Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Sediment toxicity tests have applications in ecological risk and chemical safety assessments. Despite the many years of experience in testing and the availability of standard protocols, sediment toxicity testing remains challenging with very hydrophobic organic chemicals (VHOCs; i.e., chemicals with a log octanol/water partition coefficient of more than 6). The challenges primarily relate to the chemicals\' low aqueous solubilities and slow kinetics, due to which several experimental artifacts may occur. To investigate the potential artifacts, experiments were performed, focusing on spiking and equilibrating (aging) sediments, as well as exposure quantification with passive sampling. The results demonstrated that generally applied, Organisation for Economic Co-operation and Development-recommended spiking (coating) methods may lead to significant chemical losses and the formation of nondissolved, nonbioavailable VHOCs. Direct spiking appeared to be the most optimal, provided that intensive mixing was applied simultaneously. Passive dosing was tested as a novel way of spiking liquid VHOCs, but the approach proved unsuccessful. Intensive postspiking mixing during sediment equilibration for 1 to 2 weeks was shown to be essential for producing a homogeneous system, minimizing the presence of nondissolved chemical (crystals or nonaqueous phase liquids; NAPLs), and creating a stable toxicological response in subsequent toxicity tests. Finally, exposure quantification of VHOCs in sediments through passive sampling was found to be feasible with different polymers, although prolonged equilibration times may be required, and determining sampler/water partition coefficients can be extremely challenging. The results of additional experiments, focusing on toxicity test exposure duration, concentrations above which NAPLs will occur, and ways to distinguish actual toxicity from false-positive results, are presented in Part 2 of this publication series. Environ Toxicol Chem 2024;43:1717-1727. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Advancements in mycelium technology, stemming from fungal electronics and the development of living mycelium composites and skins, have opened new avenues in the fusion of biological and artificial systems. This paper explores an experimental endeavour that successfully incorporates living, self-regenerating, and reactive Ganoderma sessile mycelium into a model cyborg figure, creating a bio-cybernetic entity. The mycelium, cultivated using established techniques, was homogeneously grown on the cyborg model\'s surface, demonstrating robust reactivity to various stimuli such as light exposure and touch. This innovative merger points towards the future of sustainable biomaterials and the potential integration of these materials into new and existing technologies.

Vestibular afferent neurons occur as two populations with differences in spike timing regularity that are independent of rate. The more excitable regular afferents have lower current thresholds and sustained spiking responses to injected currents, while irregular afferent neurons have higher thresholds and transient responses. Differences in expression of low-voltage-activated potassium (K LV ) channels are emphasized in models of spiking regularity and excitability in these neurons, leaving open the potential contributions of the voltage-gated sodium (Na V ) channels responsible for the spike upstroke. We investigated the impact of different Na V current modes (transient, persistent, and resurgent) with whole-cell patch clamp experiments in mouse vestibular ganglion neurons (VGNs), the cultured and dissociated cell bodies of afferents. All VGNs had transient Na V current, many had a small persistent (non-inactivating) Na V current, and a few had resurgent current, which flows after the spike peak when Na V channels that were blocked are unblocked. Na V 1.6 channels conducted most or all of each Na V current mode, and a Na V 1.6-selective blocker decreased spike rate and altered spike waveforms in both sustained and transient VGNs. A Na V channel agonist enhanced persistent current and increased spike rate and regularity. We hypothesized that persistent and resurgent currents have different effects on sustained (regular) VGNs vs. transient (irregular) VGNs. Lacking blockers specific for the different current modes, we used modeling to isolate their effects on spiking of simulated transient and sustained VGNs, driven by simulated current steps and noisy trains of simulated EPSCs. In all simulated neurons, increasing transient Na V current increased spike rate and rate-independent regularity. In simulated sustained VGNs, adding persistent current increased both rate and rate-independent regularity, while adding resurgent current had limited impact. In transient VGNs, adding persistent current had little impact, while adding resurgent current increased both rate and rate-independent irregularity by enhancing sensitivity to synaptic noise. These experiments show that the small Na V current modes may enhance the differentiation of afferent populations, with persistent currents selectively making regular afferents more regular and resurgent currents selectively making irregular afferents less regular.

