Natural Vitamin E Supplementation during Pregnancy in Rats Increases RRR-α-Tocopherol Stereoisomer Proportion and Enhances Fetal Antioxidant Capacity, Compared to Synthetic Vitamin E Administration.

INTRODUCTION: Low dietary intake of vitamin E is a global public health issue. RRR-α-tocopherol (RRR-αT) is the only naturally occurring vitamin E stereoisomer, but the equimolecular mixture of all eight stereoisomers, synthetic vitamin E (S-αT), is commonly consumed. The objective of this study was to evaluate bioavailability and antioxidant activity of RRR-αT versus S-αT, in both mother and fetus, after maternal supplementation during pregnancy. METHODS: Female rats (7 weeks of age) received a modified AIN-93G diet supplemented with 75 IU/kg of RRR-αT (NVE, n = 20) or S-αT (SVE, n = 17). At delivery, the levels of αT, stereoisomer distribution, and antioxidant capacity were analyzed in maternal and fetal plasma. RESULTS: NVE administration significantly increased the proportion of RRR-αT stereoisomer in maternal and fetal plasma. The percentage of RRR-αT increased from 32.76% to 88.33% in maternal plasma, and 35.25% to 97.94% in fetal plasma, in the NVE group compared to SVE. Fetal plasma from the NVE group was found to have higher total antioxidant capacity compared to SVE. Lastly, fetal plasma RRR-αT stereoisomer percentage was positively associated with expression levels of scavenger receptor class B type 1 (SR-B1) in the placenta. CONCLUSIONS: Both natural and synthetic sources of vitamin E showed similar bioavailability. Still, NVE supplementation increased the proportion of RRR-αT and promoted higher antioxidant activity in fetal plasma at birth. Placental SR-B1 might be involved in the stereoselective transfer of RRR-αT stereoisomer across the placenta and may improve αT bioactivity in the fetus.

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging.

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.

A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders.

The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected.

Performance Evaluation of Deep Learning-Based Prostate Cancer Screening Methods in Histopathological Images: Measuring the Impact of the Model's Complexity on Its Processing Speed.

Prostate cancer (PCa) is the second most frequently diagnosed cancer among men worldwide, with almost 1.3 million new cases and 360,000 deaths in 2018. As it has been estimated, its mortality will double by 2040, mostly in countries with limited resources. These numbers suggest that recent trends in deep learning-based computer-aided diagnosis could play an important role, serving as screening methods for PCa detection. These algorithms have already been used with histopathological images in many works, in which authors tend to focus on achieving high accuracy results for classifying between malignant and normal cases. These results are commonly obtained by training very deep and complex convolutional neural networks, which require high computing power and resources not only in this process, but also in the inference step. As the number of cases rises in regions with limited resources, reducing prediction time becomes more important. In this work, we measured the performance of current state-of-the-art models for PCa detection with a novel benchmark and compared the results with PROMETEO, a custom architecture that we proposed. The results of the comprehensive comparison show that using dedicated models for specific applications could be of great importance in the future.

Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification.

Monitoring animals' behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt.

Event-Based Gesture Recognition through a Hierarchy of Time-Surfaces for FPGA.

Neuromorphic vision sensors detect changes in luminosity taking inspiration from mammalian retina and providing a stream of events with high temporal resolution, also known as Dynamic Vision Sensors (DVS). This continuous stream of events can be used to extract spatio-temporal patterns from a scene. A time-surface represents a spatio-temporal context for a given spatial radius around an incoming event from a sensor at a specific time history. Time-surfaces can be organized in a hierarchical way to extract features from input events using the Hierarchy Of Time-Surfaces algorithm, hereinafter HOTS. HOTS can be organized in consecutive layers to extract combination of features in a similar way as some deep-learning algorithms do. This work introduces a novel FPGA architecture for accelerating HOTS network. This architecture is mainly based on block-RAM memory and the non-restoring square root algorithm, requiring basic components and enabling it for low-power low-latency embedded applications. The presented architecture has been tested on a Zynq 7100 platform at 100 MHz. The results show that the latencies are in the range of 1 µ s to 6.7 µ s, requiring a maximum dynamic power consumption of 77 mW. This system was tested with a gesture recognition dataset, obtaining an accuracy loss for 16-bit precision of only 1.2% with respect to the original software HOTS.

Combined effects of microplastics and chemical contaminants on the organ toxicity of zebrafish (Danio rerio).

