Experimental And Computational Analyses Of Locomotor Rhythm Generation And Modulation In Caenorhabditis Elegans

Neural circuits coordinate with muscles and sensory feedback to generate motor behaviors appropriate to its natural environment. Studying mechanisms underlying complex organism locomotion has been challenging, partly due to the complexity of their nervous systems. Here, I used the roundworm C. elegans to understand the locomotor circuit. With its well-mapped nervous system, easily-measurable movements, genetic manipulability, and many human homologous genes, C. elegans has been commonly used as a model organism for dissecting the circuit, cellular, and molecular principles of locomotion. My work introduces two separate approaches to probe the mechanisms by which the C. elegans motor circuit generates and modulates undulations. First, I quantified C. elegans movements during free locomotion and during transient muscle inhibition. Undulations were asymmetrical with respect to the duration of bending and unbending per cycle. Phase response curves induced by brief optogenetic head muscle inhibitions showed gradual increases and rapid decreases as a function of phase at which the perturbation was applied. A relaxation oscillator model was developed based on proprioceptive thresholds that switch the active muscle moment. It quantitatively agrees with data from free movement, phase responses, and previous results for gait adaptation to mechanical loads. Next, I characterized a proprioception-mediated compensatory behavior during C. elegans forward locomotion: the anterior body bending amplitude compensates for the change in midbody bending amplitude by an opposing homeostatic response. I demonstrated that curvature compensation requires dopamine signaling driven by PDE neurons. Calcium imaging experiments suggested a proprioceptive functionality for PDE in sensing midbody curvature. Downstream of PDE dopamine signaling, curvature compensation requires D2-like dopamine receptor DOP-3 in the interneurons AVK. FMRFamide-like neuropeptide FLP-1, released by AVK, regulates SMB motor neurons via receptor NPR-6 to modulate anterior bending amplitude. These results revealed a mechanism whereby proprioception works with dopamine and neuropeptide signaling to mediate homeostatic locomotor control. Together, through a consolidation of experimental and computational approaches, I found C. elegans utilizes its circuitry not only to act motor behaviors but to adjust/correct its ongoing behaviors in its natural contexts

EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training

Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-EMG). The framework utilizes a combination of techniques: decoding EEG by using Graph Convolutional LSTM Networks (GCN-LSTM) to recognize the subject’s motion intention, and decoding EMG by using a convolutional neural network (CNN) to accurately identify hand movements. In EEG decoding, the correlation between channels is calculated using Standardized Permutation Mutual Information (SPMI), and the decoding process is further explained by analyzing the correlation matrix. In EMG decoding, experiments are conducted on two task paradigms, both achieving promising results. The proposed framework is validated using the publicly available WAL-EEG-GAL (Wearable interfaces for hand function recovery Electroencephalography Grasp-And-Lift) dataset, where the average classification accuracies of EEG and EMG are 0.892 and 0.954, respectively. This research aims to establish an efficient and user-friendly EEG-EMG hybrid BCI, thereby facilitating the hand rehabilitation training of stroke patients

Group delay dispersion monitoring for computational manufacturing of dispersive mirrors

We present a computational manufacturing program for monitoring group delay dispersion (GDD). Two kinds of dispersive mirrors computational manufactured by GDD, broadband, and time monitoring simulator are compared. The results revealed the particular advantages of GDD monitoring in dispersive mirror deposition simulations. The self-compensation effect of GDD monitoring is discussed. GDD monitoring can improve the precision of layer termination techniques, it may become a possible approach to manufacture other optical coatings

A General SIMD-based Approach to Accelerating Compression Algorithms

Compression algorithms are important for data oriented tasks, especially in the era of Big Data. Modern processors equipped with powerful SIMD instruction sets, provide us an opportunity for achieving better compression performance. Previous research has shown that SIMD-based optimizations can multiply decoding speeds. Following these pioneering studies, we propose a general approach to accelerate compression algorithms. By instantiating the approach, we have developed several novel integer compression algorithms, called Group-Simple, Group-Scheme, Group-AFOR, and Group-PFD, and implemented their corresponding vectorized versions. We evaluate the proposed algorithms on two public TREC datasets, a Wikipedia dataset and a Twitter dataset. With competitive compression ratios and encoding speeds, our SIMD-based algorithms outperform state-of-the-art non-vectorized algorithms with respect to decoding speeds

Different Selectivity in Fungal Communities Between Manure and Mineral Fertilizers: A Study in an Alkaline Soil After 30 Years Fertilization

Fertilizer application has contributed substantially to increasing crop yield. Despite the important role of soil fungi in agricultural production, we still have limited understanding of the complex responses of fungal taxonomic and functional groups to organic and mineral fertilization in long term. Here we report the responses of the fungal communities in an alkaline soil to 30-year application of mineral fertilizer (NP), organic manure (M) and combined fertilizer (NPM) by the Illumina HiSeq sequencing and quantitative real-time PCR to target fungal internal transcribed spacer (ITS) genes. The results show: (1) compared to the unfertilized soil, fertilizer application increased fungal diversity and ITS gene copy numbers, and shifted fungal community structure. Such changes were more pronounced in the M and NPM soils than in the NP soil (except for fungal diversity), which can be largely attributed to the manure induced greater increases in soil total organic C, total N and available P. (2) Compared to the unfertilized soil, the NP and NPM soils reduced the proportion of saprotrophs by 40%, the predominant taxa of which may potentially affect cellulose decomposition. (3) Indicator species analysis suggested that the indicator operational taxonomic units (OTUs) in the M soil occupied 25.6% of its total community, but that only accounted for 0.9% in the NP soil. Our findings suggest that fertilization-induced changes of total fungal community were more responsive to organic manure than mineral fertilizer. The reduced proportion of cellulose decomposition-related saprotrophs in mineral fertilizer treatments may potentially contribute to increasing their soil C stocks

What you don't know... can't hurt you? A natural field experiment on relative performance feedback in higher education

This paper studies the effect of providing feedback to college students on their position in the grade distribution by using a natural field experiment. This information was updated every six months during a three-year period. We find that greater grades transparency decreases educational performance, as measured by the number of examinations passed and grade point average (GPA). However, self-reported satisfaction, as measured by surveys conducted after feedback is provided but before students take their examinations, increases. We provide a theoretical framework to understand these results, focusing on the role of prior beliefs and using out-of-trial surveys to test the model. In the absence of treatment, a majority of students underestimate their position in the grade distribution, suggesting that the updated information is “good news” for many students. Moreover, the negative effect on performance is driven by those students who underestimate their position in the absence of feedback. Students who overestimate initially their position, if anything, respond positively. The performance effects are short lived—by the time students graduate, they have similar accumulated GPA and graduation rates

Subject-independent EEG classification based on a hybrid neural network

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

IntroductionBrain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.MethodsThis paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.ResultsA classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.DiscussionThis approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery