Entity Recommendation for Everyday Digital Tasks

Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their real-world tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data

EntityBot: Supporting everyday digital tasks with entity recommendations

Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user's digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals' computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then applied to detect the user's task context to retrieve entities such as applications, documents, contact information, and several keywords determining the task. The system has been evaluated with real-world tasks, demonstrating that the recommendation had an impact on the tasks and led to high user satisfaction

Rapid unwinding of triplet repeat hairpins by Srs2 helicase of Saccharomyces cerevisiae

Expansions of trinucleotide repeats cause at least 15 heritable human diseases. Single-stranded triplet repeat DNA in vitro forms stable hairpins in a sequence-dependent manner that correlates with expansion risk in vivo. Hairpins are therefore considered likely intermediates during the expansion process. Unwinding of a hairpin by a DNA helicase would help protect against expansions. Yeast Srs2, but not the RecQ homolog Sgs1, blocks expansions in vivo in a manner largely dependent on its helicase function. The current study tested the idea that Srs2 would be faster at unwinding DNA substrates with an extrahelical triplet repeat hairpin embedded in a duplex context. These substrates should mimic the relevant intermediate structure thought to occur in vivo. Srs2 was faster than Sgs1 at unwinding several substrates containing triplet repeat hairpins or another structured loop. In contrast, control substrates with an unstructured loop or a Watson–Crick duplex were unwound equally well by both enzymes. Results with a fluorescently labeled, three-way junction showed that Srs2 unwinding proceeds unabated through extrahelical triplet repeats. In summary, Srs2 maintains its facile unwinding of triplet repeat hairpins embedded within duplex DNA, supporting the genetic evidence that Srs2 is a key helicase in Saccharomyces cerevisiae for preventing expansions

New Functions of Ctf18-RFC in Preserving Genome Stability outside Its Role in Sister Chromatid Cohesion

Expansion of DNA trinucleotide repeats causes at least 15 hereditary neurological diseases, and these repeats also undergo contraction and fragility. Current models to explain this genetic instability invoke erroneous DNA repair or aberrant replication. Here we show that CAG/CTG tracts are stabilized in Saccharomyces cerevisiae by the alternative clamp loader/unloader Ctf18-Dcc1-Ctf8-RFC complex (Ctf18-RFC). Mutants in Ctf18-RFC increased all three forms of triplet repeat instability—expansions, contractions, and fragility—with effect over a wide range of allele lengths from 20–155 repeats. Ctf18-RFC predominated among the three alternative clamp loaders, with mutants in Elg1-RFC or Rad24-RFC having less effect on trinucleotide repeats. Surprisingly, chl1, scc1-73, or scc2-4 mutants defective in sister chromatid cohesion (SCC) did not increase instability, suggesting that Ctf18-RFC protects triplet repeats independently of SCC. Instead, three results suggest novel roles for Ctf18-RFC in facilitating genomic stability. First, genetic instability in mutants of Ctf18-RFC was exacerbated by simultaneous deletion of the fork stabilizer Mrc1, but suppressed by deletion of the repair protein Rad52. Second, single-cell analysis showed that mutants in Ctf18-RFC had a slowed S phase and a striking G2/M accumulation, often with an abnormal multi-budded morphology. Third, ctf18 cells exhibit increased Rad52 foci in S phase, often persisting into G2, indicative of high levels of DNA damage. The presence of a repeat tract greatly magnified the ctf18 phenotypes. Together these results indicate that Ctf18-RFC has additional important functions in preserving genome stability, besides its role in SCC, which we propose include lesion bypass by replication forks and post-replication repair

Gendered dimensions of obesity in childhood and adolescence

BACKGROUND: The literature on childhood and adolescent obesity is vast. In addition to producing a general overview, this paper aims to highlight gender differences or similarities, an area which has tended not to be the principal focus of this literature. METHODS: Databases were searched using the terms 'obesity' and 'child', 'adolescent', 'teenager', 'youth', 'young people', 'sex', 'gender', 'masculine', 'feminine', 'male', 'female', 'boy' and 'girl' (or variations on these terms). In order to limit the potential literature, the main focus is on other reviews, both general and relating to specific aspects of obesity. RESULTS: The findings of genetic studies are similar for males and females, and differences in obesity rates as defined by body mass index are generally small and inconsistent. However, differences between males and females due to biology are evident in the patterning of body fat, the fat levels at which health risks become apparent, levels of resting energy expenditure and energy requirements, ability to engage in certain physical activities and the consequences of obesity for the female reproductive system. Differences due to society or culture include food choices and dietary concerns, overall physical activity levels, body satisfaction and the long-term psychosocial consequences of childhood and adolescent obesity. CONCLUSION: This review suggests differences between males and females in exposure and vulnerability to obesogenic environments, the consequences of child and adolescent obesity, and responses to interventions for the condition. A clearer focus on gender differences is required among both researchers and policy makers within this field

Postreplication Repair Inhibits CAG · CTG Repeat Expansions in Saccharomyces cerevisiae

Trinucleotide repeats (TNRs) are unique DNA microsatellites that can expand to cause human disease. Recently, Srs2 was identified as a protein that inhibits TNR expansions in Saccharomyces cerevisiae. Here, we demonstrate that Srs2 inhibits CAG · CTG expansions in conjunction with the error-free branch of postreplication repair (PRR). Like srs2 mutants, expansions are elevated in rad18 and rad5 mutants, as well as the PRR-specific PCNA alleles pol30-K164R and pol30-K127/164R. Epistasis analysis indicates that Srs2 acts upstream of these PRR proteins. Also, like srs2 mutants, the pol30-K127/164R phenotype is specific for expansions, as this allele does not alter mutation rates at dinucleotide repeats, at nonrepeating sequences, or for CAG · CTG repeat contractions. Our results suggest that Srs2 action and PRR processing inhibit TNR expansions. We also investigated the relationship between PRR and Rad27 (Fen1), a well-established inhibitor of TNR expansions that acts at 5′ flaps. Our results indicate that PRR protects against expansions arising from the 3′ terminus, presumably replication slippage events. This work provides the first evidence that CAG · CTG expansions can occur by 3′ slippage, and our results help define PRR as a key cellular mechanism that protects against expansions

Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood.

In a multisensory task, human adults integrate information from different sensory modalities--behaviorally in an optimal Bayesian fashion--while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities--i.e. selection--at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon

Impact of .

<p>We used four different values (0.05, 0.25, 0.45, 0.80) for from being conservative to liberal in terms of confidence. All graphs are results of averaging over 10 independent runs and passing a moving average window with size 500. (<b>A</b>) Average acceptance rate (1–rejection rate) of the individual sensors in the proposed method (MOS). The upper/lower ribbon for each value of represents visual/auditory sensor. By increasing , the test becomes harder for the individual sensors to pass. (<b>B</b>) The average dominancy percentage of each source in decision making (MOS). For each value of , the ascending ribbon represents integration and the two descending ribbons represent selection of visual and auditory sensors. Increasing results in earlier cross of the ascending and the descending ribbons; i.e. earlier switch from selection to integration.</p

Dominancy of subspaces over time.

<p>The average dominancy percentage of different combination of sensors in decision making (LUS). Subspaces including the unreliable source have been filtered. Furthermore, dependency on the integration of reliable sensors increases over time.</p

The function that implements LUS method.

<p>The function that implements LUS method.</p