Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging Roles of Liver Sinusoidal Endothelial Cells in Nonalcoholic Steatohepatitis

Simple Summary Nonalcoholic fatty liver disease (NAFLD) is a hepatic manifestation of the metabolic syndrome. With the prevalence of obesity and type 2 diabetes, NAFLD is becoming the most common liver disorder worldwide. More than 10% of NAFLD patients progress to an inflammatory and fibrotic form called nonalcoholic steatohepatitis (NASH), which can lead to end-stage liver disease. Liver sinusoidal endothelial cells (LSEC) are highly specialized cells located at the interface between the flowing blood in the liver and the other liver cells. The current review highlights the recent knowledge of the role of LSEC in the development of NASH, and how LSEC change their structure and function during NAFLD progression. Moreover, the review discusses the pathogenic role of nanometer-sized particles called extracellular vesicles that mediate intercellular communication in the NASH liver. The current manuscript has a special emphasis on the role of adhesion molecules expressed on the LSEC surface in the recruitment of circulating leukocytes to the liver, a critical step in liver inflammation in NASH. Furthermore, the review shed some lights on LSEC-targeted potential therapeutic strategies in NASH. Abstract Nonalcoholic steatohepatitis (NASH) has become a growing public health problem worldwide, yet its pathophysiology remains unclear. Liver sinusoidal endothelial cells (LSEC) have unique morphology and function, and play a critical role in liver homeostasis. Emerging literature implicates LSEC in many pathological processes in the liver, including metabolic dysregulation, inflammation, angiogenesis, and carcinogenesis. In this review, we highlight the current knowledge of the role of LSEC in each of the progressive phases of NASH pathophysiology (steatosis, inflammation, fibrosis, and the development of hepatocellular carcinoma). We discuss processes that have important roles in NASH progression including the detrimental transformation of LSEC called “capillarization”, production of inflammatory and profibrogenic mediators by LSEC as well as LSEC-mediated angiogenesis. The current review has a special emphasis on LSEC adhesion molecules, and their key role in the inflammatory response in NASH. Moreover, we discuss the pathogenic role of extracellular vesicles and their bioactive cargos in liver intercellular communication, inflammation, and fibrosis. Finally, we highlight LSEC-adhesion molecules and derived bioactive product as potential therapeutic targets for human NASH.
Using Quality Improvement to Increase Access to Palliative Care.

PURPOSE As part of a larger effort to integrate palliative care into a cancer center, we identified barriers to palliative care referral for patients with breast or gynecologic cancer and developed a pilot program to improve access to palliative care services. METHODS We developed a multidisciplinary steering committee to uncover barriers to palliative care referral and developed a pilot program, called the Warm Handoff. Through ongoing collaboration and midpilot feedback sessions, we identified several additional barriers and opportunities to increase access to palliative care. RESULTS Clinicians used the initial Warm Handoff process only 20 times over a period of 7 months. Of those calls, 10 were for issues outside of those that the Warm Handoff pilot was intended to address. During the pilot, we identified lack of access to urgent visits and clinician telephone availability for clinical case discussion as additional barriers to access. Increased collaboration led to the creation of a clinical provider of the day (CPOD) care model, which allowed for a notable increase in the capacity to see urgent consults. After this intervention, we observed an average of 19 patients seen urgently per month. In addition, there was a trend toward increasing referrals from breast oncology after the initiation of the CPOD. CONCLUSION A CPOD model, developed via close oncology/palliative care collaboration, resulted in increased utilization of palliative care services.
A consideration of factors affecting palliative oral appliance effectiveness for obstructive sleep apnea: a scoping review.

STUDY OBJECTIVES This scoping review allows physicians, researchers and others interested in obstructive sleep apnea (OSA) to consider effectiveness of oral appliances (OAs). The intent is to improve understanding of OA effectiveness by considering morphologic interaction in patients with OSA. METHODS Morphologic and biomechanical criteria for positional alterations of the mandible assessed success rates of OA appliances. Searches of databases (Medline, PubMed, The Cochrane Library, EBSCO) using terms: OA treatment effectiveness, positive and/or negative outcome predictors. Craniofacial predictors of OAs and OSA biomechanical factors of anatomical traits associated with OA effectiveness were included. Databases searched radiographic cephalometric imaging for morphology/phenotypes and apnea-hypopnea index (AHI) responses. Articles were excluded if title or abstract was not relevant or a case report. If the analysis did not report mean or standard deviation for AHI, it was excluded. No language, age or sex restrictions were applied. RESULTS Analysis of 140 articles included in searched literature indicated alterations in musculature and pharyngeal airway structure through OA use. These alterations were individually unpredictable with wide variability 61.81% ± 12.29 (AHI mean ± standard deviation). Morphologic variations as predictors were typically weak and idiosyncratic. Biomechanical factors and wide variations in the metrics of appliance application were unclear, identifying gaps in knowledge and practice of OAs. CONCLUSIONS An integrated basis to identify morphologic and biomechanical elements of phenotypic expressions of sleep disordered breathing in the design and application of OAs is needed. Current knowledge is heterogeneous and shows high variability. Identification of subgroups of OSA patients responding to OAs is needed.
Identification of the sleep–wake states in rats using the high‐frequency activity of the electroencephalogram

