The Impact of the Ketogenic Diet on Depression and Psychological Wellbeing: A Randomised Controlled Trial with Integrated Qualitative Analysis

Background and aims: There is evidence to suggest that a ketogenic diet (KD) may help to alleviate psychiatric symptoms, including depression, but this has not been studied extensively or compared directly to the impact of the more common low carbohydrate diet (LCD). The aim of this research was to understand the impact of a non-calorie-restricted low carbohydrate diet and ketogenic diet on depression and aspects of psychological well-being in those with either mild to moderate depressive symptoms or low or no depressive symptoms. Materials and methods: In a randomised control trial with quasi experimental design, participants with mild to moderate depressive symptoms and low depressive symptoms were randomised into either a LCD, a KD, or a control diet (diet as usual) generating a total of 6 participant groups. The dietary interventions (LCD and KD) were delivered through an online education platform for 12 weeks, followed by 12 weeks of unsupported continued diet. The control diet was maintained for a total of 6 weeks. Examinations at baseline (T0), day 1, week 6, week 12, and week 24 included questionnaires and psychological measures stress, anxiety, mental wellbeing, positive and negative affect, depression, self-compassion, social support, and body appreciation. Demographical data was also collected and analysed. Attrition rates were explored post intervention, and a qualitative thematic analysis was carried out on participants interview data following the KD to better understand their experience of the dietary intervention. Results: From study 1, the KD group saw no improvements in psychological wellbeing. The LCD group reported significant improvements in stress, anxiety, and negative affect after 12 weeks and in depressive symptoms after 24 weeks compared to the KD and control group. Significant improvements in positive affect, mental well-being and depressive symptoms were found in those with lower levels of body appreciation compared to those with higher levels, regardless of diet type. From study 2, dropout rates peaked during the 12-week intervention compared to post intervention and the end of the study at 24 weeks. Those with depressive symptoms were less likely to drop out of the study compared to those who were ‘healthy’. From the qualitative study 3, participants in the KD group experienced both physical and mental health improvements. They lost weight and experienced an increase in confidence, energy, and self-esteem. Some reported a renewed meaning and purpose in life. Conclusion: The ketogenic diet did not improve quantitatively measured depressive symptoms or aspects of psychological well-being from self-reported questionnaires. However, from interview data, improvements were experienced by those on the ketogenic diet suggesting that the diet worked for some. Reasons for this contradiction are explored and may be explained in part, by reviewing the intervention design. A low carbohydrate diet was found to improve some aspects of psychological well-being in those with mild to moderate depressive symptoms over 24 weeks. Adverse events experienced were mild and temporary, but retention of participants was challenging. Further well-designed randomised control trials are warranted to identify whether a ketogenic diet would improve psychological well-being in those with more severe depression akin to antidepressant efficacy

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making

Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success. We refer to these scenarios as AI-assisted decision making, where the individual strengths of the human and the AI come together to optimize the joint decision outcome. A key to their success is to appropriately \textit{calibrate} human trust in the AI on a case-by-case basis; knowing when to trust or distrust the AI allows the human expert to appropriately apply their knowledge, improving decision outcomes in cases where the model is likely to perform poorly. This research conducts a case study of AI-assisted decision making in which humans and AI have comparable performance alone, and explores whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. Specifically, we study the effect of showing confidence score and local explanation for a particular prediction. Through two human experiments, we show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors. We also highlight the problems in using local explanation for AI-assisted decision making scenarios and invite the research community to explore new approaches to explainability for calibrating human trust in AI

Dynamics and consequences of DNA looping by the FokI restriction endonuclease

Genetic events often require proteins to be activated by interacting with two DNA sites, trapping the intervening DNA in a loop. While much is known about looping equilibria, only a few studies have examined DNA-looping dynamics experimentally. The restriction enzymes that cut DNA after interacting with two recognition sites, such as FokI, can be used to exemplify looping reactions. The reaction pathway for FokI on a supercoiled DNA with two sites was dissected by fast kinetics to reveal, in turn: the initial binding of a protein monomer to each site; the protein–protein association to form the dimer, trapping the loop; the subsequent phosphodiester hydrolysis step. The DNA motion that juxtaposes the sites ought on the basis of Brownian dynamics to take ∼2 ms, but loop capture by FokI took 230 ms. Hence, DNA looping by FokI is rate limited by protein association rather than DNA dynamics. The FokI endonuclease also illustrated activation by looping: it cut looped DNA 400 times faster than unlooped DNA

Winter habitat selection by marsh tits Poecile palustris in a British woodland

Capsule: Radio-tracking and remote sensing showed Marsh Tits selected for English Oak Quercus robur trees within large winter home-ranges. Aims: To investigate winter habitat selection by Marsh Tits in a British wood, testing for preferences in tree species and woodland structure. Methods: Thirteen Marsh Tits were radio-tracked during the winter, and home-ranges were derived. Lidar and hyperspectral data were used to compare the vegetation structure and tree species composition of entire home-ranges and the core areas of intensive use within. Instantaneous sampling observations provided further information for tree species utilization. Results: The mean home-range was very large (39 ha, n=13). There were no significant differences in mean tree height, canopy closure, understorey height, or shrub volume between full home-ranges and the core areas of use. Core areas contained a significantly greater proportion of English Oak relative to availability in the full home-ranges. Instantaneous sampling confirmed that English Oak was used significantly more than other trees. Conclusion: Selection for English Oak during winter contrasted with previous studies of breeding territories, indicating that habitat usage varies seasonally and demonstrating the need for habitat selection studies throughout the year. Large home-ranges help to explain the Marsh Tit’s sensitivity to habitat fragmentation

Bootstrapping Conversational Agents With Weak Supervision

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textit{search, label, and propagate} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.Comment: 6 pages, 3 figures, 1 table, Accepted for publication in IAAI 201

Spanish suffixes in tagalog: the case of common nouns

Language Use in Past and Presen

Cryptic diversity of the jewel beetles Agrilus viridis (Coleoptera: Buprestidae) hosted on hazelnut

The genus Agrilus (Coleoptera: Buprestidae) represents a taxonomic puzzle, since the boundaries between species, subspecies and morphotypes tied to different host plants are sometimes difficult to establish on morphological characteristics alone. Some Agrilus species can cause severe agricultural damage; this makes correct distinctions of the taxon and knowing whether the insects switch from one host plant to another important. This study of mtDNA examined the genetic characteristics of lineages of A. viridis, a jewel beetle recently found causing damage to the hazelnut Corylus avellana in NW Italy. Three mitochondrial markers (a portion of the 12S rDNA and a DNA-fragment including partial NADH dehydrogenase subunit I gene, the tRNA Leucine gene and partial 16S rDNA, and partial  Cytochrome c oxidase) were compared between individuals collected on birch Betula sp., beech Fagus sp., willow Salix sp., alder Alnus sp. and hazelnut. We found a high genetic distance between A. viridis sampled on different host plants, while individuals sampled on the same host plant were similar despite a considerable geographic gap between sampled areas. Our study supports the general pattern for strong ecological separation between populations living on different host plants