ObjectivesHospital pharmacies provide centralised intravenous additive services (CIVAS), such as antibiotic reconstitution. The aim of this study was to demonstrate the physicochemical stability of high-concentration cefuroxime sodium in aqueous injections, which is mandatory for the centralised preparation of products with automation.MethodsThe physicochemical stability of three high-concentration injections (1.5 g of cefuroxime sodium in 15 mL, 16 mL and 18 mL of water for injection (WFI)) were studied in two primary packing materials (glass vials and polypropylene syringes). The samples were reconstituted with automation in three mid-sized hospital pharmacies in a good manufacturing practice (GMP) grade A/B cleanroom. During the study, the samples were stored in refrigerated conditions (4°C) and 1.5 g/15 mL solution in ambient temperature (22°C). Cefuroxime and descarbamoyl cefuroxime were analysed by high-performance liquid chromatography with UV detection. In addition, the appearance, pH and uniformity of dosage units were investigated.ResultsThe freshly prepared cefuroxime injections fulfilled the criteria of content uniformity (acceptance value (AV) <15). A significant decrease in concentration of cefuroxime and increase in content of descarbamoyl cefuroxime was observed in all injections. Cefuroxime aqueous injections were physiochemically stable for up to 14 days under refrigeration storage. The relative content of descarbamoyl cefuroxime remained under 3% at 4°C. The solution of 1.5 g/15 mL was stable for only 20 hours in formulations stored for the first 14 days at 4°C and then transferred to 22°C. The colour of the solution changed from light yellow to a darker yellow, and the pH value of the solutions increased during storage. Neither primary packing materials, commercial source of cefuroxime sodium nor exposure to light had any significant effect on the stability of formulations.ConclusionsAlthough limited, we found the shelf life of high-concentration cefuroxime injections in refrigerated conditions sufficient for centralised antibiotic preparation in hospital pharmacy with automation. The limited shelf life of high-concentration cefuroxime injections must be considered when using these formulations.

Alcohol use disorder (AUD) is a global health problem with limited therapeutic options. The biochemical mechanisms that lead to this disorder are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD. The plasma metabolome contains a plethora of bioactive molecules that reflects the functional changes in host metabolism but also the impact of the gut microbiome and nutritional habits. In this study, we investigated the impact of severe AUD (sAUD), and of a 3-week period of alcohol abstinence, on the blood metabolome (non-targeted LC-MS metabolomics analysis) in 96 sAUD patients hospitalized for alcohol withdrawal. We found that the plasma levels of different lipids ((lyso)phosphatidylcholines, long-chain fatty acids), short-chain fatty acids (i.e. 3-hydroxyvaleric acid) and bile acids were altered in sAUD patients. In addition, several microbial metabolites, including indole-3-propionic acid, p-cresol sulfate, hippuric acid, pyrocatechol sulfate, and metabolites belonging to xanthine class (paraxanthine, theobromine and theophylline) were sensitive to alcohol exposure and alcohol withdrawal. 3-Hydroxyvaleric acid, caffeine metabolites (theobromine, paraxanthine, and theophylline) and microbial metabolites (hippuric acid and pyrocatechol sulfate) were correlated with anxiety, depression and alcohol craving. Metabolomics analysis in postmortem samples of frontal cortex and cerebrospinal fluid of those consuming a high level of alcohol revealed that those metabolites can be found also in brain tissue. Our data allow the identification of neuroactive metabolites, from interactions between food components and microbiota, which may represent new targets arising in the management of neuropsychiatric diseases such as sAUD. Gut2Behave project was initiated from ERA-NET NEURON network (Joint Transnational Call 2019) and was financed by Academy of Finland, French National Research Agency (ANR-19-NEUR-0003-03) and the Fonds de la Recherche Scientifique (FRS-FNRS; PINT-MULTI R.8013.19, Belgium). Metabolomics analysis of the TSDS samples was supported by grant from the Finnish Foundation for Alcohol Studies.

ABSTRACT Mourning the murder of Nahel Merzouk by the French police and the drowning of 750 human beings by the Greek coastguard’s recklessness, we argue that the erosion of human rights at the EU’s external borders is being co-constituted by an analogous erosion within its internal borders. To establish this connection, we re-conceptualise the EUropean b/ordering regime as a network of fractal borderscapes characterised by a selective criminalisation of mobility that reproduces longstanding racist prejudice along socio-spatial borderlines. We argue that the proliferation of these spaces of exception is degenerating into a state of exception: a polity’s self-destructive tendency to replace the rule of law for legalised despotism. Such autoimmune reaction, we claim, is rooted in borderism: a spectacle that misshapes geography into spurious antagonisms epitomised by national and civilisational dichotomies (“friends” and “foes”), thus creating a hyperreality that breeds racism, spatialises apartheid and exacerbates animosity.