The electroencephalographic signal constitutes the main sign classically used for the identification of states of alertness. However, activities in the high frequency (>100 Hz) range have not been properly studied despite their high potential for sleep scoring in rodents. In the present study, we designed a method for the identification of the sleep–wake states in rats by exclusively using high‐frequency activities of the electroencephalogram. By calculating the ratio between the amplitude of the electroencephalographic signal from 110 to 200 Hz and from 110 to 300 Hz, we obtained an index that had values that were low during wakefulness, intermediate during non‐REM sleep and high during REM sleep. This high‐frequency index (HiFI) allowed the identification of each state without the need to study other signs such as muscle activity or eye movements. To evaluate the performance of the index, we compared it with the conventional scoring of the sleep–wake cycle based upon the study of the electromyogram and delta (0.5–4 Hz), theta (6–9 Hz) and sigma (10–14 Hz) bands of the electroencephalogram. The index had an accuracy of 90.43 ± 1.91% (Cohen's kappa value of 0.82), confirming that the study of the high‐frequency activities of the electroencephalogram was sufficient to reliably identify alertness states in the rat. Compared to other sleep‐scoring methods, the HiFI has several advantages. It only requires one electroencephalography electrode, thus reducing the severity of the surgical preparation of the experimental animal, and its calculation is very simple, so it can be easily implemented online to classify sleep–wake states in real time.
Effects of a Personalized Fitness Recommender System Using Gamification and Continuous Player Modeling: System Design and Long-Term Validation Study

Background Gamification and persuasive games are effective tools to motivate behavior change, particularly to promote daily physical activities. On the one hand, studies have suggested that a one-size-fits-all approach does not work well for persuasive game design. On the other hand, player modeling and recommender systems are increasingly used for personalizing content. However, there are few existing studies on how to build comprehensive player models for personalizing gamified systems, recommending daily physical activities, or the long-term effectiveness of such gamified exercise-promoting systems. Objective This paper aims to introduce a gamified, 24/7 fitness assistant system that provides personalized recommendations and generates gamified content targeted at individual users to bridge the aforementioned gaps. This research aims to investigate how to design gamified physical activity interventions to achieve long-term engagement. Methods We proposed a comprehensive model for gamified fitness recommender systems that uses detailed and dynamic player modeling and wearable-based tracking to provide personalized game features and activity recommendations. Data were collected from 40 participants (23 men and 17 women) who participated in a long-term investigation on the effectiveness of our recommender system that gradually establishes and updates an individual player model (for each unique user) over a period of 60 days. Results Our results showed the feasibility and effectiveness of the proposed system, particularly for generating personalized exercise recommendations using player modeling. There was a statistically significant difference among the 3 groups (full, personalized, and gamified) for overall motivation (F3,36=22.49; P<.001), satisfaction (F3,36=22.12; P<.001), and preference (F3,36=15.0; P<.001), suggesting that both gamification and personalization have positive effects on the levels of motivation, satisfaction, and preference. Furthermore, qualitative results revealed that a customized storyline was the most requested feature, followed by a multiplayer mode, more quality recommendations, a feature for setting and tracking fitness goals, and more location-based features. Conclusions On the basis of these results and drawing from the gamer modeling literature, we conclude that personalizing recommendations using player modeling and gamification can improve participants’ engagement and motivation toward fitness activities over time.
A Temporal Threshold for Distinguishing Off-Wrist from Inactivity Periods: A Retrospective Actigraphy Analysis

