Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning accurate path integration in ring attractor models of the head direction system

Ring attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila HD system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system.
Tackling Nonalcoholic Fatty Liver Disease: Three Targeted Populations

The scale of the nonalcoholic fatty liver disease (NAFLD) epidemic is staggering. It is estimated that 24% of the world’s people have NAFLD. (1) By the year 2030, the United States will have approximately 100 million individuals with NAFLD, leading to a higher incidence of decompensated cirrhosis, hepatocellular carcinoma (HCC), and liverrelated death. (2) NAFLD is strongly associated with the metabolic syndrome and its individual components. (3) Unfortunately, relative to the scale of the epidemic, few public health dollars are spent on obesity and NAFLD prevention. (4) When considering interventions to prevent NAFLD or delay progression to nonalcoholic steatohepatitis (NASH) and cirrhosis, targeted populations should experience longlasting benefits, with the potential to impact future generations, and be at higher risk for progression. With these principles in mind, children and adolescents, future parents, and postmenopausal women are appropriate targeted populations for interventions to mitigate the morbidity and mortality from NAFLD (Table 1).
Swarm SLAM: Challenges and Perspectives

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.
Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.
Illuminating the Brain With X-Rays: Contributions and Future Perspectives of High-Resolution Microtomography to Neuroscience

The assessment of three-dimensional (3D) brain cytoarchitecture at a cellular resolution remains a great challenge in the field of neuroscience and constant development of imaging techniques has become crucial, particularly when it comes to offering direct and clear obtention of data from macro to nano scales. Magnetic resonance imaging (MRI) and electron or optical microscopy, although valuable, still face some issues such as the lack of contrast and extensive sample preparation protocols. In this context, x-ray microtomography (μCT) has become a promising non-destructive tool for imaging a broad range of samples, from dense materials to soft biological specimens. It is a new supplemental method to be explored for deciphering the cytoarchitecture and connectivity of the brain. This review aims to bring together published works using x-ray μCT in neurobiology in order to discuss the achievements made so far and the future of this technique for neuroscience.
Improving Emotion Regulation Through Real-Time Neurofeedback Training on the Right Dorsolateral Prefrontal Cortex: Evidence From Behavioral and Brain Network Analyses

We investigated if emotion regulation can be improved through self-regulation training on non-emotional brain regions, as well as how to change the brain networks implicated in this process. During the training period, the participants were instructed to up-regulate their right dorsolateral prefrontal cortex (rDLPFC) activity according to real-time functional near-infrared spectroscopy (fNIRS) neurofeedback signals, and there was no emotional element. The results showed that the training significantly increased emotion regulation, resting-state functional connectivity (rsFC) within the emotion regulation network (ERN) and frontoparietal network (FPN), and rsFC between the ERN and amygdala; however, training did not influence the rsFC between the FPN and the amygdala. However, self-regulation training on rDLPFC significantly improved emotion regulation and generally increased the rsFCs within the networks; the rsFC between the ERN and amygdala was also selectively increased. The present study also described a safe approach that may improve emotion regulation through self-regulation training on non-emotional brain regions.
α-Synuclein Up-regulates Monoamine Oxidase A Expression and Activity via Trans-Acting Transcription Factor 1

Abnormal α-Synuclein (α-SYN) aggregates are the pathological hallmarks of Parkinson’s disease (PD), which may affect dopamine (DA) neuron function and DA metabolism. Monoamine oxidase A (MAOA) is an enzyme located on the outer mitochondrial membrane that catalyzes the oxidative deamination of DA. Both α-SYN and MAOA are associated with PD pathogenesis, suggesting possible crosstalk between these two molecules. In the present study, we aimed to investigate the potential impacts of α-SYN on MAOA function and further explore the underlying mechanisms. Our study showed that overexpression of α-SYN [both wild-type (WT) and A53T] increased MAOA function via upregulating its expression without impacting MAOA stability. Overexpression of α-SYNWT or α-SYNA53T enhanced the transcription activity of the MAOA promoter region containing the binding sites of cell division cycle associated 7 like (R1, a transcriptional repressor of MAOA) and trans-acting transcription factor 1 (Sp1, a transcription factor of MAOA). Interestingly, α-SYN selectively increased Sp1 expression, thereby enhancing the binding capacity of Sp1 with MAOA promoter to increase MAOA expression. Taken together, our findings demonstrate that α-SYN can upregulate MAOA expression via modulation of Sp1 and may shed light on future studies of α-SYN associated PD pathogenesis.
Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning

On the road toward artificial general intelligence (AGI), two solution paths have been explored: neuroscience-driven neuromorphic computing such as spiking neural networks (SNNs) and computer-science-driven machine learning such as artificial neural networks (ANNs). Owing to availability of data, high-performance processors, effective learning algorithms, and easy-to-use programming tools, ANNs have achieved tremendous breakthroughs in many intelligent applications. Recently, SNNs also attracted a lot of attention due to its biological plausibility and the possibility of achieving energy-efficiency (Roy et al., 2019). However, they suffer from ongoing debates and skepticisms due to worse accuracy compared to “standard” ANNs. The performance gap comes from a variety of factors, including learning techniques, benchmarks, programming tools and execution hardware, all of which in SNNs are not as developed as those in the ANN domain. To this end, we propose a Research Topic, named “Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning,” in Frontiers in Neuroscience and Frontiers in Computational Neuroscience to collect recent researches on neuromorphic computing and machine learning to help understand and bridge the aforementioned gap. We received 18 submissions in total and accepted 14 of them in the end. The scope of these accepted papers covers learning algorithms, applications, and efficient hardware.
Continuous and Intermittent Artificial Gravity as a Countermeasure to the Cognitive Effects of 60 Days of Head-Down Tilt Bed Rest

Environmental and psychological stressors can adversely affect astronaut cognitive performance in space. This study used a 6° head-down tilt bed rest (HDBR) paradigm to simulate some of the physiologic changes induced by microgravity. Twenty-four participants (mean ± SD age 33.3 ± 9.2 years, N = 16 men) spent 60 consecutive days in strict HDBR. They were studied in three groups of eight subjects each. One group served as Control, whereas the other two groups received either a continuous or intermittent artificial gravity (AG) countermeasure of 30 min centrifugation daily (1 g acceleration at the center of mass and 2 g at the feet). Participants performed all 10 tests of NASA’s Cognition battery and a brief alertness and mood survey repeatedly before, during, and after the HDBR period. Test scores were adjusted for practice and stimulus set difficulty effects. A modest but statistically significant slowing across a range of cognitive domains was found in all three groups during HDBR compared to baseline, most consistently for sensorimotor speed, whereas accuracy was unaffected. These changes were observed early during HDBR and did not further worsen or improve with increasing time in HDBR, except for emotion recognition performance. With increasing time spent in HDBR, participants required longer time to decide which facial emotion was expressed. They were also more likely to select categories with negative valence over categories with neutral or positive valence. Except for workload, which was rated lower in the Control group, continuous or intermittent AG did not modify the effect of HDBR on cognitive performance or subjective responses. Participants expressed several negative survey responses during HDBR relative to baseline, and some of the responses further deteriorated during recovery, which highlights the importance of adequate medical and psychological support during extended duration HDBR studies. In conclusion, 60 days of HDBR were associated with moderate cognitive slowing and changes in emotion recognition performance, but these effects were not mitigated by either continuous or intermittent exposure to AG for 30 min daily.
False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

In the process of the detection of a false data injection attack (FDIA) in power systems, there are problems of complex data features and low detection accuracy. From the perspective of the correlation and redundancy of the essential characteristics of the attack data, a detection method of the FDIA in smart grids based on cyber-physical genes is proposed. Firstly, the principle and characteristics of the FDIA are analyzed, and the concept of the cyber-physical FDIA gene is defined. Considering the non-functional dependency and nonlinear correlation of cyber-physical data in power systems, the optimal attack gene feature set of the maximum mutual information coefficient is selected. Secondly, an unsupervised pre-training encoder is set to extract the cyber-physical attack gene. Combined with the supervised fine-tuning classifier to train and update the network parameters, the FDIA detection model with stacked autoencoder network is constructed. Finally, a self-adaptive cuckoo search algorithm is designed to optimize the model parameters, and a novel attack detection method is proposed. The analysis of case studies shows that the proposed method can effectively improve the detection accuracy and effect of the FDIA on cyber-physical power systems.
A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