Microplastics contamination of the aquatic environment is considered a growing problem. The ingestion of microplastics has been documented for a variety of aquatic animals. Studies have shown the potential of microplastics to affect the bioavailability and uptake route of sorbed co-contaminants of different nature in living organisms. Persistent organic pollutants and metals have been the co-contaminants majorly investigated in this field. The combined effect of microplastics and sorbed co-contaminants in aquatic organisms still needs to be properly understood. To address this, we have subjected zebrafish to four different feeds: A) untreated feed; B) feed supplemented with microplastics (LD-PE 125-250µm of diameter); C) feed supplemented with 2% microplastics to which a mixture of PCBs, BFRs, PFCs and methylmercury were sorbed; and D) feed supplemented with the mixture of contaminants only. After 3 weeks of exposure fish were dissected and liver, intestine, muscular tissue and brain were extracted. After visual observation, evaluation of differential gene expression of some selected biomarker genes in liver, intestine and brain were carried out. Additionally, quantification of perfluorinated compounds in liver, brain, muscular tissue and intestine of some selected samples were performed. The feed supplemented with microplastics with sorbed contaminants produced the most evident effects especially on the liver. The results indicate that microplastics alone does not produce relevant effects on zebrafish in the experimental conditions tested; on the contrary, the combined effect of microplastics and sorbed contaminants altered significantly their organs homeostasis in a greater manner than the contaminants alone.

Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics.

Taking inspiration from biology to solve engineering problems using the organizing principles of biological neural computation is the aim of the field of neuromorphic engineering. This field has demonstrated success in sensor based applications (vision and audition) as well as in cognition and actuators. This paper is focused on mimicking the approaching detection functionality of the retina that is computed by one type of Retinal Ganglion Cell (RGC) and its application to robotics. These RGCs transmit action potentials when an expanding object is detected. In this work we compare the software and hardware logic FPGA implementations of this approaching function and the hardware latency when applied to robots, as an attention/reaction mechanism. The visual input for these cells comes from an asynchronous event-driven Dynamic Vision Sensor, which leads to an end-to-end event based processing system. The software model has been developed in Java, and computed with an average processing time per event of 370 ns on a NUC embedded computer. The output firing rate for an approaching object depends on the cell parameters that represent the needed number of input events to reach the firing threshold. For the hardware implementation, on a Spartan 6 FPGA, the processing time is reduced to 160 ns/event with the clock running at 50 MHz. The entropy has been calculated to demonstrate that the system is not totally deterministic in response to approaching objects because of several bioinspired characteristics. It has been measured that a Summit XL mobile robot can react to an approaching object in 90 ms, which can be used as an attentional mechanism. This is faster than similar event-based approaches in robotics and equivalent to human reaction latencies to visual stimulus.

Detection of exposure effects of mixtures of heavy polycyclic aromatic hydrocarbons in zebrafish embryos.

In this study we evaluated the exposure effects of mixtures of five polycyclic aromatic hydrocarbons (PAHs); namely, benzo[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene, benzo[k]fluoranthene and chrysene on zebrafish embryos. Supplementation of the exposure media with 0.45% dimethyl sulfoxide and 50 ppm of Tween 20 could guarantee the solubilization and stabilization of the PAHs up to 24 h without affecting the embryos development. The exposure effects were tested by detecting the differential expression of a number of genes related to the aryl hydrocarbon receptor gene battery. Effects were detectable already after 6 h of exposure. After 24 h of exposure, all PAHs, except for benzo[a]anthracene, acted as potent inducers of the gene cyp1a1. Benzo[k]fluoranthene was the major inducer; the effect caused by the mixture at the lower concentration tested (1 ng ml-1 ) was dominated by its presence. However, in the mixture at the highest concentration tested (10 ng ml-1 ) it caused less induction and was not dominant. No significant bioaccumulation values were detected on embryos exposed to the PAHs tested in this study; however, the results obtained, indicated that PAHs undergo a very rapid metabolization inside the embryos, and that those biotransformation products yield changes on the expression of genes involved in the aryl hydrocarbon receptor pathway. Future work should focus on identification of the PAH metabolization products and on the effect of these metabolites on toxicity. Copyright © 2016 John Wiley & Sons, Ltd.

Toxic effects of colloidal nanosilver in zebrafish embryos.

A variety of consumer products containing silver nanoparticles (Ag NPs) are currently marketed. However, their safety for humans and for the environment has not yet been established and no standard method to assess their toxicity is currently available. The objective of this work was to develop an effective method to test Ag NP toxicity and to evaluate the effects of ion release and Ag NP size on a vertebrate model. To this aim, the zebrafish animal model was exposed to a solution of commercial nanosilver. While the exposure of embryos still surrounded by the chorion did not allow a definite estimation of the toxic effects exerted by the compound, the exposure for 48 h of 3-day-old zebrafish hatched embryos afforded a reliable evaluation of the effects of Ag NPs. The effects of the exposure were detected especially at molecular level; in fact, some selected genes expressed differentially after the exposure. The Ag NP toxic performance was due to the combined effect of Ag(+) ion release and Ag NP size. However, the effect of NP size was particularly detectable at the lowest concentration of nanosilver tested (0.01 mg l(-1)) and depended on the solubilization media. The results obtained indicate that in vivo toxicity studies of nanosilver should be performed with ad hoc methods (in this case using hatched embryos) that might be different depending on the type of nanosilver. Moreover, the addition of this compound to commercial products should take into consideration the Ag NP solubilization media.

