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Agent-based models can be used to better understand the impacts of lifting restrictions or implementing interventions during a pandemic. However, agent-based models are computationally expensive, and running a model of a large population can result in a simulation taking too long to run for the model to be a useful analysis tool during a public health crisis. To reduce computing time and power while running a detailed agent-based model for the spread of COVID-19 in the Republic of Ireland, we introduce a scaling factor that equates 1 agent to 100 people in the population. We present the results from model validation and show that the scaling factor increases the variability in the model output, but the average model results are similar in scaled and un-scaled models of the same population, and the scaled model is able to accurately simulate the number of cases per day in Ireland during the autumn of 2020. We then test the usability of the model by using the model to explore the likely impacts of increasing community mixing when schools reopen after summer holidays. [ABSTRACT FROM AUTHOR]

Background: In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest effect on an outbreak.Methods: To test the effects of a school closure policy on the spread of an infectious disease (in this case measles) we run simulations closing schools based on either the proximity of the town to the initial outbreak or the centrality of the town within the network of towns in the simulation. To do this we use a hybrid model that combines an agent-based model with an equation-based model. In our analysis, we use three measures to compare the effects of different intervention strategies: the total number of model runs leading to an outbreak, the total number of infected agents, and the geographic spread of outbreaks.Results: Our results show that closing down the schools in the town where an outbreak begins and the town with the highest in degree centrality provides the largest reduction in percent of runs leading to an outbreak as well as a reduction in the geographic spread of the outbreak compared to only closing down the town where the outbreak begins. Although closing down schools in the town with the closest proximity to the town where the outbreak begins also provides a reduction in the chance of an outbreak, we do not find the reduction to be as large as when the schools in the high in degree centrality town are closed.Conclusions: Thus we believe that focusing on high in degree centrality towns during an outbreak is important in reducing the overall size of an outbreak. [ABSTRACT FROM AUTHOR]

In this paper we explain the difference between two aspects of semantic relatedness: taxonomic and thematic relations. We notice the lack of evaluation tools for measuring thematic relatedness, identify two datasets that can be recommended as thematic benchmarks, and verify them experimentally. In further experiments, we use these datasets to perform a comprehensive analysis of the performance of an extensive sample of computational models of semantic relatedness, classified according to the sources of information they exploit. We report models that are best at each of the two dimensions of semantic relatedness and those that achieve a good balance between the two. [ABSTRACT FROM AUTHOR]

Globalization and technical change have had a significant impact on work in the cultural industries. Online games are services that are networked, operate around the clock and require ongoing player and company input. The industry's content production networks are dispersed internationally, and many of its services are offered transnationally. These new services have generated new forms of work, like community management, which are often outsourced to near-to-market locations. Much of the research on media work is focused on high status creative occupations or the free labour of amateurs. This paper draws upon the findings of a content analysis of job advertisements and face-to-face interviews with community managers to examine the recruitment and work of community managers. Using scholarship on media production, media work and emotional labour this article argues that recruitment and organizational practices surrounding community managers appropriate passion, community, and experience in the service of capital, but also marginalize these workers. While community managers see themselves as creative workers, their visibility and creative autonomy are limited by organizational and workplace cultures. [ABSTRACT FROM AUTHOR]

The article discusses jurisdictional nexus standards in relation to the U.S. government's ability to tax cross-border commerce as of April 2014, focusing on U.S. international tax principles and the nation's Internal Revenue Code. Electronic commerce and efforts to mitigate the risks associated with multiple taxation are examined. Domiciles of foreign companies and bilateral income tax treaties are mentioned, along with the term "permanent establishment" which applies to stores and factories.

