Dynamic behaviour of brain and surrogate materials under ballistic impact

In the last several decades the number of the fatalities related to criminally inflicted cranial gunshot wounds has increased (Aarabi et al.; Jena et al., 2014; Mota et al., 2003). Back-spattered bloodstain patterns are often important in investigations of cranial gunshot fatalities, particularly when there is a doubt whether the death is suicide or homicide. Back-spatter is the projection of blood and tissue back toward the firearm. However, the mechanism of creation of the backspatter is not understood well. There are several hypotheses, which describe the formation of the backspatter. However, as it is difficult to study the internal mechanics of formation of the backspatter in animal experiments as the head is opaque and sample properties vary from animal to animal. Performing ballistic experiments on human cadavers is rarely not possible for ethical reasons. An alternative is to build a realistic physical 3D model of the human head, which can be used for reconstruction of crime scenes and BPA training purposes. This requires a simulant material for each layer of the human head. In order to build a realistic model of human head, it is necessary to understand the effect of the each layer of the human head to the generation of the back-spatter. Simulant materials offer the possibility of safe, well‐controlled experiments. Suitable simulants must be biologically inert, be stable over some reasonable shelf‐life, and respond to ballistic penetration in the same way as the responding human tissues. Traditionally 10-20% (w/w) gelatine have been used as a simulant for human soft tissues in ballistic experiments. However, 10-20% of gelatine has never been validated as a brain simulant. Moreover, due to the viscoelastic nature of the brain it is not possible to find the exact mechanical properties of the brain at ballistic strain rates. Therefore, in this study several experiments were designed to obtain qualitative and quantitative data using high speed cameras to compare different concentrations of gelatine and new composite material with the bovine and ovine brains. Factors such as the form of the fragmentation, velocity of the ejected material, expansion rate, stopping distance, absorption of kinetic energy and effect of the suction as well as ejection of the air from the wound cavity and its involvement in the generation of the backspatter have been investigated. Furthermore, in this study a new composite material has been developed, which is able to create more realistic form of the fragmentation and expansion rate compared to the all different percentage of the gelatine. The results of this study suggested that none of the concentrations the gelatine used in this study were capable of recreating the form of the damage to the one observed from bovine and ovine brain. The elastic response of the brain tissue is much lower that observed in gelatine samples. None of the simulants reproduced the stopping distance or form of the damage seen in bovine brain. Suction and ejection of the air as a result of creation of the temporary cavity has a direct relation to the elasticity of the material. For example, by reducing the percentage of the gelatine the velocity of the air drawn into the cavity increases however, the reverse scenario can be seen for the ejection of the air. This study showed that elastic response of the brain tissue was not enough to eject the brain and biological materials out of the cranium. However, the intracranial pressure raises as the projectile passes through the head. This pressure has the potential of ejecting the brain and biological material backward and create back-spatter. Finally, the results of this study suggested that for each specific type of experiment, a unique simulant must be designed to meet the requirements for that particular experiment

Proceedings of the Linux Audio Conference 2018

These proceedings contain all papers presented at the Linux Audio Conference 2018. The conference took place at c-base, Berlin, from June 7th - 10th, 2018 and was organized in cooperation with the Electronic Music Studio at TU Berlin

Nanotechnology and the Treatment of HIV Infection

Suboptimal adherence, toxicity, drug resistance and viral reservoirs make the lifelong treatment of HIV infection challenging. The emerging field of nanotechnology may play an important role in addressing these challenges by creating drugs that possess pharmacological advantages arising out of unique phenomena that occur at the “nano” scale. At these dimensions, particles have physicochemical properties that are distinct from those of bulk materials or single molecules or atoms. In this review, basic concepts and terms in nanotechnology are defined, and examples are provided of how nanopharmaceuticals such as nanocrystals, nanocapsules, nanoparticles, solid lipid nanoparticles, nanocarriers, micelles, liposomes and dendrimers have been investigated as potential anti-HIV therapies. Such drugs may, for example, be used to optimize the pharmacological characteristics of known antiretrovirals, deliver anti-HIV nucleic acids into infected cells or achieve targeted delivery of antivirals to the immune system, brain or latent reservoirs. Also, nanopharmaceuticals themselves may possess anti-HIV activity. However several hurdles remain, including toxicity, unwanted biological interactions and the difficulty and cost of large-scale synthesis of nanopharmaceuticals

Probabilistic spatial filter estimation for signal enhancement in multi-channel automatic speech recognition

Speech recognition in multi-channel environments requires target speaker localization, multi-channel signal enhancement and robust speech recognition. We here propose a system that addresses these problems: Localization is performed with a recently introduced probabilistic localization method that is based on support-vector machine learning of GCC-PHAT weights and that estimates a spatial source probability map. The main contribution of the present work is the introduction of a probabilistic approach to (re-)estimation of location-specific steering vectors based on weighting of observed inter-channel phase differences with the spatial source probability map derived in the localization step. Subsequent speech recognition is carried out with a DNN-HMM system using amplitude modulation filter bank (AMFB) acoustic features which are robust to spectral distortions introduced during spatial filtering. The system has been evaluated on the CHIME-3 multi-channel ASR dataset. Recognition was carried out with and without probabilistic steering vector re-estimation and with MVDR and delay-and-sum beamforming, respectively. Results indicate that the system attains on real-world evaluation data a relative improvement of 31.98% over the baseline and of 21.44% over a modified baseline. We note that this improvement is achieved without exploiting oracle knowledge about speech/non-speech intervals for noise covariance estimation (which is, however, assumed for baseline processing)

Spatial speech detection for binaural hearing aids using deep phoneme classifiers

Current hearing aids are limited with respect to speech-specific optimization for spatial sound sources to perform speech enhancement. In this study, we therefore propose an approach for spatial detection of speech based on sound source localization and blind optimization of speech enhancement for binaural hearing aids. We have combined an estimator for the direction of arrival (DOA), featuring high spatial resolution but no specialization to speech, with a measure of speech quality with low spatial resolution obtained after directional filtering. The DOA estimator provides spatial sound source probability in the frontal horizontal plane. The measure of speech quality is based on phoneme representations obtained from a deep neural network, which is part of a hybrid automatic speech recognition (ASR) system. Three ASR-based speech quality measures (ASQM) are explored: entropy, mean temporal distance (M-Measure), matched phoneme (MaP) filtering. We tested the approach in four acoustic scenes with one speaker and either a localized or a diffuse noise source at various signal-to-noise ratios (SNR) in anechoic or reverberant conditions. The effects of incorrect spatial filtering and noise were analyzed. We show that two of the three ASQMs (M-Measure, MaP filtering) are suited to reliably identify the speech target in different conditions. The system is not adapted to the environment and does not require a-priori information about the acoustic scene or a reference signal to estimate the quality of the enhanced speech signal. Nevertheless, our approach performs well in all acoustic scenes tested and varying SNRs and reliably detects incorrect spatial filtering angles

A Binaural Steering Beamformer System for Enhancing a Moving Speech Source

In many daily life communication situations, several sound sources are simultaneously active. While normal-hearing listeners can easily distinguish the target sound source from interfering sound sources—as long as target and interferers are spatially or spectrally separated—and concentrate on the target, hearing-impaired listeners and cochlear implant users have difficulties in making such a distinction. In this article, we propose a binaural approach composed of a spatial filter controlled by a direction-of-arrival estimator to track and enhance a moving target sound. This approach was implemented on a real-time signal processing platform enabling experiments with test subjects in situ. To evaluate the proposed method, a data set of sound signals with a single moving sound source in an anechoic diffuse noise environment was generated using virtual acoustics. The proposed steering method was compared with a fixed (nonsteering) method that enhances sound from the frontal direction in an objective evaluation and subjective experiments using this database. In both cases, the obtained results indicated a significant improvement in speech intelligibility and quality compared with the unprocessed signal. Furthermore, the proposed method outperformed the nonsteering method

The impact of anti-eosinophilic therapy on exercise capacity and inspiratory muscle strength in patients with severe asthma

Introduction Exercise limitation is frequently described among asthmatic patients and could be related to different mechanisms of the pulmonary, cardiovascular and muscular systems. Despite this, cardiopulmonary exercise testing (CPET) does not have an established role in the management of severe asthma. The aim of our study was to investigate the role of CPET and inspiratory pressure measurement in exercise capacity and muscle strength in severe asthmatic patients treated with anti-IL-5 therapy. Methods A monocentric observational study was conducted at Hanover Medical School, Germany, from April 2018 to June 2019. Patients affected by severe asthma treated with either mepolizumab or benralizumab were included. All patients underwent CPET before the initiation of antibody therapy and after 3 months, and follow-up visits were scheduled at 3, 6 and 12 months with plethysmography, inspiratory pressure measurement and blood gas analysis. Results 14 patients were enrolled: 10 (71.4%) females, median age 52 years (IQR 47–61). Seven patients were treated with benralizumab, seven with mepolizumab. Oxygen uptake (V′O2 peak) did not change significantly after 3 months of antibody treatment, while the mean value of the breathing reserve exhaustion reduced significantly from 78% to 60% (p=0.004). Whereas at baseline seven patients depleted the breathing reserve and two of them experienced oxygen desaturation during exercise, at 3 months no one presented any desaturation or breathing reserve exhaustion. The inspiratory pressure remained unchanged before and after the antibody therapy. Conclusion CPET could show hints of alveolar recruitment and ventilatory efficiency in severe asthma patients treated with antibody therapy