Protein synthesis-dependent formation of protein kinase Mζ in long-term potentiation

The maintenance of long-term potentiation (LTP) in the CA1 region of the hippocampus has been reported to require both a persistent increase in phosphorylation and the synthesis of new proteins. The increased activity of protein kinase C (PKC) during the maintenance phase of LTP may result from the formation of PKMζ, the constitutively active fragment of a specific PKC isozyme. To define the relationship among PKMζ, longterm EPSP responses, and the requirement for new protein synthesis, we examined the regulation of PKMζ after subthreshold stimulation that produced short-term potentiation (STP) and after suprathreshold stimulation by single and multiple tetanic trains that produced LTP. We found that, although no persistent increase in PKMζ followed STP, the degree of long-term EPSP potentiation was linearly correlated with the increase of PKMζ. The increase was first observed 10 min after a tetanus that induced LTP and lasted for at least 2 hr, in parallel with the persistence of EPSP enhancement. Both the maintenance of LTP and the long-term increase in PKMζ were blocked by the protein synthesis inhibitors anisomycin and cycloheximide. These results suggest that PKMζ is a component of a protein synthesis-dependent mechanism for persistent phosphorylation in LTP

Evaluation of comorbidity measures for predicting mortality and revision surgery after elective primary shoulder replacement surgery based on data from the National Joint Registry and Hospital Episode Statistics for England: population based cohort study

Objective: To determine the importance of comorbidity measures when predicting mortality and revision surgery after elective primary shoulder replacement surgery. Design: Population based cohort study. Setting: Linked data from the National Joint Registry and NHS Hospital Episode Statistics were used to identify all elective primary shoulder replacements in England, 6 January 2012 to 30 March 2022. Participants: 37 176 consenting patients, aged 18-100 years, who had elective primary shoulder replacement surgery. Main outcome measures: Risk of mortality at 90 and 365 days, and risk of long term revision surgery after the primary surgery. Results: 37 176 primary shoulder replacement procedures were included; 102 patients died within 90 days and 445 within 365 days of the primary surgery. 1219 patients had revision surgery over a maximum follow-up period of >10 years. The addition of comorbidity measures derived from Hospital Episode Statistics (Charlson comorbidity index with summary hospital mortality index weights, Elixhauser comorbidity index, and hospital frailty risk score) to simpler models resulted in little improvement in predictive performance. Optimism adjusted performance (C index) of the models that included age, sex, American Society of Anesthesiologists (ASA) grade, and main surgical indication was 0.76 (95% confidence interval (CI) 0.72 to 0.81) for 90 day mortality, 0.74 (0.71 to 0.76) for 365 day mortality, and 0.64 (0.63 to 0.66) for revision surgery. The best performing models that included a comorbidity measure had an optimism adjusted C index of 0.77 (95% CI 0.73 to 0.81) for 90 day mortality, 0.76 (0.74 to 0.78) for 365 day mortality, and 0.65 (0.63 to 0.66) for revision surgery. Heterogeneity in model performance across regions of England was low, and decision curve analysis showed minimal improvement in net benefit when including comorbidity measures. Conclusions: In this study, patient comorbidity scores added little improvement to simpler models that included age, sex, ASA grade, and main surgical indication for predicting mortality and revision surgery after elective primary shoulder replacement surgery. This improvement needs to be balanced against the additional challenges of routine data linkage to obtain these scores

Risk of serious adverse events after primary shoulder replacement: development and external validation of a prediction model using linked national data from England and Denmark

