Predictors of social service contact among teenagers in England

Very few UK studies make use of longitudinal general population data to explore social service contact for children and young people. Those that do only look at specific interventions such as care placements. This paper seeks to address this gap by asking to what extent do structural, neighbourhood, familial and individual characteristics predict social service contact. We provide an empirical answer by analysing the Longitudinal Survey of Young People in England, which includes data on social service contact in connection with young people's behaviour. Our findings indicate that social class, gender, ethnicity, stepfamily status and special education needs are all significant predictors of social service contact. Difficult parent–child relationships, frequent arguments and parents' lack of engagement with school meetings also matter, as does young people's own risk-taking behaviour. We conclude with a discussion of the limitation of the data for social work research and the implications of the findings

Use of Air Arthrograms to Aid in Joint Distraction During Hip Arthroscopic Surgery Decreases Postoperative Pain and Opioid Requirements.

BackgroundPositive-pressure air arthrography and venting of the hip capsule are techniques used to decrease the traction forces needed for joint distraction during hip arthroscopic surgery. Little is known about the effects that these techniques have on postoperative pain.HypothesisPositive-pressure air arthrography and venting during hip arthroscopic surgery will decrease patient-reported pain and narcotic requirements in the acute postoperative setting.Study designCohort study; Level of evidence, 3.MethodsA retrospective cohort analysis was conducted to analyze 35 patients who underwent positive-pressure air arthrography and venting to aid joint distraction during hip arthroscopic surgery versus a group with similar demographics, pathologies, and treatments who did not undergo air arthrography. Numeric pain rating scale (NPRS) scores and medication administration including narcotic and nonnarcotic analgesia in the postanesthesia care unit (PACU) were tracked and compared.ResultsThe maximum (7.17 vs 4.97, respectively), minimum (2.43 vs 1.09, respectively), and mean (5.15 vs 3.11, respectively) NPRS scores were all higher in the control group compared with the air arthrogram group (P < .001, P = .007, and P < .001, respectively). The administration of oral morphine equivalents (OMEs) during the PACU stay was significantly lower in the air arthrogram group, with a mean of 36.75 ± 11.37 OMEs, compared with 44.53 ± 16.06 OMEs in the control group (P = .023). There was no difference in postoperative nonopioid medications, such as ketorolac or acetaminophen, given between groups.ConclusionPatients undergoing hip arthroscopic surgery with air arthrography and venting used to aid distraction had significantly less postoperative pain and required a lower total dosage of opioids during their PACU stay when compared with patients who underwent hip arthroscopic surgery without air arthrography

DATA DRIVEN APPLICATION STORE LISTING OPTIMIZATION

A computing system (e.g., a cloud server that hosts an application store) may predict the effect of modifying marketing assets (e.g., text, an image, a screenshot, a description, a video, etc.) of an application (hereinafter referred to as an “app”) on acquisitions (e.g., installations) for the app. The computing system may generate these predictions based on historical performance data (e.g., data relating to modifications to one or more marketing assets of the app and to acquisitions for the app) of marketing assets for a variety of types (e.g., lifestyle apps, social media apps, utility apps, productivity apps, entertainment apps including games, etc.) of apps on the app store. In some examples, the historical performance data may include the origin country, language, device type, purchase history, acquisition history, and/or the like of potential customers (e.g., customers of the app) such that the predictions generated by the computing system may be based on one or more of those factors. The computing system may then provide these predictions to a user (e.g., an app developer) of the application store to help (e.g., by providing benchmarks and/or recommendations) the user modify the marketing assets of the user’s app in a manner predicted to increase acquisitions for the user’s app in the app store. For example, the computing system may cause the user’s computing device (e.g., a smartphone, a tablet, a laptop) to display a statistical model (e.g., a cat and whisker plot, a bar graph, etc.) indicating the relationship between modifying one or more marketing assets and acquisitions for any type of app. The computing system may also provide statistics such as the average acquisition, the range of acquisition, the distribution of acquisition, the standard deviation of acquisition, and/or the like. Such statistics may represent acquisition benchmarks for guiding the user in modifying the user’s marketing assets

