Post-traumatic stress symptoms in pathological gambling: Potential evidence of anti-reward processes

Excessive gambling is considered to be a part of the addiction spectrum. Stress-like emotional states are a key feature both of pathological gambling (PG) and of substance addiction. In substance addiction, stress symptomatology has been attributed in part to “anti-reward” allostatic neuroadaptations, while a potential involvement of anti-reward processes in the course of PG has not yet been investigated. Methods To that end, individuals with PG (n = 22) and mentally healthy subjects (n = 13) were assessed for trauma exposure and post-traumatic stress symptomatology (PTSS) using the Life Events Checklist and the Civilian Mississippi Scale, respectively. Results In comparison with healthy subjects, individuals with PG had significantly greater PTSS scores including greater physiological arousal sub-scores. The number of traumatic events and their recency were not significantly different between the groups. In the PG group, greater gambling severity was associated with more PTSS, but neither with traumatic events exposure nor with their recency. Conclusions Our data replicate prior reports on the role of traumatic stress in the course of PG and extend those findings by suggesting that the link may be derived from the anti-reward-type neuroadaptation rather than from the traumatic stress exposure per se

Epidemiology of overweight and obesity in early childhood in the Gulf Cooperation Council countries:a systematic review and meta-analysis protocol

Introduction There has been a notable increase in the prevalence of overweight and obesity in school-aged children in many industrialised regions. The worldwide prevalence of childhood overweight and obesity increased from 4.2% in 1990 to 6.7% in 2010. Although many studies have been published, the epidemiological burden of overweight and obesity in the Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates) is unclear. There is a need to bring together and appraise relevant studies in order to estimate the epidemiological burden (including incidence, prevalence, risk factors, trend over time) of overweight and obesity in this region and thus help to inform national and regional policies. Methods and analysis We will conduct a systematic review and meta-analysis on the epidemiology of overweight and obesity in early childhood including incidence, prevalence, risk factors and trends over time in the GCC countries. We will search international electronic databases including MEDLINE, EMBASE, Cochrane Library, ISI Web of Science, CINAHL, Google Scholar, AMED, Psych INFO, CAB International and WHO Global Health Library for published, unpublished and in-progress epidemiological studies of interest published from inception to 2017. In addition, we will contact an international panel of experts on the topic. There will be no restriction on the language of publication of studies. We will use the Effective Public Health Practice Project (EPHPP) to appraise the methodological quality of included studies. Meta-analysis will be undertaken using random effects models. Ethics and dissemination Ethical approval is not required. The outcome of the review will be disseminated through conference presentations and peer-reviewed journal publication

Ultrafast Excited-State Dynamics of Rhenium(I) Photosensitizers [Re(Cl)(CO)_(3)(N,N)] and [Re(imidazole)(CO)_(3)(N,N)]^+: Diimine Effects

Femto- to picosecond excited-state dynamics of the complexes [Re(L)(CO)_(3)(N,N)]^n (N,N = bpy, phen, 4,7-dimethyl-phen (dmp); L = Cl, n = 0; L = imidazole, n = 1+) were investigated using fluorescence up-conversion, transient absorption in the 650−285 nm range (using broad-band UV probe pulses around 300 nm) and picosecond time-resolved IR (TRIR) spectroscopy in the region of CO stretching vibrations. Optically populated singlet charge-transfer (CT) state(s) undergo femtosecond intersystem crossing to at least two hot triplet states with a rate that is faster in Cl (~100 fs)^(−1) than in imidazole (~150 fs)^(−1) complexes but essentially independent of the N,N ligand. TRIR spectra indicate the presence of two long-lived triplet states that are populated simultaneously and equilibrate in a few picoseconds. The minor state accounts for less than 20% of the relaxed excited population. UV−vis transient spectra were assigned using open-shell time-dependent density functional theory calculations on the lowest triplet CT state. Visible excited-state absorption originates mostly from mixed L;N,N^(•−) → Re^(II) ligand-to-metal CT transitions. Excited bpy complexes show the characteristic sharp near-UV band (Cl, 373 nm; imH, 365 nm) due to two predominantly ππ*(bpy^(•−)) transitions. For phen and dmp, the UV excited-state absorption occurs at 305 nm, originating from a series of mixed ππ* and Re → CO;N,N•− MLCT transitions. UV−vis transient absorption features exhibit small intensity- and band-shape changes occurring with several lifetimes in the 1−5 ps range, while TRIR bands show small intensity changes (≤5 ps) and shifts (~1 and 6−10 ps) to higher wavenumbers. These spectral changes are attributable to convoluted electronic and vibrational relaxation steps and equilibration between the two lowest triplets. Still slower changes (≥15 ps), manifested mostly by the excited-state UV band, probably involve local-solvent restructuring. Implications of the observed excited-state behavior for the development and use of Re-based sensitizers and probes are discussed