Objectives: Surgical treatment of odontoid fractures is recommended by many surgeons to prevent sudden neurologic injury or progressive myelopathy. Less aggressive approach to the treatment of odontoid fractures has been advocated by some authors especially in the elderly population. Very few reports have followed up patients' outcomes following conservative treatment of odontoid fractures. Here we evaluate the clinical and radiographic results of patients without myelopathy treated without surgery for an odontoid type fracture. Patients and methods: 101 patients with traumatic odontoid fracture admitted to the Pennsylvania State Hershey Medical Center between 1998 and 2008. Fractures were defined using a CT scan according to the Anderson-D'Alonzo Classification. Conservative treatment was pursued in appropriately selected patients. Results: Fifty-nine patients were selected to be treated in a cervical collar. Sixteen patients failed using radiographic evidence and continued neck pain. Fourteen patients went on to be surgically stabilized. The other two patients opted to continue with cervical orthosis and regular clinical evaluations. The Forty-three remaining patients had stable imaging studies and with no other complaints. None of the patients developed myelopathy symptoms during the follow-up period. Conclusions: Our results indicate that a select group of patients with odontoid fracture who are deemed stable on initial evaluation in a cervical orthosis may be effectively managed non-operatively. None of the patients who were managed conservatively had clinical worsening during the period of management. The decision to proceed with surgical treatment was based on failure of resolution of neck pain or worsening or concerning instability on imaging studies. However in many patients, even elderly patients in a surgical risks are greater, many odontoid fractures can be safely managed in a cervical orthosis. [ABSTRACT FROM AUTHOR]

Background Wiping of the mouth and nose at birth is an alternative method to oronasopharyngeal suction in delivery-room management of neonates, but whether these methods have equivalent effectiveness is unclear. Methods For this randomised equivalency trial, neonates delivered at 35 weeks' gestation or later at the University of Alabama at Birmingham Hospital, Birmingham, AL, USA, between October, 2010, and November, 2011, were eligible. Before birth, neonates were randomly assigned gentle wiping of the face, mouth (implemented by the paediatric or obstetric resident), and nose with a towel (wipe group) or suction with a bulb syringe of the mouth and nostrils (suction group). The primary outcome was the respiratory rate in the first 24 h after birth. We hypothesised that respiratory rates would differ by fewer than 4 breaths per min between groups. Analysis was by intention to treat. This study is registered with ClinicalTrials.gov, number NCT01197807. Findings 506 neonates born at a median of 39 weeks' gestation (IQR 38-40) were randomised. Three parents withdrew consent and 15 non-vigorous neonates with meconium-stained amniotic fluid were excluded. Among the 488 treated neonates, the mean respiratory rates in the first 24 h were 51 (SD 8) breaths per min in the wipe group and 50 (6) breaths per min in the suction group (difference of means 1 breath per min, 95% C1-2 to 0, p<0.001). Interpretation Wiping the nose and mouth has equivalent efficacy to routine use of oronasopharyngeal suction in neonates born at or beyond 35 weeks' gestation. Funding None. [ABSTRACT FROM AUTHOR]

The author, a college art professor, discusses observing his daughter drawing as a toddler, and describes the effect of his daughter's art on his own perception of art. Storytelling in art, the psychology of young children, and learning are discussed, as well as communication ability in children. Visual culture and efficiency are discussed, and the author mentions teaching freshman about artists such as Southern Cheyenne Indian Howling Wolf and American artist James Taylor. Realism versus memory in art is also mentioned.

This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cognition module, which provides the interface to the models of prepositional semantics, and the other components in the architecture. Following this, we present two new computational models of topological and projective spatial prepositions. The main novelty within these models is the fact that they account for the contextual effect which other distractor objects in a visual scene can have on the region described by a given preposition. We next present psycholinguistic tests evaluating our approach to distractor interference on prepositional semantics, and illustrate how these models are used for both interpretation and generation of prepositional expressions. [ABSTRACT FROM AUTHOR]

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual’s social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831–0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals. [ABSTRACT FROM AUTHOR]

Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the MT model to generate a better translation. In recent times, MT researchers have turned their focus to this line of MT research. As an example, hierarchical attention network (HAN) models use document-level context for translation prediction. In this work, we studied translations produced by the HAN-based MT systems. We examined how contextual information improves translation in document-level NMT. More specifically, we investigated why context-aware models such as HAN perform better than vanilla baseline NMT systems that do not take context into account. We considered Hindi-to-English, Spanish-to-English and Chinese-to-English for our investigation. We experimented with the formation of conditional context (i.e., neighbouring sentences) of the source sentences to be translated in HAN to predict their target translations. Interestingly, we observed that the quality of the target translations of specific source sentences highly relates to the context in which the source sentences appear. Based on their sensitivity to context, we classify our test set sentences into three categories, i.e., context-sensitive, context-insensitive and normal. We believe that this categorization may change the way in which context is utilized in document-level translation. [ABSTRACT FROM AUTHOR]