DATA DRIVEN APPLICATION STORE LISTING OPTIMIZATION

A computing system (e.g., a cloud server that hosts an application store) may predict the effect of modifying marketing assets (e.g., text, an image, a screenshot, a description, a video, etc.) of an application (hereinafter referred to as an “app”) on acquisitions (e.g., installations) for the app. The computing system may generate these predictions based on historical performance data (e.g., data relating to modifications to one or more marketing assets of the app and to acquisitions for the app) of marketing assets for a variety of types (e.g., lifestyle apps, social media apps, utility apps, productivity apps, entertainment apps including games, etc.) of apps on the app store. In some examples, the historical performance data may include the origin country, language, device type, purchase history, acquisition history, and/or the like of potential customers (e.g., customers of the app) such that the predictions generated by the computing system may be based on one or more of those factors. The computing system may then provide these predictions to a user (e.g., an app developer) of the application store to help (e.g., by providing benchmarks and/or recommendations) the user modify the marketing assets of the user’s app in a manner predicted to increase acquisitions for the user’s app in the app store. For example, the computing system may cause the user’s computing device (e.g., a smartphone, a tablet, a laptop) to display a statistical model (e.g., a cat and whisker plot, a bar graph, etc.) indicating the relationship between modifying one or more marketing assets and acquisitions for any type of app. The computing system may also provide statistics such as the average acquisition, the range of acquisition, the distribution of acquisition, the standard deviation of acquisition, and/or the like. Such statistics may represent acquisition benchmarks for guiding the user in modifying the user’s marketing assets

CUSTOM APPLICATION STORE LISTING FOR ADVERTISEMENTS

A computing system (e.g., a cloud server) may cause a computing device to display a custom store listing (e.g., in accordance with a landing page optimization strategy) for an application (hereinafter referred to as “app”) on an app store. The computing system may generate an identifier (e.g., a code, a token, a parameter, etc.) for each custom store listing. The computing system may then combine the respective identifier for each custom store listing with a base link (e.g., a Uniform Resource Locator (URL)) for the application to create a custom store listing link for the custom store listing. As such, a customer may select one of the custom store listing links and be directed to a custom store listing that is customized to appeal to the customer (e.g., as part of a targeted digital advertising strategy or campaign). In this way, a user (e.g., an app developer) of the computing system may create or use custom store listings as part of digital advertising campaigns for the user’s app, which may increase acquisitions (e.g., downloads) for the user’s app

Role of Adaptor TrfA and ClpPC in Controlling Levels of SsrA-Tagged Proteins and Antitoxins in Staphylococcus aureus

Staphylococcus aureus responds to changing extracellular environments in part by adjusting its proteome through alterations of transcriptional priorities and selective degradation of the preexisting pool of proteins. In Bacillus subtilis, the proteolytic adaptor protein MecA has been shown to play a role in assisting with the proteolytic degradation of proteins involved in competence and the oxidative stress response. However, the targets of TrfA, the MecA homolog in S. aureus, have not been well characterized. In this work, we investigated how TrfA assists chaperones and proteases to regulate the proteolysis of several classes of proteins in S. aureus. By fusing the last 3 amino acids of the SsrA degradation tag to Venus, a rapidly folding yellow fluorescent protein, we obtained both fluorescence-based and Western blot assay-based evidence that TrfA and ClpCP are the adaptor and protease, respectively, responsible for the degradation of the SsrA-tagged protein in S. aureus. Notably, the impact of TrfA on degradation was most prominent during late log phase and early stationary phase, due in part to a combination of transcriptional regulation and proteolytic degradation of TrfA by ClpCP. We also characterized the temporal transcriptional regulation governing TrfA activity, wherein Spx, a redox-sensitive transcriptional regulator degraded by ClpXP, activates trfA transcription while repressing its own promoter. Finally, the scope of TrfA-mediated proteolysis was expanded by identifying TrfA as the adaptor that works with ClpCP to degrade antitoxins in S. aureus. Together, these results indicate that the adaptor TrfA adds temporal nuance to protein degradation by ClpCP in S. aureus

The Effect on Systemic and Pulmonary Circulations in the Resuscitation of Asphyxiated Newborn Piglets with 21%, 50% or 100% Oxygen

peer reviewe

Heavy bottom squark mass in the light gluino and light bottom squark scenario

Restrictive upper bounds on the heavy bottom squark mass when the gluino and one bottom squark are both light are based on the predicted reduction of $R_b$ (the fraction of $Z$ hadronic decays to $b \bar b$ pairs) in such a scenario. These bounds are found to be relaxed by the process $Z \to b\bar{\tilde b}{\tilde g}/{\bar b}{\tilde b}{\tilde g}$, which may partially compensate for the reduction of $R_b$. The relaxation of bounds on the top squark and the scale-dependence of the strong coupling constant are also discussed.Comment: 9 pages, LaTeX, 2 figures, to be submitted to Phys. Lett. B, more discussions adde