that two main policies imposed by Safari and Chrome disproportionately hurt price responsive consumers and small/niche product sellers. To address this, we propose and evaluate a probabilistic recognition algorithm that associates devices with user accounts without using exact user identity. Our findings demonstrate that this approach mitigates welfare and revenue losses significantly, striking a balance between privacy and personalization.
State-Dependent Pricing Turns Money into a Two-Edged Sword

Strong evidence exists that price/wage durations are dependent on the state of the economy, especially inflation. We embed this dependence in a macro model of the US that otherwise does well in matching the economy's behaviour in the last three decades; it now also matches it over the whole post-war period. This finding implies a major new role for monetary policy: besides controlling inflation it now determines the economy's price stickiness. We find that, when backed by fiscal policy in preventing a ZLB, by targeting nominal GDP monetary policy can achieve high price stability and avoid large cyclical output fluctuations.
Ontology-Mediated Query Answering over Log-Linear Probabilistic Data (Abstract)

Advances in automated knowledge base construction have led to successful systems, such as DeepDive [17], NELL [13], and Google’s Knowledge Vault [7]. They extract structured knowledge from multiple sources, through a chain of statistical techniques, and produce probabilistic knowledge bases (PKBs). The basic data model underlying these systems is given by probabilistic databases (PDBs) [18]; see recent surveys focusing on PKBs [2, 4]. PKBs are inherently incomplete, which makes reasoning more challenging. A common way to deal with incompleteness is to add commonsense knowledge, in the form of logical theories, to allow for deductions that go beyond existing facts in the knowledge base. Statistical relational models are concise, and lifted representations of probabilistic graphical models [9]. Well-known examples include Markov logic networks (MLNs) [15], relational Bayesian networks [10], and approaches to probabilistic logic programming (PLP). All these models can encode commonsense knowledge, but they are based on the closed-domain assumption (CDA) that requires the set of relevant objects to be finite, and known at design-time, which is not always an easy condition to be met. And contrary to the intuition, the CDA does not necessarily imply efficiency in comparison to open-domain models.
The application of ensemble techniques for land-cover classification in arid lands

Robust classification approaches are required for accurate classification of complex land-use/land-cover categories of desert landscapes using remotely sensed data. Machine-learning ensemble classifiers have proved to be powerful for the classification of remotely sensed data. However, they have not been evaluated for classifying land-cover categories in desert regions. In this study, the performance of two machine-learning ensemble classifiers – random forests (RF) and boosted artificial neural networks – is explored in the context of classification of land use/land cover of desert landscapes. The evaluation is based on the accuracy of classification of remotely sensed data, with and without integration of ancillary data. Landsat-5 Thematic Mapper data captured for a desert landscape in the north-western coastal desert of Egypt are used with ancillary variables derived from a digital terrain model to classify 13 different land-use/land-cover categories. Results show that the two ensemble methods produce accurate land-cover classifications, with and without integrating spectral data with ancillary data. In general, the overall accuracy exceeded 85% and the kappa coefficient (κ) attained values over 0.83. The integration of ancillary data improved the performance of the boosted artificial neural networks by approximately 5% and the random forests by 9%. The latter showed overall higher accuracy; however, boosted artificial neural networks showed better generalization ability and lower overfitting tendencies. The results reveal the merit of applying ensemble methods to integrated spectral and ancillary data of similar desert landscapes for achieving high classification accuracies.
Application of Different Reclamation Methods on Salt Affected Soils for Crop Production

Effects of salic conditions on soil properties under a given land use type and the methods for reclamation has not received the desired research attention in Nigeria. Understanding of how soil properties and crop yield respond to the influence of salic conditions is needed for employment of location-specific management strategies for the economic agricultural production. This study was conducted on salt affected soils during 2011/2012 and 2012/2013 crop years to investigate the soil physico-chemical properties and their effects on two maize cultivar growth and yield. Three approaches were employed to reclaim the salt affected soils in order to increase their efficiency and reduce the time of reclamation. Soils were sampled at two depth intervals: surface (0-15 cm depth) and subsurface (15-30 cm) for physical and chemical analysis. The experimental design was randomized complete block design (RCBD). The treatments were arranged in RCBD and replicated thrice. The treatment applications were 100% gypsum (CaS04) Gypsum requirement (GR); 25 Mg ha -1 gypsum + farm yard manure (FYM) and chiseling. Leaching with irrigation water was done over a period of 2-4 hours per week. During the two cropping seasons, rice and millet crops were grown. Original Research Article Ezeaku et al.; AJEA, 9(2): 1-11, 2015; Article no.AJEA. 17187 2 Data collected were analysed statistically following ANOVA technique and treatment differences were evaluated using LSD test. Pre-cultivation soil analysis revealed a mean soil bulk density value of 2.37 g cm -3 and a value of 17.46 for total porosity. Soil pH showed alkalinity (>pH 7.5) with high values of exchangeable bases and base saturation. Post-cultivation results show that highest yields of rice and millet were obtained from application of 100% gypsum. Yields obtained from gypsum + FYM treatments were, however, statistically similar. Yields from control treatment were consistently low. First millet post-harvest (2011/2012 crop year) soil test showed a reduction in electrical conductivity (ECe) value in all treatment plots except in control. The values of soil pH and sodium adsorption ratio (SAR) reduced after second millet harvest in 2012/2013 cropping season. The study found that soil chemical properties in control treatment did not improve, while combined use of gypsum + FYM + Chiseling appeared most effective in improving the soil conditions for land use sustainability.
Ethical Motives for Strategic Voting

We propose an alternative approach to modeling strategic voting in large elections. In particular, we examine strategic voting in the presence of ethical voters, who follow a group-welfare maximizing voting rule. This rule may require them to vote strategically, that is, to vote for a candidate that is not their most preferred. The model delivers comparative statics that link the degree of strategic voting with various electoral parameters. We show that strategic voting is non-monotonic in the popularity of the Condorcet loser. This observation reconciles the apparently contradicting empirical evidence and is robust to more general specifications of the model. for helpful advice and comments. We would also like to thank Samir Mamadehussene and Kym Pram for useful insights.