I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans.

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.

Comparison of orientation encoding across layers within single columns of primate V1 revealed by high-density recordings.

Primary visual cortex (V1) has been the focus of extensive neurophysiological investigations, with its laminar organization serving as a crucial model for understanding the functional logic of neocortical microcircuits. Utilizing newly developed high-density, Neuropixels probes, we measured visual responses from large populations of simultaneously recorded neurons distributed across layers of macaque V1. Within single recordings, myriad differences in the functional properties of neuronal subpopulations could be observed. Notably, while standard measurements of orientation selectivity showed only minor differences between laminar compartments, decoding stimulus orientation from layer 4C responses outperformed both superficial and deep layers within the same cortical column. The superior orientation discrimination within layer 4C was associated with greater response reliability of individual neurons rather than lower correlated activity within neuronal populations. Our results underscore the efficacy of high-density electrophysiology in revealing the functional organization and network properties of neocortical microcircuits within single experiments.

Eliminating quantization errors in classification-based sound source localization.

Sound Source Localization (SSL) involves estimating the Direction of Arrival (DOA) of sound sources. Since the DOA estimation output space is continuous, regression might be more suitable for DOA, offering higher precision. However, in practice, classification often outperforms regression, exhibiting greater robustness. Conversely, classification's drawback is inherent quantization error. Within the classification paradigm, the DOA output space is discretized into several intervals, each treated as a class. These classes show strong inter-class correlations, being inherently ordered, with higher similarity as intervals grow closer. Nevertheless, this characteristic has not been fully exploited. To address this, we propose Unbiased Label Distribution (ULD) to eliminate quantization error in training targets. Furthermore, we introduce Weighted Adjacent Decoding (WAD) to overcome quantization error during the decoding stage. Finally, we tailor two loss functions for the soft labels: Negative Log Absolute Error (NLAE) and Mean Squared Error without activation (MSE(wo)). Experimental results show our approach surpasses classification quantization limits, achieving state-of-the-art performance. Our code and supplementary material are available at https://github.com/linfeng-feng/ULD.

Global motor dynamics - invariant neural representations of motor behavior in distributed brain-wide recordings.

OBJECTIVE: Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist. APPROACH: Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder. MAIN RESULTS: We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning. SIGNIFICANCE: Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

A comparison of three evolutionary algorithms for group scheduling in theme parks with multitype facilities.

During the peak tourist season, large theme parks often experience a simultaneous influx of visitors, resulting in prolonged waiting times for popular attractions. This extended waiting significantly reduces tourists' satisfaction and may negatively impact their willingness to revisit the theme park. In Taiwan, schools at all levels often plan graduation trips to theme parks for their students. Students are divided into groups and must enter and exit the theme park at the same time. This article presents a new theme park problem with multitype facilities (TPP-MTF) for student groups. Based on the group's preference for theme park facilities, multitype reserved tickets with popular facilities are designed for groups, so groups do not need to wait for the reserved facilities. Since the waiting time for groups can be reduced, the theme park can also obtain ticket fees in advance and estimate the number of visitors to the theme park, so the theme park and the group can achieve a win-win situation. This article proposes a new decoding approach for a random permutation of integer sequence and embeds it into an immune-based algorithm, genetic algorithm, and particle swarm optimization algorithm to solve the TPP-MTF problem. A theme park in Taiwan was taken as an example and numerical results of the three algorithms were analyzed and compared to verify the effectiveness of the proposed algorithms.

A Broad Spectrum Lasso Peptide Antibiotic Targeting the Bacterial Ribosome.

Lasso peptides, biologically active molecules with a distinct structurally constrained knotted fold, are natural products belonging to the class of ribosomally-synthesized and posttranslationally modified peptides (RiPPs). Lasso peptides act upon several bacterial targets, but none have been reported to inhibit the ribosome, one of the main antibiotic targets in the bacterial cell. Here, we report the identification and characterization of the lasso peptide antibiotic, lariocidin (LAR), and its internally cyclized derivative, lariocidin B (LAR-B), produced by Paenabacillussp. M2, with broad-spectrum activity against many bacterial pathogens. We show that lariocidins inhibit bacterial growth by binding to the ribosome and interfering with protein synthesis. Structural, genetic, and biochemical data show that lariocidins bind at a unique site in the small ribosomal subunit, where they interact with the 16S rRNA and aminoacyl-tRNA, inhibiting translocation and inducing miscoding. LAR is unaffected by common resistance mechanisms, has a low propensity for generating spontaneous resistance, shows no human cell toxicity, and has potent in vivo activity in a mouse model of Acinetobacter baumannii infection. Our finding of the first ribosome-targeting lasso peptides uncovers new routes toward discovering alternative protein synthesis inhibitors and offers a new chemical scaffold for developing much-needed antibacterial drugs.

Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model.

Human-machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects' performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements.

A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.

In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm2, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.

Handwritten character classification from EEG through continuous kinematic decoding.

The classification of handwritten letters from invasive neural signals has lately been subject of research to restore communication abilities in people with limited movement capacities. This study explores the classification of ten letters (a,d,e,f,j,n,o,s,t,v) from non-invasive neural signals of 20 participants, offering new insights into the neural correlates of handwriting. Letters were classified with two methods: the direct classification from low-frequency and broadband electroencephalogram (EEG) and a two-step approach comprising the continuous decoding of hand kinematics and the application of those in subsequent classification. The two-step approach poses a novel application of continuous movement decoding for the classification of letters from EEG. When using low-frequency EEG, results show moderate accuracies of 23.1% for ten letters and 39.0% for a subset of five letters with highest discriminability of the trajectories. The two-step approach yielded significantly higher performances of 26.2% for ten letters and 46.7% for the subset of five letters. Hand kinematics could be reconstructed with a correlation of 0.10 to 0.57 (average chance level: 0.04) between the decoded and original kinematic. The study shows the general feasibility of extracting handwritten letters from non-invasively recorded neural signals and indicates that the proposed two-step approach can improve performances. As an exploratory investigation of the neural mechanisms of handwriting in EEG, we found significant influence of the written letter on the low-frequency components of neural signals. Differences between letters occurred mostly in central and occipital channels. Further, our results suggest movement speed as the most informative kinematic for the decoding of short hand movements.

Design of Low-Latency Layered Normalized Minimum Sum Low-Density Parity-Check Decoding Based on Entropy Feature for NAND Flash-Memory Channel.

As high-speed big-data communications impose new requirements on storage latency, low-density parity-check (LDPC) codes have become a widely used technology in flash-memory channels. However, the iterative LDPC decoding algorithm faces a high decoding latency problem due to its mechanism based on iterative message transmission. Motivated by the unbalanced bit reliability of codeword, this paper proposes two technologies, i.e., serial entropy feature-based layered normalized min-sum (S-EFB-LNMS) decoding and parallel entropy feature-based layered normalized min-sum (P-EFB-LNMS) decoding. First, we construct an entropy feature vector that reflects the real-time bit reliability of the codeword. Then, the reliability of the output information of the layered processing unit (LPU) is evaluated by analyzing the similarity between the check matrix and the entropy feature vector. Based on this evaluation, we can dynamically allocate and schedule LPUs during the decoding iteration process, thereby optimizing the entire decoding process. Experimental results show that these techniques can significantly reduce decoding latency.

A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts.

BACKGROUND AND OBJECTIVE: Electrode shift is always one of the critical factors to compromise the performance of myoelectric pattern recognition (MPR) based on surface electromyogram (SEMG). However, current studies focused on the global features of SEMG signals to mitigate this issue but it is just an oversimplified description of the human movements without incorporating microscopic neural drive information. The objective of this work is to develop a novel method for calibrating the electrode array shifts toward achieving robust MPR, leveraging individual motor unit (MU) activities obtained through advanced SEMG decomposition. METHODS: All of the MUs from decomposition of SEMG data recorded at the original electrode array position were first initialized to train a neural network for pattern recognition. A part of decomposed MUs could be tracked and paired with MUs obtained at the original position based on spatial distribution of their MUAP waveforms, so as to determine the shift vector (describing both the orientation and distance of the shift) implicated consistently by these multiple MU pairs. Given the known shift vector, the features of the after-shift decomposed MUs were corrected accordingly and then fed into the network to finalize the MPR task. The performance of the proposed method was evaluated with data recorded by a 16 × 8 electrode array placed over the finger extensor muscles of 8 subjects performing 10 finger movement patterns. RESULTS: The proposed method achieved a shift detection accuracy of 100 % and a pattern recognition accuracy approximating to 100 %, significantly outperforming the conventional methods with lower shift detection accuracies and lower pattern recognition accuracies (p < 0.05). CONCLUSIONS: Our method demonstrated the feasibility of using decomposed MUAP waveforms' spatial distributions to calibrate electrode shift. This study provides a new tool to enhance the robustness of myoelectric control systems via microscopic neural drive information at an individual MU level.

