Swordfish bill injury involving abdomen and vertebral column: case report and review

<p>Abstract</p> <p>Background</p> <p>Penetrating injuries of the abdomen and spinal canal that involve organic material of animal origin are extremely rare and derive from domestic and wild animal attacks or fish attacks.</p> <p>Case presentation</p> <p>In this case report we present the unique, as far as the literature is concerned, unprovoked woman's injury to the abdomen by a swordfish. There are only four cases of swordfish attacks on humans in the literature - one resulted to thoracic trauma, two to head trauma and one to knee trauma, one of which was fatal - none of which were unprovoked. Three victims were professional or amateur fishermen whereas in the last reported case the victim was a bather as in our case. Our case is the only case where organic debris of animal's origin remained in the spinal canal after penetrating trauma.</p> <p>Conclusions</p> <p>Although much has been written about the management of penetrating abdominal and spinal cord trauma, controversy remains about the optimal management. Moreover, there is little experience in the management of patients with such spinal injuries, due to the fact that such cases are extremely rare. In this report we focus on the patient's treatment with regard to abdominal and spinal trauma and present a review of the literature.</p

Time-series clustering with jointly learning deep representations, clusters and temporal boundaries

Abstract Clustering and segmentation of temporal data is an important task across several fields, with prominent applications in computer vision and machine learning such as face and gesture segmentation. Several related methods have been proposed in literature, focusing on learning temporal boundaries and clusters, with recent works focusing on learning deep representations for clustering. However, none of the proposed methods is suitable for jointly learning segments, clusters, as well as representations. In this paper, we propose the first methodology that simultaneously discovers suitable deep representations, as well as clusters and temporal boundaries, with the clustering process providing supervisory cues for updating temporal boundaries and training the proposed deep learning architecture. We demonstrate the power of the proposed approach on a human motion segmentation task using the CMU-MMAC database. Our method provides the best results with respect to normalized mutual information compared to other clustering algorithms

Molecular engineering of sustainable phase-change solvents: From digital design to scaling-up for CO2 capture

Phase-change solvents promise reduced energetic and environmental footprints for separation systems, including absorption-based CO2 abatement technologies. The search for efficient phase-change solvents is limited by challenges in vapour-liquid–liquid equilibrium (VLLE) prediction and in sustainability assessment. We overcome these with a digital approach to screen billions of structures and design the novel phase-change solvent S1N (N1-cyclohexylpropane-1,3-diamine) and mixture S1N/DMCA (N,N-dimethylcyclohexylamine). Screening criteria include thermodynamic and process-related properties, reactivity and sustainability of solvent production and use. VLLE phase envelopes are predicted using the SAFT-γ Mie (Statistical Associating Fluid Theory) equation of state thanks to its transferability to any structure and the implicit modelling of ionic species. Experimental validation confirms the suitability of S1N/DMCA for scaling-up, with a cyclic capacity of 1.19 mol CO2/ kg-solvent, a regeneration energy of 2.3 GJ/ton-CO2, and vapour losses and viscosity lower by 10% and 70% than those of other solvents. S1N is also safer for plant operation and working personnel

The INTERSPEECH 2018 computational paralinguistics challenge:atypical &amp; self-assessed affect, crying &amp; heart beats

Abstract The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified; in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem; in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart; and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the ‘usual’ ComParE and BoAW features and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series

The INTERSPEECH 2017 computational paralinguistics challenge:addressee, cold &amp; snoring

Abstract The INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from ‘healthy’ speech; and in the Snoring sub-challenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audio-words for the first time in the challenge series