Methodology for Self-Adaptively Solving Multi-Objective Scheduling Problems

Scheduling practices are critical decision-making processes that substantially influence the overall performance of cloud and manufacturing environments. Therefore, scheduling problems have been a primary concern of practitioners and scholars in this field for decades. The majority of scheduling problems are known NP-hard optimization problems. Hence, heuristic and improvement methods have been conventionally adopted to address scheduling concerns. Heuristic methods exhibit a light execution time but fail to sustain high solution quality for solving complex problems. Improvement methods deliver high-quality solutions but are associated with high computational effort. To mitigate the individual limitations of both methods, scholars started investigating hybrid solution methods that may combine their advantages. The individual limitations of the conventional methods, in addition to the complex nature of the scheduling problem, result in a poor practical adoption of presented scheduling methods. Recently, Deep Reinforcement Learning (DRL) methods substantiated a fundamental breakthrough and have been successfully adopted in the gaming domain. The foundational design of DRL methods includes optimization elements, making them suitable for addressing scheduling problems. Therefore, a scheduling methodology is presented that efficiently facilitates the combined utilization of heuristic, metaheuristic, and deep reinforcement learning methods to solve scheduling problems in cloud and manufacturing environments. Since most industrial scheduling problems are subject to multi-objective optimization measures, the methodology addresses scheduling concerns considering system efficiency and customer satisfaction objective measures. Parallelization and scalability technologies have been adopted to design and develop the presented artifact to achieve computational efficiency. To conduct the research systematically, the proposed methodology relies on the Design Science Research (DSR) framework and adheres to its fundamental design activities. The identified research gap, validated by the theoretical findings and the needs of application environments, is translated into functional and non-functional requirements of the artifact. The derived functional and non-functional requirements are then mapped into functionality layers to define the overall functional structure of the proposed methodology. The artifact is designed using component and modular design practices to address single-stage and multi-stage scheduling problems in cloud and manufacturing, respectively. The combined utilization of simulation, heuristic, improvement, and deep reinforcement learning methods was achieved by designing and developing a scheduling data model, several optimization encoding models for scheduling problems, DRL scheduling models, and a DRL evaluation model. The developed scheduling data model facilitates flexible instantiation of the methodology to address single-stage or multi-stage scheduling problems considering multiple objective mea- sures. The subsequent implementation of the artifact design is presented as a proof of concept and evaluated based on the DRS framework. The developed prototype is designed to support a multi-architecture infrastructure deployment and execution. The simulation and heuristic, as well as the artifact’s optimization and machine learning subsystems, are developed and deployed with parallelization and scalability features. The developed prototype is evaluated on multiple use cases to address multi-objective scheduling problems in cloud and manufacturing environments. Its utility was evaluated for solving real multi-stage scheduling problems in manufacturing environments. Compared to related works, the artifact’s optimization and DRL methods delivered, on average, 31.7% improved solutions in minimizing the system efficiency objective measures. The solutions also minimized penalties and delays by 33.3%, contributing to higher customer satisfaction.Planungsabläufe sind entscheidende Prozesse, die die Gesamtleistung von Cloud- und Produktionsumgebungen maßgeblich beeinflussen. Daher beschäftigen sich Praktiker und Wissenschaftler seit Jahrzehnten intensiv mit Planungsproblemen. Die meisten Maschinenplanungsprobleme gelten als NP-schwere Optimierungsprobleme, weshalb häufig Heuristiken und Optimierungsmethoden zur Lösung eingesetzt werden. Heuristische Methoden zeichnen sich durch kurze Ausführungszeiten aus, können jedoch bei komplexen Problemen keine hohe Lösungsqualität garantieren. Im Ge- gensatz dazu liefern Optimierungsmethoden hochwertige Lösungen, sind jedoch mit erheblichem Rechenaufwand verbunden. Um die Nachteile beider Methoden zu überwinden, haben Wissenschaftler hybride Lösungsmethoden erforscht, die die Vorteile beider Ansätze kombinieren. Aufgrund der individuellen Einschränkungen herkömmlicher Methoden sowie der Komplexität von Maschinenplanungsproblemen finden diese Ansätze jedoch selten praktische Anwendung. In den letzten Jahren haben Methoden des Deep Reinforcement Learning (DRL) bedeutende Fortschritte erzielt und wurden erfolgreich im Gaming-Bereich eingesetzt. Das Grundkonzept von DRL-Methoden beinhaltet Optimierungselemente, was sie für die Lösung von Maschinenplanungsproblemen besonders geeignet macht. Deshalb wird eine neue Scheduling-Methodik vorgestellt, die eine effiziente und präzise Kombination von heuristischen, metaheuristischen und Deep Reinforcement Learning-Methoden zur Lösung von Maschinenplanungsproblemen in Cloud- und Produktionsumgebungen ermöglicht. Da die meisten industriellen Maschinenplanungsprobleme multikriteriellen Optimierungsmaßnahmen unterliegen, berücksichtigt diese Methodik sowohl die Systemeffizienz als auch die Kundenzufriedenheit als Zielgrößen. Bei der Konzeption und Entwicklung des vorgestellten Artefakts wurden Technologien zur Parallelisierung und Skalierbarkeit genutzt, um eine hohe Recheneffizienz zu gewährleisten. Um die Forschung systematisch durchzuführen, stützt sich die vorgeschlagene Methodik auf den Rahmen der Design Science Research (DSR) und folgt deren grundlegenden Designaktivitäten. Die identifizierte Forschungslücke, die durch theoretische Erkenntnisse und praktische Bedürfnisse validiert wurde, wird in funktionale und nicht-funktionale Anforderungen an das Artefakt übersetzt. Diese Anforderungen werden dann in verschiedene Funktionalitätsschichten abge- bildet, um die gesamte funktionale Struktur der Methodik zu definieren. Das Artefakt wird unter Verwendung komponenten- und modularer Techniken entwickelt, um sowohl einstufige als auch mehrstufige Maschinenplanungsprobleme in Cloud- und Fertigungsumgebungen zu lösen. Die integrierte Nutzung von Simulations-, Heuristik-, Verbesserungs- und Deep Reinforcement Learning-Methoden wurde durch den Entwurf und die Entwicklung eines Datenmodells, mehrerer Optimierungskodierungsmodelle für Maschinenplanungsprobleme, DRL-modelle und eines DRL-Evaluierungsmodells erreicht. Die anschließende Implementierung des Artefaktdesigns wird als Proof of Concept vorgestellt und auf der Grundlage des DRS-Frameworks evaluiert. Der entwi- ckelte Prototyp ist so konzipiert, dass er den Einsatz und die Ausführung einer Multi-Architektur-Infrastruktur unterstützt. Die Simulation und Heuristik sowie die Teilsysteme für Optimierung und maschinelles Lernen des Artefakts werden mit Parallelisierungs- und Skalierungsfunktionen entwickelt und eingesetzt. Der entwickelte Prototyp wurde anhand mehrerer Anwendungsfälle evaluiert, um multikriteielle Maschinenplanungsprobleme in Cloud- und Produktionsumgebungen zu lösen. Sein Nutzen wurde für die Lösung realer mehrstufiger Maschinenplanungsprobleme in Produktionsumgebungen evaluiert. Im Vergleich zu verwandten Arbeiten lieferten die Optimierungs- und DRL-Methoden des Artefakts im Durchschnitt 31, 7% bessere Lösungen bei der Minimierung der Makepan und der Gesamtzahl der Hauptrüstzeiten, was zu einer höheren Systemeffizienz beitrug. Die Lösungen minimierten auch Penalties und Lieferterminverzögerungen um 33, 3%, was zu einer höheren Kundenzufriedenheit beitrug