Transcranial Magnetic Stimulation Facilitates Neural Speech Decoding.

Transcranial magnetic stimulation (TMS) has been widely used to study the mechanisms that underlie motor output. Yet, the extent to which TMS acts upon the cortical neurons implicated in volitional motor commands and the focal limitations of TMS remain subject to debate. Previous research links TMS to improved subject performance in behavioral tasks, including a bias in phoneme discrimination. Our study replicates this result, which implies a causal relationship between electro-magnetic stimulation and psychomotor activity, and tests whether TMS-facilitated psychomotor activity recorded via electroencephalography (EEG) may thus serve as a superior input for neural decoding. First, we illustrate that site-specific TMS elicits a double dissociation in discrimination ability for two phoneme categories. Next, we perform a classification analysis on the EEG signals recorded during TMS and find a dissociation between the stimulation site and decoding accuracy that parallels the behavioral results. We observe weak to moderate evidence for the alternative hypothesis in a Bayesian analysis of group means, with more robust results upon stimulation to a brain region governing multiple phoneme features. Overall, task accuracy was a significant predictor of decoding accuracy for phoneme categories (F(1,135) = 11.51, p < 0.0009) and individual phonemes (F(1,119) = 13.56, p < 0.0003), providing new evidence for a causal link between TMS, neural function, and behavior.

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.

Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue. Approach: We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to. Main results: We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network (RNN) decoder with 10-12 clusters. Significance: This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.

Manipulating attentional priority creates a trade-off between memory and sensory representations in human visual cortex.

People often remember visual information over brief delays while actively engaging with ongoing inputs from the surrounding visual environment. Depending on the situation, one might prioritize mnemonic contents (i.e., remembering details of a past event), or preferentially attend sensory inputs (i.e., minding traffic while crossing a street). Previous fMRI work has shown that early sensory regions can simultaneously represent both mnemonic and passively viewed sensory information. Here we test the limits of such simultaneity by manipulating attention towards sensory distractors during a working memory task performed by human subjects during fMRI scanning. Participants remembered the orientation of a target grating while a distractor grating was shown during the middle portion of the memory delay. Critically, there were several subtle changes in the contrast and the orientation of the distractor, and participants were cued to either ignore the distractor, detect a change in contrast, or detect a change in orientation. Despite sensory stimulation being matched in all three conditions, the fidelity of memory representations in early visual cortex was highest when the distractor was ignored, intermediate when participants attended distractor contrast, and lowest when participants attended the orientation of the distractor during the delay. In contrast, the fidelity of distractor representations was lowest when ignoring the distractor, intermediate when attending distractor-contrast, and highest when attending distractor-orientation. These data suggest a trade-off in early sensory representations when engaging top-down feedback to attend both seen and remembered features and may partially explain memory failures that occur when subjects are distracted by external events.

Improving non-invasive trajectory decoding via neural correlates of continuous erroneous feedback processing.

Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intocorrectorerroneousto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.Approach.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Main results.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Significance.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.

Subword Representations Successfully Decode Brain Responses to Morphologically Complex Written Words.