Background Despite a rising rate of serious medical complications after shoulder replacement surgery, there are no prediction models in widespread use to guide surgeons in identifying patients at high risk and to provide patients with personalised risk estimates to support shared decision making. Our aim was to develop and externally validate a prediction model for serious adverse events within 90 days of primary shoulder replacement surgery. Methods Linked data from the National Joint Registry, National Health Service Hospital Episode Statistics Admitted Patient Care of England, and Civil Registration Mortality databases and Danish Shoulder Arthroplasty Registry and National Patient Register were used for our modelling study. Patients aged 18–100 years who had a primary shoulder replacement between April 1, 2012, and Oct 2, 2020, in England, and April 1, 2012, and Oct 2, 2018, in Denmark, were included. We developed a multivariable logistic regression model using the English dataset to predict the risk of 90-day serious adverse events, which were defined as medical complications requiring admission to hospital and all-cause death. We undertook internal validation using bootstrapping, and internal–external cross-validation across different geographical regions of England. The English model was externally validated on the Danish dataset. Findings Data for 40 631 patients undergoing primary shoulder replacement (mean age 72·5 years [SD 9·9]; 28 709 [70·7%] women and 11 922 [29·3%] men) were used for model development, of whom 2270 (5·6%) had a 90-day serious adverse event. On internal validation, the model had a C-statistic of 0·717 (95% CI 0·707–0·728) and was well calibrated. Internal–external cross-validation showed consistent model performance across all regions in England. Upon external validation on the Danish dataset (n=6653; mean age 70·5 years [SD 10·3]; 4503 [67·7%] women and 2150 [32·3%] men), the model had a C-statistic of 0·750 (95% CI 0·723–0·776). Decision curve analysis showed clinical utility, with net benefit across all risk thresholds. Interpretation This externally validated prediction model uses commonly available clinical variables to accurately predict the risk of serious medical complications after primary shoulder replacement surgery. The model is generalisable and applicable to most patients in need of a shoulder replacement. Its use offers support to clinicians and could inform and empower patients in the shared decision-making process. Funding National Institute for Health and Care Research and the Department of Orthopaedic Surgery, Herlev and Gentofte Hospital, Denmark

Mania- and anxiety-like behavior and impaired maternal care in female diacylglycerol kinase eta and iota double knockout mice

Genome-wide association studies linked diacylglycerol kinase eta and iota to mood disorders, including bipolar disorder and schizophrenia, and both genes are expressed throughout the brain. Here, we generated and behaviorally characterized female mice lacking Dgkh alone, Dgki alone, and double Dgkh/Dgki-knockout (dKO) mice. We found that fewer than 30% of newborn pups raised by dKO females survived to weaning, while over 85% of pups survived to weaning when raised by wild-type (WT) females. Poor survival under the care of dKO mothers was unrelated to pup genotype. Moreover, pups from dKO dams survived when fostered by WT dams, suggesting the poor survival rate of dKO-raised litters was related to impaired maternal care by dKO dams. Nest building was similar between WT and dKO dams; however, some dKO females failed to retrieve any pups in a retrieval assay. Pups raised by dKO dams had smaller or absent milk spots and reduced weight, indicative of impaired nursing. Unlike WT females, postpartum dKO females showed erratic, panicked responses to cage disturbances. Virgin dKO females showed behavioral signs of anxiety and mania, which were not seen in mice lacking either Dgkh or Dgki alone. Our research indicates that combined deletion of Dgkh and Dgki impairs maternal behavior in the early postpartum period, and suggests female dKO mice model symptoms of mania and anxiety

Deep execution monitor for robot assistive tasks

We consider a novel approach to high-level robot task execution for a robot assistive task. In this work we explore the problem of learning to predict the next subtask by introducing a deep model for both sequencing goals and for visually evaluating the state of a task. We show that deep learning for monitoring robot tasks execution very well supports the interconnection between task-level planning and robot operations. These solutions can also cope with the natural non-determinism of the execution monitor. We show that a deep execution monitor leverages robot performance. We measure the improvement taking into account some robot helping tasks performed at a warehouse

Optimizing Mixing in the Banbury Mixer with Synchronous Technology (ST) Rotors

Abstract This presentation is concerned with the mixing performance of the new ST™ rotor design in Banbury® mixers operating at even speed. Rotor orientation was used to optimize machine performance in mixing a standard one-step rubber formulation, in terms of productivity, energy consumption, and product quality. Experimental data is presented on mixer discharge temperature, Mooney viscosity, and maximum rheometer torque, and their standard deviation. The effect of rotor orientation on these parameters is discussed, and optimal rotor configurations are identified.</jats:p