Sufficient homogeneity of the certified parameter(s) over the whole fill series of a matrix reference material (RM) is a fundamental quality criterion. In practice, the heterogeneity of the target parameter is evaluated, whereby a relative value can be calculated of how much the target parameter is varying over the RM-batch. A high degree of homogeneity (low heterogeneity) is an inherent quality mark of a good RM. Here, we report how challenging matrix RMs were produced by using particle suspensions at the core of the material processing step. The examples of matrix RMs produced span from whole water reference materials for persistent organic pollutants, PM2.5-like atmospheric dust certified for specific ions to microplastic RMs. Most of these RMs were subsequently used in different phases of analytical method development or for method validation. Common to all these matrices is that they cannot be easily mixed, handled, or dosed to prepare larger sample batches. In all cases, a continuously stirred suspension of particles was used during material processing. In general, relative between-bottle heterogeneities from 1.6 to 6% were achieved for the target parameters in these matrix presentations. Concerning developments of new CRMs in emerging fields, the co-dependence between the availability of validated analytical methods with good repeatability and testing materials with a known and high homogeneity of the target parameter(s) becomes particularly challenging. This situation is an RM/Method causality dilemma. To overcome that hurdle, strategies are proposed for stepwise processes where RM producers and a network of analytical method developers could work hand in hand. In addition, development of a portfolio of inexpensive and well-homogenised common samples coupled with a reporting interface is suggested. This would benefit method developers and RM producers alike. As more and more data is compiled for a specific matrix, it paves the way for new and challenging RMs that can later be used by a wider community.

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

BACKGROUND: Proactive drug facilitated crime (DFC) is the administration of psychoactive substances (PAS) for criminal purposes without the victim\'s knowledge or by force. In Paris, France, patients who report suspected proactive DFC to the police are examined at the Department of Forensic Medicine (DFM) of the Hôtel-Dieu Hospital. Preventively blood and urine samples are collected but not systematically analyzed by the judicial authority. We aimed to assess the proportion of probable proactive DFC in patients examined at the Hôtel-Dieu DFM following a police report for suspected proactive DFC.
METHODS: Blood and urine samples were collected from 100 patients. Toxicological analyses were performed by the toxicology laboratory of the Lariboisière Hospital. The results were correlated with the clinical data collected at the initial and follow-up consultations.
RESULTS: At least one PAS was detected in 86% of the cases (voluntary or involuntary intake). After correlation with clinical data, 32% of the cases were classified as probable proactive DFC. In these cases, 49% of the substances identified were illicit substances (amphetamines, MDMA, etc.); 16% were benzodiazepines and related substances; 16% were antihistamines and sedatives; 14% were opioids; and 5% were antidepressants and anti-epileptics. In 90% of the cases, patients reported a voluntary ethanol consumption in the hours prior to the suspected proactive DFC.
CONCLUSIONS: Toxicological analyses revealed a high proportion of both probable proactive DFC and probable opportunistic DFC. Our results indicate the need to perform systematical toxicological analysis in cases of suspected DFC.

Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons in neuromorphic (brain-inspired) photonic systems. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) coupled to a photodetector as a receiver and a vertical cavity surface emitting laser as a transmitter. We demonstrate a well-defined excitability threshold, above which the neuron produces optical spiking responses with characteristic neural-like refractory period. We utilise its fan-in capability to perform in-device coincidence detection (logical AND) and exclusive logical OR (XOR) tasks. These results provide first experimental validation of deterministic triggering and tasks in an RTD-based spiking optoelectronic neuron with both input and output optical (I/O) terminals. Furthermore, we also investigate in simulation the prospects of the proposed system for nanophotonic implementation in a monolithic design combining a nanoscale RTD element and a nanolaser; therefore demonstrating the potential of integrated RTD-based excitable nodes for low footprint, high-speed optoelectronic spiking neurons in future neuromorphic photonic hardware.

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High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent.
We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns.
The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data.
The model accuracy for top 0-20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.