Open software repositories make large amounts of source code publicly available. Potentially, this source code could be used as training data to develop new, machine learning-based programming tools. For many applications, however, raw code scraped from online repositories does not constitute an adequate training dataset. Building on the recent and rapid improvements in machine translation (MT), one possibly very interesting application is code generation from natural language descriptions. One of the bottlenecks in developing these MT-inspired systems is the acquisition of parallel text-code corpora required for training code-generative models. This paper addresses the problem of automatically synthetizing parallel text-code corpora in the software testing domain. Our approach is based on the observation that self-documentation through descriptive method names is widely adopted in test automation, in particular for unit testing. Therefore, we propose synthesizing parallel corpora comprised of parsed test function names serving as code descriptions, aligned with the corresponding function bodies. We present the results of applying one of the state-of-the-art MT methods on such a generated dataset. Our experiments show that a neural MT model trained on our dataset can generate syntactically correct and semantically relevant short Java functions from quasi-natural language descriptions of functionality. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning holds significant potential for application in industrial decision-making, offering a promising alternative to traditional physical models. However, its black-box learning approach presents challenges for real-world and safety-critical systems, as it lacks interpretability and explanations for the derived actions. Moreover, a key research question in deep reinforcement learning is how to focus policy learning on critical decisions within sparse domains. This paper introduces a novel approach that combines probabilistic modeling and reinforcement learning, providing interpretability and addressing these challenges in the context of safety-critical predictive maintenance. The methodology is activated in specific situations identified through the input–output hidden Markov model, such as critical conditions or near-failure scenarios. To mitigate the challenges associated with deep reinforcement learning in safety-critical predictive maintenance, the approach is initialized with a baseline policy using behavioral cloning, requiring minimal interactions with the environment. The effectiveness of this framework is demonstrated through a case study on predictive maintenance for turbofan engines, outperforming previous approaches and baselines, while also providing the added benefit of interpretability. Importantly, while the framework is applied to a specific use case, this paper aims to present a general methodology that can be applied to diverse predictive maintenance applications. • Segmentation of the state–space through probabilistic modeling. • Initializing baseline policy for guided exploration using Behavioral Cloning (BC). • Identifying segmented abnormal states with probabilistic modeling and DRL. • Interpreting operating conditions using human feedback, probabilistic model, and DRL. • Enhanced cross-validation methods for predictive maintenance benchmarking. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Oronasopharyngeal suction versus wiping of the mouth and nose at birth: A randomised equivalency trial," by John Kelleher, R. Bhat, A.A. Salas and colleagues, with authors' reply included.

Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks. [ABSTRACT FROM AUTHOR]

Errors in visual perception may cause problems in situated dialogues. We investigated this problem through an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot. We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions. We then introduced different methods for the user to request information about the robot’s understanding of the environment. We quantify the impact of perception errors on the dialogues, and investigate resolution attempts by users at a structural level and at the level of referring expressions. [ABSTRACT FROM AUTHOR]

Background: Escherichia coli (E. coli) comprise part of the normal vaginal microflora. Transfer from mother to neonate can occur during delivery resulting, sometimes, in neonatal bacterial disease. Here, we aim to report the first outbreak of CTX-M ESBL-producing E. coli with evidence of mother-to-neonate transmission in an Irish neonatal intensive care unit (NICU) followed by patient-to-patient transmission.Methods: Investigation including molecular typing was conducted. Infection was defined by clinical and laboratory criteria and requirement for antimicrobial therapy with or without positive blood cultures. Colonisation was determined by isolation without relevant symptoms or indicators of infection.Results: Index case was an 8-day-old baby born at 34 weeks gestation who developed ESBL-producing E. coli infections at multiple body sites. Screening confirmed their mother as colonised with ESBL-producing E. coli. Five other neonates, in the NICU simultaneously with the index case, also tested positive. Of these, four were colonised while one neonate developed sepsis, requiring antimicrobial therapy. The second infected neonate's mother was also colonised by ESBL-producing E. coli. Isolates from all eight positive patients (6 neonates, 2 mothers) were compared using pulsed-field gel electrophoresis (PFGE). Two distinct ESBL-producing strains were implicated, with evidence of transmission between mothers and neonates for both strains. All isolates were confirmed as CTX-M ESBL-producers. There were no deaths associated with the outbreak.Conclusions: Resources were directed towards control interventions focused on hand hygiene and antimicrobial stewardship, which ultimately proved successful. Since this incident, all neonates admitted to the NICU have been screened for ESBL-producers and expectant mothers are screened at their first antenatal appointment. To date, there have been no further outbreaks. [ABSTRACT FROM AUTHOR]