And Justice for All: Viewing the wealth of three United States billionaires through three theories of distributive justice

Wealth inequality in the United States has now hit levels not last seen since the 1920s. With this, has come a general disagreement over how to address this inequality, as well as a debate on whether it’s even an issue. Since no clear consensus has been reached, a theory that describes what is just and what is unjust wealth accumulation is needed. By summarizing the theories of traditional Libertarianism, left libertarianism and Luck Egalitarianism, and applying them to the fortunes of Oprah Winfrey, Richard Sackler and Jeff Bezos, this paper arrives at the conclusion that a version of traditional Libertarianism is what’s currently practiced in the United States. Additionally, if wealth inequality is to be confronted, then left libertarianism should be adopted as the new standard of distributive justice

Some hemiurid trematodes of marine fishes of California

The following seven species of hemiurid trematodes, some with new host records, are reported in this study: Dissosaccus laevis, Genolinea laticauda, Lecithaster salmonis, Parahemiurus merus, Sterrhurus exodicus, Sterrhurus monticelli and Tubulovesicula lindbergi. Dissosaocus laeyis and Sterrhurus monticelli are reported for the first time from the West Coast of North America. Genolinea oncorhynohi Adams and Margolis 1958 is placed in synonymy with Genolinea laticauda Manter 1925. Keys for Genolinea and Tubulovesicula species, modified from Manter (1954) are included. Two tables, one summarizing the hemiurids reported from the West Coast of North America, the other a summary of hemiurids from Monterey Bay, California, encountered in this study, are also included

