A Rare Complication of a Vaginal Breech Delivery

Rectal lesions without anal sphincter trauma in childbirth are only sporadically described in literature. We describe the case of a 29-year-old primigravida who delivered a child in frank breech presentation. During the second stage of labour a foot presented transanally through a rectal laceration with intact anal sphincters. The laceration was repaired immediately after delivery in theatre. Follow-up visits showed a properly cured laceration and no complaints of incontinence or foul discharge

Apomorphine-induced disruption of prepulse inhibition that can be normalised by systemic haloperidol is insensitive to clozapine pretreatment

Rationale: Prepulse inhibition (PPI) of startle refers to the phenomenon in which a weak prepulse attenuates the startle response to a succeeding intense stimulus. PPI can be disrupted by systemic apomorphine in animals, and reduced PPI has been consistently reported in schizophrenia patients. The ability of the atypical antipsychotic clozapine to reverse apomorphine-induced PPI deficit has been demonstrated in the rat, but has not yet been tested in the mouse. The present study was designed to fill this gap. Objective and results: We investigated the efficacy of clozapine in reversing apomorphine-induced (2.0 or 2.5mg/kg, SC) PPI deficit in C57BL6 mice. Clozapine failed to restore PPI disruption in apomorphine-treated mice in two independent laboratories across two dose ranges (1-3mg/kg, IP, or 3-30mg/kg, PO), whereas the typical antipsychotic haloperidol (1mg/kg,IP) completely normalised PPI performance. Conclusions: Unlike the rat, apomorphine-induced PPI disruption in mice might be instrumental in distinguishing between typical and atypical antipsychotic drugs. This also lends further support to the suggestion that the neuropharmacology of PPI is not identical in the two rodent specie

Selective leaching of copper and zinc from primary ores and secondary mineral residues using biogenic ammonia

With the number of easily accessible ores depleting, alternate primary and secondary sources are required to meet the increasing demand of economically important metals. Whilst highly abundant, these materials are of lower grade with respect to traditional ores, thus highly selective and sustainable metal extraction technologies are needed to reduce processing costs. Here, we investigated the metal leaching potential of biogenic ammonia produced by a ureolytic strain of Lysinibacillus sphaericus on eight primary and secondary materials, comprised of mining and metallurgical residues, sludges and automotive shredder residues (ASR). For the majority of materials, moderate to high yields (30–70%) and very high selectivity (>97% against iron) of copper and zinc were obtained with 1 mol L−1 total ammonia. Optimal leaching was achieved and further refined for the ASR in a two-step indirect leaching system with biogenic ammonia. Copper leaching was the result of local corrosion and differences in leaching against the synthetic (NH4)2CO3 control could be accounted for by pH shifts from microbial metabolism, subsequently altering free NH3 required for coordination. These results provide important findings for future sustainable metal recovery technologies from secondary materials.This work was conducted under the financial support of the Strategic Initiative Materials in Flanders (SIM) (SBO-SMART: Sustainable Metal Extraction from Tailings, grant no. HBC.2016.0456) and the European Union’s Horizon 2020 research and innovation programme, Metal Re-covery from Low-Grade Ores and Wastes Plus (METGROW+, grant no. 690088) . FV acknowledges support by the Flemish Agency for Inno-vation and Entrepreneurship (Vlaio) via a Baekeland PhD fellowship (HBC.2017.0224) and by the Research & Development Umicore Group. We would like to thank Pieter Ostermeyer and Karel Folens for assis-tance with thermodynamic modelling and CMET and ECOCHEM group members and SMART/METGROW+partners for valuable discussions throughout the projec

Taming our wild data: On intercoder reliability in discourse research

Many research questions in the field of applied linguistics are answered by manually analyzing data collections or corpora: collections of spoken, written and/or visual communicative messages. In this kind of quantitative content analysis, the coding of subjective language data often leads to disagreement among raters. In this paper, we discuss causes of and solutions to disagreement problems in the analysis of discourse. We discuss crucial factors determining the quality and outcome of corpus analyses, and focus on the sometimes tense relation between reliability and validity. We evaluate formal assessments of intercoder reliability. We suggest a number of ways to improve the intercoder reliability, such as the precise specification of the variables and their coding categories and carving up the coding process into smaller substeps. The paper ends with a reflection on challenges for future work in discourse analysis, with special attention to big data and multimodal discourse

Bioleaching of metals from secondary materials using glycolipid biosurfactants

With the global demand for economically important metals increasing, compounded by the depletion of readily accessible ores, secondary resources and low-grade ores are being targeted to meet growing demands. Novel technologies developed within biobased industries, such as microbial biosurfactants, could be implemented to improve the sustainability of traditional hydrometallurgy techniques. This study investigates newly developed microbial biosurfactants (acidic- and bolaform glycolipids) for the leaching of metals (particularly Cu and Zn) from a suite of mine tailings, metallurgical sludges and automotive shredder residues. Generally, acidic sophorolipids were the most performant, and optimal Cu leaching was observed from a fayalite slag (27%) and a copper sulfide mine tailing (53%). Further investigation of the leached fayalite material showed that leaching was occurring from small metallic Cu droplets in this material via a corrosion-based mechanism, and/or from Cu-Pb sulfides, selective against dominant Fe-silicate matrices. This study highlights that acidic sophorolipid microbial biosurfactants have the potential to leach Cu and Zn from low-grade secondary materials. It also provides important fundamental insights into biosurfactant-metal and mineral interactions that are currently unexplored. Together, the convergence of leaching and mining industries with bio-industries can improve material recovery and will positively impact the bio- and circular economies and the environment.The authors thank Bio Base Europe Pilot plant for supplying the biosurfactants that enabled the execution of the leaching experiments. We also thank Joachim Neri, Karel Folens, Nina Ricci Nicomel and Melgü Kizilmese for their assistance during ICP-analyses

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases