Transfer of heavy metals in the food chain earthworm Black-tailed godwit (Limosa limosa): comparison of a polluted and a reference site in The Netherlands

The Black-tailed godwit (Limosa limosa) is a migratory wader that favours wet meadows for breeding. The species has a Red List status in The Netherlands, as it strongly declined in numbers since the 1960s. Intensification of agriculture and land use change resulting in habitat loss are considered major causes of this decline. In some areas the breeding habitat is contaminated with heavy metals. Adult godwits mainly feed on earthworms in the breeding season, which are known to accumulate heavy metals from the soil. In this paper we investigate the transfer of heavy metals from the soil to the Black-tailed godwit, which may have an additive negative effect on the viability of local populations. We measured heavy metal concentrations in soil, earthworms, and godwit eggs and feathers at a polluted and a reference site. The results suggest that Lead, Mercury and Cadmium are transferred from the soil to godwits even though the species spends only a few months in the breeding area during the year.     

WS2 nanobuds as a new hybrid nanomaterial

We report on the first inorganic nanobuds: WS2 nanotubes decorated with fullerene-like particles. They were synthesized by sulfurization of W5O14 nanowires. The fullerene-like particles nucleate in surface corrugations of the nanowires and grow by a diffusion process simultaneously with the transformation of nanowires into hollow multiwall nanotubes. Electron microscopy data are correlated with details of the transformation process revealing the possible mechanism of the formation of these new complex nanomaterials.     

Effect of intermittent compression of upper arm veins on forearm vessels in patients with end-stage renal disease

Native arteriovenous fistula is the best vascular access for chronic hemodialysis. Primary and long-term success depends, in part, on the state of arteries and veins at the time of the operation. The aim of our study was to investigate the effects of intermittent compression of upper arm veins on forearm vessels in patients with terminal renal disease. The study group was composed of 16 chronic hemodialysis patients who performed daily intermittent compression of the upper arm without vascular access by elastic band (Eschmarch). Ten chronic hemodialysis patients were included in the control group, which performed no specific activity. Forearm measurements were obtained at the beginning of the study and 4 and 8 weeks later during the course of intermittent compression of the upper arm veins. The forearm circumference and maximal handgrip strength were measured. The artery measures, including endothelium-dependent vasodilatation and forearm vein variables, were obtained by ultrasonography measurements. The forearm circumference, maximal handgrip strength, and artery variables, including endothelium-dependent vasodilatation, remained unchanged. The basal venous diameters (2.29 +/- 0.19 mm at the beginning, 2.46 +/- 0.19 mm after 4 weeks, and 2.53 +/- 0.18 mm after 8 weeks) were significantly increased in the study group. The distensibility of veins was preserved in the study group. There were no significant changes in the control group. Our study demonstrated that daily intermittent compression of the upper arm veins increases the forearm vein diameter and preserves the distensibility of veins in patients with end-stage renal failure.     

A New Sensor for Determination of Anionic Surfactants in Detergent Products with Carbon Nanotubes as Solid Contact

A new solid‐state sensor for potentiometric determination of surfactants with a layer of multi‐walled carbon nanotubes was prepared. As a sensing material, 1,3‐didecyl‐2‐methylimidazolium–tetraphenylborate ion‐pair was used. The investigated sensor showed a Nernstian response for both dodecylbenzenesulphonate (DBS, 57.6 mV/decade of activity between 5 × 10^?7 to 1 × 10^?3 M) and sodium lauryl sulfate (LS, 58.4 mV/decade of activity between 2 × 10^?7 to 2 × 10^?3 M). It responded in 8–10 s for each ten‐fold concentration change in the range of 1 × 10^?6 to 3 × 10^?3 M. The detection limits for DS and DBS were 2 × 10^?7 and 3 × 10^?7 M, respectively. The sensor revealed a stable response (signal drift 2.6 mV/h) and exhibited satisfactory selectivity performances for LS over most of the anions generally used in surfactant‐based commercial detergents. The main application of this sensor was the end‐point determination in potentiometric titrations of anionic surfactants. The (diisobutyl phenoxy ethoxy ethyl)dimethyl benzyl ammonium chloride (Hyamine), cetyltrimethylammonium bromide, hexadecylpyridinium chloride monohydrate (HDPC) and 1,3‐didecyl‐2‐methylimidazolium chloride were tested as potential cationic titrants, and all exhibited analytically usable titration curves with well‐defined equivalence points. The standard solution of HDPC was used as a cationic titrant by all potentiometric titrations. The operational life‐time of the sensor described was prolonged to more than 3 months.     