A systematic comparison of deep learning methods for Gleason grading and scoring.

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.

Neuro-inspired spike-based motion: from dynamic vision sensor to robot motor open-loop control through spike-VITE.

In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation.

Quality changes of Atlantic horse mackerel fillets (Trachurus trachurus) packed in a modified atmosphere at different storage temperatures.

BACKGROUND: Atlantic horse mackerel (Trachurus trachurus) is an important commercial fish in Europe. Changes in microbial growth, sensory quality and chemical evolution of modified atmosphere packed (MAP) horse mackerel fillets (48% CO2/50% N2/2% O2) were monitored at different storage temperatures (2, 4, 6 and 10 °C). RESULTS: The observed sensory shelf life of MAP fish fillets was found to be approximately 7 days at 2 °C, 6 days at 4 °C, 5 at 6 °C and 3 days at 10 °C. A significant correlation was observed between all sensory attributes and two groups of bacteria [total viable count (TVC) and psychrotrophic bacteria], with sensorial rejection when the bacteria approached 106 CFU g⁻¹ at 4, 6 and 10 °C. A level of 30-50 mg total volatile basic nitrogen 100 g⁻¹ of mackerel muscle was well correlated with sensory shelf life determination at all temperatures, except for 2 °C. During the storage time, lipid oxidation did not increase at any temperature and could not be related to quality decay. The presence of acetaldehyde and butyraldehyde in the packing atmosphere was only found in fillets stored at the highest temperature studied (10 °C). CONCLUSION: In this study, key quality parameters were established to monitor the spoilage and shelf life of MAP Trachurus trachurus stored at different temperatures. In addition, these results confirm the use of modified atmosphere packaging as a mild preservation method for fillets of Atlantic horse mackerel.

A neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs.

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

Effects of ß-Hydroxy ß-Methylbutyrate Supplementation on Working Memory and Hippocampal Long-Term Potentiation in Rodents.

ß-hydroxy ß-methylbutyrate (HMB), a metabolite of the essential amino acid leucine, has been shown to preserve muscle mass and strength during aging. The signaling mechanism by which HMB elicits its favorable effects on protein metabolism in skeletal muscle is also preserved in the brain. However, there are only a few studies, all at relatively high doses, addressing the effect of HMB supplementation on cognition. This study evaluated the effects of different doses of HMB on the potentiation of hippocampal synapses following the experimental induction of long-term potentiation (LTP) in the hippocampus of behaving rats, as well as on working memory test (delayed matching-to-position, DMTP) in mice. HMB doses in rats were 225 (low), 450 (medium), and 900 (high) mg/kg body weight/day and were double in mice. Rats who received medium or high HMB doses improved LTP, suggesting that HMB administration enhances mechanisms related to neuronal plasticity. In the DMTP test, mice that received any of the tested doses of HMB performed better than the control group in the overall test with particularities depending on the dose and the task phase.

Calcifediol During Pregnancy Improves Maternal and Fetal Availability of Vitamin D Compared to Vitamin D3 in Rats and Modifies Fetal Metabolism.

The fetus depends on the transplacental transfer of vitamin D. Calcifediol (25-OH-D3) is the vitamin D metabolite that crosses the placenta. Previously, oral 25-OH-D3 improved serum 25-OH-D3 compared to vitamin D3 in non-pregnant subjects, although no studies are available in pregnant women. We evaluated the availability of oral 25-OH-D3 compared to vitamin D3 during pregnancy, as well as, their levels in the fetus and effect on metabolism-related proteins. Twenty female rats per group were fed with 25 µg/kg of diet of vitamin D3 (1,000 UI vitamin D/kg diet) or with 25 µg/kg diet of 25-OH-D3. We analyzed 25-OH-D3 levels in maternal and fetal plasma; protein levels of vitamin D receptor (VDR), fatty acid translocase (FAT), and scavenger-receptor class B type-1 (SR-B1) in both maternal liver and placenta; and protein levels of VDR and Glutamate decarboxylase (GAD67) in fetal brain. 25-OH-D3 doubled the concentration of 25-OH-D3 in both maternal and fetal plasma compared to vitamin D3. In addition, maternal liver VDR, FAT, and SR-BI increased significantly in the 25-OH-D3 group, but no changes were found in the placenta. Interestingly, 25-OH-D3 decreased GAD67 expression in the fetal brain and it also tended to decrease VDR (P = 0.086). In conclusion, 25-OH-D3 provided better vitamin D availability for both mother and fetus when administered during pregnancy compared to vitamin D3. No adverse effects on pregnancy outcomes were observed. The effects of 25-OH-D3 on the expression of VDR and GAD67 in fetal brain require further investigation.