(1) Background. To facilitate accurate actigraphy data analysis, inactive periods have to be distinguished from periods during which the device is not being worn. The current analysis investigates the degree to which off-wrist and inactive periods can be automatically identified. (2) Methods. In total, 125 actigraphy records were manually scored for ‘off-wrist’ and ‘inactivity’ (99 collected with the Motionlogger AMI, 26 (sampling frequency of 60 (n = 20) and 120 (n = 6) s) with the Philips Actiwatch 2. ) Data were plotted with cumulative frequency percentage and analyzed with receiver operating characteristic curves. To confirm findings, the thresholds determined in a subset of the Motionlogger dataset (n = 74) were tested in the remaining dataset (n = 25). (3) Results. Inactivity data lasted shorter than off-wrist periods, with 95% of inactive events being shorter than 11 min (Motionlogger), 20 min (Actiwatch 2; 60 s epochs) or 30 min (Actiwatch 2; 120 s epochs), correctly identifying 35, 92 or 66% of the off-wrist periods. The optimal accurate detection of both inactive and off-wrist periods for the Motionlogger was 3 min (Youden’s Index (J) = 0.37), while it was 18 (J = 0.89) and 16 min (J = 0.81) for the Actiwatch 2 (60 and 120 s epochs, respectively). The thresholds as determined in the subset of the Motionlogger dataset showed similar results in the remaining dataset. (4) Conclusion. Off-wrist periods can be automatically identified from inactivity data based on a temporal threshold. Depending on the goal of the analysis, a threshold can be chosen to favor inactivity data’s inclusion or accurate off-wrist detection.
PERCEIVED ACCOUNTABILITY AND CONFLICT MANAGEMENT STYLES AS PREDICTORS OF JOB PERFORMANCE OF PUBLIC OFFICIALS IN PAKISTAN

The paradigm of quantifying and managing performance is a critical component of public service organizations at the federal and provincial level in Pakistan. In this context, the current study examines the effects of perceived accountability and conflict handling techniques on job performance of public officials in Pakistan. Study data was gathered from the four Provincial Capitals including Lahore, Karachi, Quetta and Peshawar and the Federal Capital i.e. Islamabad. Correlational analysis determined the relationships of the independent variables with the dependent variable. Study results confirmed that perceived accountability (r=.512, p<.01) has a significant positive relationship with job performance. Integrating style (r=.664, p<.01), compromising style (r=.508, p<.01) and obliging style (r=.501, p<.01) of conflict management were also found to have a positively significant relationship with job performance. Additionally, dominating style (r=-.372, p<.01) and avoiding style (r=-.297, p<.01) showed negative association to job performance. The overall study outcomes revealed that perceived accountability and conflict management styles could affect the behaviors in job performances of public officials in government organizations.
Byzantine Reinforcement Learning

The byzantine agreement problem has been extensively studied in the literature since the 1980s, and gained even more attention with the emergence of blockchain structures. Of particular interest is the byzantine agreement in the asynchronous fully-connected network, without cryptographic assumptions. The proposed algorithm for solving this problem by Ben-Or [1] in 1983 has expected exponential communication time, and this result has not yet been significantly improved. On the other hand, reinforcement learning has proven very successful in scenarios where agents have to learn complex strategies through self-play [2], [3]. In this project, we present the novel approach of combining these two exciting fields – we are using reinforcement learning to solve the asynchronous byzantine agreement problem through self-play. In our setup, byzantine adversary has access to all the messages in transit, and can hide the values of some nodes from some of the correct nodes, while revealing them to the other correct nodes. The goal of the correct nodes is to reach agreement in the smallest number of rounds, and the goal of the byzantine adversary is to disturb this process. We have modelled this setup as a zero-sum game, and created an environment in which the correct and byzantine agents interact and receive rewards. We have then employed reinforcement learning algorithms and analysed the learned strategies. Finally, we argue that reinforcement learning can be successfully used in the asynchronous byzantine agreement problem and might lead to a better algorithm in the future.
LIFE CYCLE OF FLOATING DEBRIS IN THE CANALS OF HO CHI MINH CITY

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. LIFE CYCLE OF FLOATING DEBRIS IN THE CANALS OF HO CHI MINH CITY Thuy Chung Kieu-Le, Emilie Strady, Morganne Perset
Practical Sampling Techniques for INFRARED ANALYSIS

Theoretical Considerations (P.B. Coleman). Sample Handling in Infrared Spectroscopy-An Overview (W.D. Perkins). Optimization of Data by Internal Reflectance Spectroscopy (S.V. Compton and D.A.C. Compton). Diffuse Reflectance Infrared Spectroscopy: Sampling Techniques for Qualitative/Quantitative Analysis of Solids (S. Culler). A Practical Guide to FT-IR Photoacoustic Spectroscopy (J.F. McClelland, R.W. Jones, S. Luo, and L.M. Seaverson). The Versatile Sampling Methods of Infrared Microspectroscopy (P.L. Lang and L.J. Richwine). General Methods of Sample Preparation for the Infrared Hyphenated Techniques (G.L. McClure). Quantitative Analysis-Avoiding Common Pitfalls (S.V. Compton and D.A.C. Compton). Errors in Spectral Interpretation Caused by Sample-Preparation Artifacts and FT-IR Hardware Problems (R.C. Williams). Sampling Techniques for Vibrational Analysis in the Environmental and Forensic Laboratories (K.S. Kalasinsky and V.F. Kalasinsky). Infrared Sampling Techniques Training (T.J. Porro and S.C. Pattacini).
Defect Prediction Guided Search-Based Software Testing

Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited testing budget. In this paper, we propose an automated test generation technique that is also guided by the estimated degree of defectiveness of the source code. Parts of the code that are likely to be more defective receive more testing budget than the less defective parts. To measure the degree of defectiveness, we leverage Schwa, a notable defect prediction technique.We implement our approach into EvoSuite, a state of the art SBST tool for Java. Our experiments on the Defects4J benchmark demonstrate the improved efficiency of defect prediction guided test generation and confirm our hypothesis that spending more time budget on likely defective parts increases the number of bugs found in the same time budget.
Einfluss der extrazellulären Superoxiddismutase und des Kalziumsensitizers Levosimendan in einem Rattenmodell der extrakorporalen Zirkulation

Background. Extracorporeal circulation (ECC) causes ischemia and reperfusion injury (IRI). Superoxide, which can be detoxified by the enzyme class of superoxide dismutases, is known as a major mediator of IRI. The extracellular superoxide dismutase (SOD3) is the dominant isoform in the cardiovascular system. The calcium sensitizer levosimendan is part of the heart failure treatment; however, its effectiveness is still uncertain. Objective. This study aimed to identify 1) the role of SOD3 and 2) the therapeutic effects of levosimendan in the context of IRI associated with ECC. Material andmethods. Rats with an impaired SOD3 enzyme activity (SOD3-E124D; n= 9) were compared with SOD3-competent controls in a rodent model of ECC with deep hypothermic cardiac arrest. Subsequently, in the same model healthy rats were either treated with levosimendan (12μg/kg BW; n= 4) or a placebo (vehicle solution, n= 5). Activation of cardioprotective signaling cascades, apoptosis rates and expression of various SOD isoforms were analyzed. Results.While levosimendan was able to intermittently improve the hemodynamics, a clinical deterioration of the experimental animals was shown by reduction of the SOD3 activity. The reduction of SOD3 activity induced a decreased activation of cardiac salvage pathways as well as an increase in apoptosis. Levosimendan was able to reduce the apoptosis and activate a protective signaling pathway. Conclusion. Both SOD3 and levosimendan could achieve protective effects against IRI in a small animal model. They represent a potential target for future pharmaceutical intervention strategies for prevention of IRI by ECC.
The Application of the Plate Waste on Menu Analysis**

Menu analysis Menu engineering Plate waste Food consumption Disconfirmation theory Abstract In the study, it is aimed to determine the amount of plate waste and to use it with menu engineering (ME) method which is one of the most prevalent menu analysis approach in terms of literature and practices. A case study was conducted in a fine-dining restaurant in Antalya. In the study, 10 food items which are in the main course category on the dinner menu were taken into consideration. Material cost, sales amount and price data were collected through the document review while plate waste data was collected by using a digital camera. At the end of the research, it has been revealed that the amount of plate waste can be used effectively in the menu analysis. Thus, the customer’s point of view was included in the menu evaluation process which mostly reflects the business perspective. Therefore, more detailed performance data has been provided regarding menu items. In terms of restaurant managers, this means that the results of analysis can serve for both short-and long-term business purposes.
MockSniffer: Characterizing and Recommending Mocking Decisions for Unit Tests

In unit testing, mocking is popularly used to ease test effort, reduce test flakiness, and increase test coverage by replacing the actual dependencies with simple implementations. However, there are no clear criteria to determine which dependencies in a unit test should be mocked. Inappropriate mocking can have undesirable consequences: under-mocking could result in the inability to isolate the class under test (CUT) from its dependencies while over-mocking increases the developers' burden on maintaining the mocked objects and may lead to spurious test failures. According to existing work, various factors can determine whether a dependency should be mocked. As a result, mocking decisions are often difficult to make in practice. Studies on the evolution of mocked objects also showed that developers tend to change their mocking decisions: 17% of the studied mocked objects were introduced sometime after the test scripts were created and another 13% of the originally mocked objects eventually became unmocked. In this work, we are motivated to develop an automated technique to make mocking recommendations to facilitate unit testing. We studied 10,846 test scripts in four actively maintained open-source projects that use mocked objects, aiming to characterize the dependencies thatare mocked in unit testing. Based on our observations on mocking practices, we designed and implemented a tool, MockSniffer, to identify and recommend mocks for unit tests. The tool is fully automated and requires only the CUT and its dependencies as input. It leverages machine learning techniques to make mocking recommendations by holistically considering multiple factors that can affect developers' mocking decisions. Our evaluation of Mock-Sniffer on ten open-source projects showed that it outperformed three baseline approaches, and achieved good performance in two potential application scenarios.