Indoor motion planning challenges researchers because of the high density and unpredictability of moving obstacles. Classical algorithms work well in the case of static obstacles but suffer from collisions in the case of dense and dynamic obstacles. Recent reinforcement learning (RL) algorithms provide safe solutions for multiagent robotic motion planning systems. However, these algorithms face challenges in convergence: slow convergence speed and suboptimal converged result. Inspired by RL and representation learning, we introduced the ALN-DSAC: a hybrid motion planning algorithm where attention-based long short-term memory (LSTM) and novel data replay combine with discrete soft actor-critic (SAC). First, we implemented a discrete SAC algorithm, which is the SAC in the setting of discrete action space. Second, we optimized existing distance-based LSTM encoding by attention-based encoding to improve the data quality. Third, we introduced a novel data replay method by combining the online learning and offline learning to improve the efficacy of data replay. The convergence of our ALN-DSAC outperforms that of the trainable state of the arts. Evaluations demonstrate that our algorithm achieves nearly 100% success with less time to reach the goal in motion planning tasks when compared to the state of the arts. The test code is available at https://github.com/CHUENGMINCHOU/ALN-DSAC.

In practice, some optimization problems require expensive calculation and exhibit multimodal characteristics simultaneously. These problems are called high-dimensional expensive multimodal optimization problems. When addressing such problems, existing surrogate-assisted evolutionary algorithms (SAEAs) encounter the “curse of dimensionality," which severely affects their capability to search optimal solutions. Therefore, this study proposed a surrogate and autoencoder-assisted multitask particle swarm optimization algorithm. First, an autoencoder-embedded multitask evolutionary framework was established to transform a high-dimensional multimodal optimization problem into multiple low-dimensional subproblems or subtasks. Further, a multi-level surrogate model management mechanism combining mirror learning was proposed. An appropriate local surrogate model can be rapidly generated for each modality of the problem. Moreover, a dual-mode local exploitation strategy was developed to improve the capability of swarm to exploit each subtask. The proposed algorithm was compared with seven existing SAEAs on 33 benchmark functions and the aeroengine aerodynamic design optimization problem. Experimental results revealed that the proposed algorithm can obtain multiple highly competitive optimal solutions, including global optimal solutions.

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and-wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero echo time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole-brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

Abstract This study relies on the constrained communication framework to compare the use of embedded inversion in English as a Foreign Language (EFL) and English as a Second Language (ESL). It is based on several (sub)corpora of EFL and ESL, but also reference corpora of native English, which differ along the constraint dimensions of language activation (monolingual/bilingual), proficiency (native users/proficient L2 users/learners), and modality (speech/writing). In addition to these constraint dimensions, we also investigate the possible effect of linguistic factors that have been claimed to play a role in the use of embedded inversion. A multifactorial analysis comparing embedded inversion with standard-like indirect questions, supplemented by a close examination of the patterns of use of embedded inversion, reveals both shared and distinctive features across EFL and ESL. It also highlights the importance of linguistic factors and variety/L1, and their interaction with communicative constraints.

Clinical risk scores for coronary artery disease (CAD) are used in clinical practice to select patients for diagnostic testing and therapy. Several studies have proposed that polygenic risk scores (PRSs) can improve the prediction of CAD, but the scores need to be validated in clinical populations with accurately characterized phenotypes. We assessed the predictive power of the three most promising PRSs for the prediction of coronary atherosclerosis and obstructive CAD. This study was conducted on 943 symptomatic patients with suspected CAD for whom the phenotype was accurately characterized using anatomic and functional imaging. Previously published genome-wide polygenic scores were generated to compare a genetic model based on PRSs with a model based on clinical data. The test and PRS cohorts were predominantly Caucasian of northern European ancestry. All three PRSs predicted coronary atherosclerosis and obstructive CAD statistically significantly. The predictive accuracy of the models combining clinical data and different PRSs varied between 0.778 and 0.805 in terms of the area under the receiver operating characteristic (AUROC), being close to the model including only clinical variables (AUROC 0.769). The difference between the clinical model and combined clinical + PRS model was not significant for PRS1 (p=0.627) and PRS3 (p=0.061). Only PRS2 slightly improved the predictive power of the model (p=0.04). The likelihood ratios showed the very weak diagnostic power of all PRSs. The addition of PRSs to conventional risk factors did not clinically significantly improve the predictive accuracy for either coronary atherosclerosis or obstructive CAD, showing that current PRSs are not justified for routine clinical use in CAD.