Investigating the association between obesity and asthma in 6- to 8-year-old Saudi children:a matched case-control study

Background: Previous studies have demonstrated an association between obesity and asthma, but there remains considerable uncertainty about whether this reflects an underlying causal relationship. Aims: To investigate the association between obesity and asthma in pre-pubertal children and to investigate the roles of airway obstruction and atopy as possible causal mechanisms. Methods: We conducted an age- and sex-matched case–control study of 1,264 6- to 8-year-old schoolchildren with and without asthma recruited from 37 randomly selected schools in Madinah, Saudi Arabia. The body mass index (BMI), waist circumference and skin fold thickness of the 632 children with asthma were compared with those of the 632 control children without asthma. Associations between obesity and asthma, adjusted for other potential risk factors, were assessed separately in boys and girls using conditional logistic regression analysis. The possible mediating roles of atopy and airway obstruction were studied by investigating the impact of incorporating data on sensitisation to common aeroallergens and measurements of lung function. Results: BMI was associated with asthma in boys (odds ratio (OR)=1.14, 95% confidence interval (CI), 1.08–1.20; adjusted OR=1.11, 95% CI, 1.03–1.19) and girls (OR=1.37, 95% CI, 1.26–1.50; adjusted OR=1.38, 95% CI, 1.23–1.56). Adjusting for forced expiratory volume in 1 s had a negligible impact on these associations, but these were attenuated following adjustment for allergic sensitisation, particularly in girls (girls: OR=1.25; 95% CI, 0.96–1.60; boys: OR=1.09, 95% CI, 0.99–1.19). Conclusions: BMI is associated with asthma in pre-pubertal Saudi boys and girls; this effect does not appear to be mediated through respiratory obstruction, but in girls this may at least partially be mediated through increased risk of allergic sensitisation

HEAR-BRUX: HEARable for handling BRUXism

Bruxism is a parafunctional oral behavior that can occur during sleep (sleep bruxism) or wakefulness (awake bruxism). Bruxism is characterized by teeth grinding and jaw clenching. It can lead to various health consequences such as tooth fracture, tooth wear, and muscle fatigue. Several devices have been developed to treat and detect the symptoms of bruxism. Oral splints are the most widely used device to manage sleep bruxism by eliminating tooth contact. Electromyography (EMG) is used to monitor the activity of the masticatory muscles to detect bruxism. However, mouth guards are passive devices that don't necessarily reduce the occurrence of bruxism, and EMG can be cumbersome to wear while sleeping or wakefulness. Haerables are wearable ear devices that can record signals such as sound. Such devices may be advantageous for the detection of bruxism induced events as they are easy to use and socially acceptable. Therefore, the question is whether ear devices - sometimes called hearables - that use sound as a biomarker can be affordable devices to detect bruxism. In a first study, I investigated the effect of the type of ear occlusion on recording and found that complete occlusion of the ear with a moldable earpiece supported recording of the characteristic feature of jaw clenching. For reasons of practicality and hygiene, I fitted an off-the-shelf earpiece with a transducer as part of an experimental setup in a second study to investigate the effect of transducer placement on the recording. The oral behaviors recorded were: jaw clenching, teeth grinding, reading, eating, and drinking. The transducers were placed on the zygomatic bone, frontal bone, temporal bone, and inside the ear. Finally, I investigated the use of 2D sound representations to classify the different oral behaviors recorded from the ears using deep learning. Three classifiers were tested, 2-Class (Grinding and Pause), 4-Class (Eating, Grinding, Pause, and Eeading), and 6-Class (Clenching, Drinking, Eating, Grinding, Pause, Reading). I observed that sounds of bruxism-induced events can be recorded from different parts of the head. From the experiment, I observed that the ear is an ideal location to record bruxism-induced sounds, because it compensates for head movements due to eating or drinking that may affect the recording. I also successfully classified the sounds recorded from the ear, but - as expected - the overall test accuracy of the classifier decreased as the number of classes increased. This result has good practical implications, as my approach demonstrated that bruxism-induced sounds can be recorded and distinguished from other oral behaviors. Finally, this project focused on bruxism from a biomechanical lens with the goal of developing a method to record and distinguish bruxism events from other oral behaviors. This method could be used to activate bio-feedback. Future research directions would be to investigating the causes of bruxism - which were not addressed in this work - and for this, further research is important to address one of its main causes, chronic emotional stress, which requires viewing bruxism through a biopsychosocial lens