Performance Improvement of Two-Hop Relay System Using Polarization Diversity

With growing demands for mobile data, providing reliable communication with high quality of service has become a critical issue. Wireless relaying has been recognized as an effective solution to enhance system coverage, data rate, reliability and energy efficiency by introducing additional network node-relay. However, complex wireless channel characteristics, such as fading, path loss, noise and interference limits the performance of such systems in a manner that it is dominated by the worst channel. That is why additional techniques are necessary to be implemented in order to improve the characteristics of this bottleneck link, and performance of relay system as whole. Multi-antenna relaying has emerged as a promising technique to overcome bottleneck effect in relay system and provide performance enhancements, but the small size of network nodes limits their use at wireless terminals. In this paper we propose the implementation of polarization diversity, where one compact dual-polarized antenna is used instead of two adequately separated antennas. Maximal ratio combining of diversity signals in amplify-and-forward relaying systems applying variable gain is analysed. Mixed Rayleigh/Ricean fading environment is assumed, while two diversity signals are described as two correlated and non-identical Rayleigh fading channels. Novel analytical model for determining the performance of the analysed relay system expressed through the system’s outage and bit error rate is derived. Excellent match between Monte-Carlo simulations and numerical results confirms the validity of the proposed analytical model. Furthermore, the presented results illustrate the effect of various channels parameters on the system performance, such as Ricean K factor, average signal to noise ratios, diversity signals’ correlation and cross-polar discrimination. For a given performance requirements, the proposed solution can significantly improve relay system performance.     

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.     

Behaviour-based control: examples from navigation,learning, and group behaviour

This paper describes the main properties of behaviour-based approaches to control. Different approaches to designing and using behaviours as basic units for control, representation, and learning are illustrated on three empirical examples of robots performing navigation and path-finding, group behaviours, and learning behaviour selection.     

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of ¹H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in‐depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.     

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Deep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be accurate by standard evaluation metrics and benchmarks, a surge of work has recently exposed the demographic bias that such algorithms exhibit–highlighting that accuracy does not entail fairness. Clearly, deploying biased systems under real-world settings can have grave consequences for affected populations. Indeed, learning methods are prone to inheriting, or even amplifying the bias present in a training set, manifested by uneven representation across demographic groups. In facial datasets, this particularly relates to attributes such as skin tone, gender, and age. In this work, we address the problem of mitigating bias in facial datasets by data augmentation. We propose a multi-attribute framework that can successfully transfer complex, multi-scale facial patterns even if these belong to underrepresented groups in the training set. This is achieved by relaxing the rigid dependence on a single attribute label, and further introducing a tensor-based mixing structure that captures multiplicative interactions between attributes in a multilinear fashion. We evaluate our method with an extensive set of qualitative and quantitative experiments on several datasets, with rigorous comparisons to state-of-the-art methods. We find that the proposed framework can successfully mitigate dataset bias, as evinced by extensive evaluations on established diversity metrics, while significantly improving fairness metrics such as equality of opportunity.

     

Fully automatic recognition of the temporal phases of facial actions

Past work on automatic analysis of facial expressions has focused mostly on detecting prototypic expressions of basic emotions like happiness and anger. The method proposed here enables the detection of a much larger range of facial behavior by recognizing facial muscle actions [action units (AUs)] that compound expressions. AUs are agnostic, leaving the inference about conveyed intent to higher order decision making (e.g., emotion recognition). The proposed fully automatic method not only allows the recognition of 22 AUs but also explicitly models their temporal characteristics (i.e., sequences of temporal segments: neutral, onset, apex, and offset). To do so, it uses a facial point detector based on Gabor-feature-based boosted classifiers to automatically localize 20 facial fiducial points. These points are tracked through a sequence of images using a method called particle filtering with factorized likelihoods. To encode AUs and their temporal activation models based on the tracking data, it applies a combination of GentleBoost, support vector machines, and hidden Markov models. We attain an average AU recognition rate of 95.3% when tested on a benchmark set of deliberately displayed facial expressions and 72% when tested on spontaneous expressions.