Passive acoustic monitoring is a well-established tool for researching the occurrence, movements, and ecology of a wide variety of marine mammal species. Advances in hardware and data collection have exponentially increased the volumes of passive acoustic data collected, such that discoveries are now limited by the time required to analyze rather than collect the data. In order to address this limitation, we trained a deep convolutional neural network (CNN) to identify humpback whale song in over 187,000 h of acoustic data collected at 13 different monitoring sites in the North Pacific over a 14-year period. The model successfully detected 75 s audio segments containing humpback song with an average precision of 0.97 and average area under the receiver operating characteristic curve (AUC-ROC) of 0.992. The model output was used to analyze spatial and temporal patterns of humpback song, corroborating known seasonal patterns in the Hawaiian and Mariana Islands, including occurrence at remote monitoring sites beyond well-studied aggregations, as well as novel discovery of humpback whale song at Kingman Reef, at 5∘ North latitude. This study demonstrates the ability of a CNN trained on a small dataset to generalize well to a highly variable signal type across a diverse range of recording and noise conditions. We demonstrate the utility of active learning approaches for creating high-quality models in specialized domains where annotations are rare. These results validate the feasibility of applying deep learning models to identify highly variable signals across broad spatial and temporal scales, enabling new discoveries through combining large datasets with cutting edge tools.
Anti-AAV Antibodies in AAV Gene Therapy: Current Challenges and Possible Solutions

Adeno-associated virus (AAV) vector-based gene therapy is currently the only in vivo gene therapy approved in the US and Europe. The recent tragic death of three children in a clinical trial to treat X-Linked Myotubular Myopathy by delivering myotubularin with an AAV8 vector notwithstanding, AAV remains a highly promising therapeutic gene delivery platform. But the successful use of AAV vectors to treat an increasing number of diseases also makes establishing protocols to determine therapeutically relevant titers of pre-existing anti-AAV antibodies and approaches to deplete those antibodies more urgent than ever. In this mini review, I will briefly discuss (i) our knowledge regarding the prevalence of anti-AAV antibodies, (ii) the challenges to measure those antibodies by methods that are most predictive of their influence on therapeutic efficacy of AAV gene transfer, and (iii) approaches to overcome the formidable hurdle that anti-AAV antibodies pose to the successful clinical use of AAV gene therapy.
Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.
Deletion of the Thrombin Proteolytic Site in Neurofascin 155 Causes Disruption of Nodal and Paranodal Organization

In the central nervous system, myelin is attached to the axon in the paranodal region by a trimolecular complex of Neurofascin155 (NF155) in the myelin membrane, interacting with Caspr1 and Contactin1 on the axolemma. Alternative splicing of a single Neurofascin transcript generates several different Neurofascins expressed by several cell types, but NF155, which is expressed by oligodendrocytes, contains a domain in the third fibronectinIII-like region of the molecule that is unique. The immunoglobulin 5–6 domain of NF155 is essential for binding to Contactin1, but less is known about the functions of the NF155-unique third fibronectinIII-like domain. Mutations and autoantibodies to this region are associated with several neurodevelopmental and demyelinating nervous system disorders. Here we used Crispr-Cas9 gene editing to delete a 9 bp sequence of NF155 in this unique domain, which has recently been identified as a thrombin binding site and implicated in plasticity of the myelin sheath. This small deletion results in dysmyelination, eversion of paranodal loops of myelin, substantial enlargement of the nodal gap, a complete loss of paranodal septate junctions, and mislocalization of Caspr1 and nodal sodium channels. The animals exhibit tremor and ataxia, and biochemical and mass spectrometric analysis indicates that while NF155 is transcribed and spliced normally, the NF155 protein is subsequently degraded, resulting in loss of the full length 155 kDa native protein. These findings reveal that this 9 bp region of NF155 in its unique third fibronectinIII-like domain is essential for stability of the protein.
Bacteria From the Southern Gulf of Mexico: Baseline, Diversity, Hydrocarbon-Degrading Potential and Future Applications

The Gulf of Mexico Research Consortium (Consorcio de Investigación del Golfo de México (CIGoM), 2020) was founded in 2015 as a consortium of scientific research and consulting services, specializing in multidisciplinary projects related to the potential environmental impacts of natural and human-induced oil spills in marine ecosystems, to understand and act in the case of possible large-scale oil spills in the Gulf of Mexico. CIGoM comprises more than 300 specialized researchers trained at the most recognized Mexican institutions. Among the main interests of CIGoM are developing the first baseline of the bacterial community inhabiting the southern Gulf of Mexico, investigating the natural degradation of hydrocarbons by bacterial communities and microbial consortia and identifying and characterizing industrially relevant enzymes. In this review, using third-generation sequencing methodologies coupled to function screening methodologies, we report the bacterial profile found in samples of water and sediments in Mexican regions that include the Perdido Fold Belt (northwest of Mexico), Campeche Knolls (in the southeast) and Southwest region of the Gulf of Mexico. We also highlight some examples of novel lipases and dioxygenases with high biotechnological potential and some culturable hydrocarbon-degrading strains used in diverse bioremediation processes.