This study extends the idea of decoding word-evoked brain activations using a corpus-semantic vector space to multimorphemic words in the agglutinative Finnish language. The corpus-semantic models are trained on word segments, and decoding is carried out with word vectors that are composed of these segments. We tested several alternative vector-space models using different segmentations: no segmentation (whole word), linguistic morphemes, statistical morphemes, random segmentation, and character-level 1-, 2- and 3-grams, and paired them with recorded MEG responses to multimorphemic words in a visual word recognition task. For all variants, the decoding accuracy exceeded the standard word-label permutation-based significance thresholds at 350-500 ms after stimulus onset. However, the critical segment-label permutation test revealed that only those segmentations that were morphologically aware reached significance in the brain decoding task. The results suggest that both whole-word forms and morphemes are represented in the brain and show that neural decoding using corpus-semantic word representations derived from compositional subword segments is applicable also for multimorphemic word forms. This is especially relevant for languages with complex morphology, because a large proportion of word forms are rare and it can be difficult to find statistically reliable surface representations for them in any large corpus.

[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].

In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.

tRNAVal allows four-way decoding with unmodified uridine at the wobble position in Lactobacillus casei.

Modifications at the wobble position (position 34) of tRNA facilitate interactions that enable or stabilize non-Watson-Crick basepairs. In bacterial tRNA, 5-hydroxyuridine (ho5U) derivatives xo5U [x: methyl (mo5U), carboxymethyl (cmo5U), and methoxycarbonylmethyl (mcmo5U)] present at the wobble positions of tRNAs are responsible for recognition of NYN codon families. These modifications of U34 allow basepairing not only with A and G but also with U and in some cases C. mo5U was originally found in Gram-positive bacteria, and cmo5U and mcmo5U were found in Gram-negative bacteria. tRNAs of Mycoplasma species, mitochondria, and chloroplasts adopt four-way decoding in which unmodified U34 recognizes codons ending in A, G, C, and U. Lactobacillus casei, Gram-positive bacteria and lactic acid bacteria, lacks the modification enzyme genes for xo5U biosynthesis. Nevertheless, L. casei has only one type of tRNAVal with the anticodon UAC [tRNAVal(UAC)]. However, the genome of L. casei encodes an undetermined tRNA (tRNAUnd) gene, and the sequence corresponding to the anticodon region is GAC. Here, we confirm that U34 in L. casei tRNAVal is unmodified and that there is no tRNAUnd expression in the cells. In addition, in vitro transcribed tRNAUnd was not aminoacylated by L. casei valyl-tRNA synthetase suggesting that tRNAUnd is not able to accept valine, even if expressed in cells. Correspondingly, native tRNAVal(UAC) with unmodified U34 bound to all four valine codons in the ribosome A site. This suggests that L. casei tRNAVal decodes all valine codons by four-way decoding, similarly to tRNAs from Mycoplasma species, mitochondria, and chloroplasts.

A double-blind trial of decoded neurofeedback intervention for specific phobias.

AIM: A new closed-loop functional magnetic resonance imaging method called multivoxel neuroreinforcement has the potential to alleviate the subjective aversiveness of exposure-based interventions by directly inducing phobic representations in the brain, outside of conscious awareness. The current study seeks to test this method as an intervention for specific phobia. METHODS: In a randomized, double-blind, controlled single-university trial, individuals diagnosed with at least two (one target, one control) animal subtype-specific phobias were randomly assigned (1:1:1) to receive one, three, or five sessions of multivoxel neuroreinforcement in which they were rewarded for implicit activation of a target animal representation. Amygdala response to phobic stimuli was assessed by study staff blind to target and control animal assignments. Pretreatment to posttreatment differences were analyzed with a two-way repeated-measures anova. RESULTS: A total of 23 participants (69.6% female) were randomized to receive one (n = 8), three (n = 7), or five (n = 7) sessions of multivoxel neuroreinforcement. Eighteen (n = 6 each group) participants were analyzed for our primary outcome. After neuroreinforcement, we observed an interaction indicating a significant decrease in amygdala response for the target phobia but not the control phobia. No adverse events or dropouts were reported as a result of the intervention. CONCLUSION: Results suggest that multivoxel neuroreinforcement can specifically reduce threat signatures in specific phobia. Consequently, this intervention may complement conventional psychotherapy approaches with a nondistressing experience for patients seeking treatment. This trial sets the stage for a larger randomized clinical trial to replicate these results and examine the effects on real-life exposure. CLINICAL TRIAL REGISTRATION: The now-closed trial was prospectively registered at ClinicalTrials.gov with ID NCT03655262.