An Event-Based Digital Time Difference Encoder Model Implementation for Neuromorphic Systems.

Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such systems. Furthermore, the event-based signal processing approach can be exploited for reducing the computational load and avoiding data loss due to its inherently sparse representation of sensed data and adaptive sampling time. In event-based systems, the information is commonly coded by the number of spikes within a specific temporal window. However, the temporal information of event-based signals can be difficult to extract when using rate coding. In this work, we present a novel digital implementation of the model, called time difference encoder (TDE), for temporal encoding on event-based signals, which translates the time difference between two consecutive input events into a burst of output events. The number of output events along with the time between them encodes the temporal information. The proposed model has been implemented as a digital circuit with a configurable time constant, allowing it to be used in a wide range of sensing tasks that require the encoding of the time difference between events, such as optical flow-based obstacle avoidance, sound source localization, and gas source localization. This proposed bioinspired model offers an alternative to the Jeffress model for the interaural time difference estimation, which is validated in this work with a sound source lateralization proof-of-concept system. The model was simulated and implemented on a field-programmable gate array (FPGA), requiring 122 slice registers of hardware resources and less than 1 mW of power consumption.

Beneficial effects of dietary supplementation with green tea catechins and cocoa flavanols on aging-related regressive changes in the mouse neuromuscular system.

Besides skeletal muscle wasting, sarcopenia entails morphological and molecular changes in distinct components of the neuromuscular system, including spinal cord motoneurons (MNs) and neuromuscular junctions (NMJs); moreover, noticeable microgliosis has also been observed around aged MNs. Here we examined the impact of two flavonoid-enriched diets containing either green tea extract (GTE) catechins or cocoa flavanols on age-associated regressive changes in the neuromuscular system of C57BL/6J mice. Compared to control mice, GTE- and cocoa-supplementation significantly improved the survival rate of mice, reduced the proportion of fibers with lipofuscin aggregates and central nuclei, and increased the density of satellite cells in skeletal muscles. Additionally, both supplements significantly augmented the number of innervated NMJs and their degree of maturity compared to controls. GTE, but not cocoa, prominently increased the density of VAChT and VGluT2 afferent synapses on MNs, which were lost in control aged spinal cords; conversely, cocoa, but not GTE, significantly augmented the proportion of VGluT1 afferent synapses on aged MNs. Moreover, GTE, but not cocoa, reduced aging-associated microgliosis and increased the proportion of neuroprotective microglial phenotypes. Our data indicate that certain plant flavonoids may be beneficial in the nutritional management of age-related deterioration of the neuromuscular system.

Evaluation of immunoassays as an alternative for the rapid determination of pesticides in wine and grape samples.

The main objective of this paper is to address the performance of immunochemical assays for the detection of the residues of three pesticides [atrazine, bromopropylate, and 2,4,6-trichlorophenol (TCP)] in real winery samples, such as wine, grapes, and grape juice. Different approaches have been evaluated to minimize interferences from the matrixes, and suitable working protocols have been established in order to achieve the necessary LODs, accuracy, and precision for real samples. A simple dilution of the sample proved to be sufficient for the determination of atrazine and bromopropylate in red and white wine and grape juice at the required levels of concentration. However, for TCP, an SPE procedure has been optimized using amino cartridges. The recoveries were above 85% in all cases, and the LOD values were below the parts per billion level, except for bromopropylate, which ranged between 2 and 50 microg/L, depending on the matrix. The grape matrix effect could be resolved by a simple extraction with methanol. Complete recoveries were obtained, and the final measurement procedures were able to determine selected pesticides below their maximum residue levels. The newly developed methods have been compared with standard chromatographic methods.

Enzymatic assays for the assessment of toxic effects of halogenated organic contaminants in water and food. A review.

Halogenated organic compounds are a particular group of contaminants consisting of a large number of substances, and of great concern due to their persistence in the environment, potential for bioaccumulation and toxicity. Some of these compounds have been classified as persistent organic pollutants (POPs) under The Stockholm Convention and many toxicity assessments have been conducted on them previously. In this work we provide an overview of enzymatic assays used in these studies to establish toxic effects and dose-response relationships. Studies in vivo and in vitro have been considered with a particular emphasis on the impact of halogenated compounds on the activity of relevant enzymes to the humans and the environment. Most information available in the literature focuses on chlorinated compounds, but brominated and fluorinated molecules are also the target of increasing numbers of studies. The enzymes identified can be classified as enzymes: i) the activities of which are affected by the presence of halogenated organic compounds, and ii) those involved in their metabolisation/detoxification resulting in increased activities. In both cases the halogen substituent seems to have an important role in the effects observed. Finally, the use of these enzymes in biosensing tools for monitoring of halogenated compounds is described.