In order to mitigate early hypoglycemia in preterm infants, some clinicians have recently explored interventions such as delivery room commencement of dextrose infusions or delivery room administration of buccal dextrose gel. This review aimed to systematically investigate the literature regarding the provision of delivery room (prior to admission) parenteral glucose as a method to reduce the risk of initial hypoglycemia (measured at the time of NICU admission blood testing) in preterm infants. Using PRISMA guidelines a literature search (May 2022) was conducted using PubMed, Embase, Scopus, Cochrane Library, OpenGrey, and Prospero databases. The clinicaltrials.gov database was searched for possible completed/ongoing clinical trials. Studies that included moderate preterm ( ≤ 33+6 weeks) or younger birth gestations or very low birth weight (or smaller) infants, and that administered parenteral glucose in the delivery room were included. The literature was appraised via data extraction, narrative synthesis, and critical review of the study data. A total of five studies (published 2014–2022) were eligible for inclusion (three before-after "quasi-experimental" studies, one retrospective cohort study, and one case-control study). Most included studies used intravenous dextrose as the intervention. Individual study effects (odds ratios) favored the intervention in all included studies. It was felt that the low number of studies, the variability in study design, and the nonadjustment for confounding co-interventions (co-exposures) precluded a meta-analysis. Quality assessment of the studies revealed a spectrum of bias from low to high risk, however, most studies had moderate to high risk of bias, and their direction of bias favored the intervention. This extensive search and systematic appraisal of the literature indicates that there exists few studies (these are low grade and at moderate to high risk of bias) for the interventions of either intravenous or buccal dextrose given in the delivery room. It is not clear if these interventions impact on rates of early (NICU admission) hypoglycemia in these preterm infants. Obtaining intravenous access in the delivery room is not guaranteed and can be difficult in these small infants. Future research should consider various routes for commencing delivery room glucose in these preterm infants and should take the form of randomized controlled trials. [ABSTRACT FROM AUTHOR]

The article presents an obituary for William Bruce Thomson, former consultant physician in Wycombe and Amersham Hospitals.

BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005–March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments): C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes. C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor. C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features. [ABSTRACT FROM AUTHOR]

As an alternative to current bone grafting strategies, a poly-lactide- co-glycolide/calcium phosphate composite microsphere-based scaffold has been synthesized by the direct formation of calcium phosphate within forming microspheres. It was hypothesized that the synthesis of low crystalline calcium phosphate within forming microspheres would provide a site-specific delivery of calcium ions to enhance calcium phosphate reprecipitation onto the scaffold. Both polymeric and composite scaffolds were incubated in simulated body fluid (SBF) for 8 weeks, during which time polymer molecular weight, scaffold mass, calcium ion concentration of SBF, pH of SBF, and calcium phosphate reprecipitation was monitored. Results showed a 20% decrease in polymeric scaffold molecular weight compared to 11–14% decrease for composite scaffolds over 8 weeks. Composite scaffold mass and SBF pH decreased for the first 2 weeks but began increasing after 2 weeks and continued to do so up to 8 weeks, suggesting interplay between pH changes and calcium phosphate dissolution/reprecipitation. Free calcium ion concentration of SBF containing composite scaffolds increased 20–40% over control values within 4 h of incubation but then dropped as low as 40% below control values, suggesting an initial burst release of calcium ions followed by a reprecipitation onto the scaffold surface. Scanning electron micrographs confirm calcium phosphate reprecipitation on the scaffold surface after only 3 days of incubation. Results suggest the composite scaffold is capable of initiating calcium phosphate reprecipitation which may aid in bone/implant integration. [ABSTRACT FROM AUTHOR]