Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, hierarchical, interregional structure with a highly specific laminar architecture. The last decade has seen the emergence of extensive new data sets on anatomy and connectivity at the whole brain scale, providing promising new directions for studies of cortical function that take into account the inseparability of whole-brain and microcircuit architectures. Here, we present a datadriven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, which integrates whole-brain scale data while providing cellular and subcellular specificity. This multiscale integration was achieved by building the morphologically detailed model of cortical circuitry embedded within a volumetric, digital brain atlas. The model consists of 4.2 million morphologically detailed neurons belonging to 60 different morphological types, placed in the nonbarrel subregions of the Paxinos and Watson atlas. They are connected by 13.2 billion synapses determined by axo-dendritic overlap, comprising local connectivity and long-range connectivity defined by topographic mappings between subregions and laminar axonal projection profiles, both parameterized by whole brain data sets. Additionally, we incorporated coreand matrix-type thalamocortical projection systems, associated with sensory and higher-order extrinsic inputs, respectively. An analysis of the modeled synaptic connectivity revealed a highly nonrandom topology with substantial structural differences but also synergy between local and long-range connectivity. Long-range connections featured a more divergent structure with a comparatively small group of neurons serving as hubs to distribute excitation to far away locations. Taken together with analyses at different spatial granularities, these results support the notion that local and interregional connectivity exist on a spectrum of scales, rather than as separate and distinct networks, as is commonly assumed. Finally, we predicted how the emergence of primary sensory cortical maps is constrained by the anatomy of thalamo-cortical projections. A subvolume of the model comprising 211,712 neurons in the front limb, jaw, and dysgranular zone has been made freely and openly available to the community.
Cerebrospinal fluid-contacting neuron tracing reveals structural and functional connectivity for locomotion in the mouse spinal cord

Cerebrospinal fluid-contacting neurons (CSF-cNs) are enigmatic mechano- or chemosensory cells lying along the central canal of the spinal cord. Recent studies in zebrafish larvae and lampreys have shown that CSF-cNs control postures and movements via spinal connections. However, the structures, connectivity, and functions in mammals remain largely unknown. Here we developed a method to genetically target mouse CSF-cNs that highlighted structural connections and functions. We first found that intracerebroventricular injection of adeno-associated virus with a neuron-specific promoter and Pkd2l1-Cre mice specifically labeled CSF-cNs. Single-cell labeling of 71 CSF-cNs revealed rostral axon extensions of over 1800 μm in unmyelinated bundles in the ventral funiculus and terminated on CSF-cNs to form a recurrent circuitry, which was further determined by serial electron microscopy and electrophysiology. CSF-cNs were also found to connect with axial motor neurons and premotor interneurons around the central canal and within the axon bundles. Chemogenetic CSF-cNs inactivation reduced speed and step frequency during treadmill locomotion. Our data revealed the basic structures and connections of mouse CSF-cNs to control spinal motor circuits for proper locomotion. The versatile methods developed in this study will contribute to further understanding of CSF-cNs functions in mammals.
Retinoid acid receptor β mechanically regulates the activity of pancreatic cancer cells

Pancreatic ductal adenocarcinoma (PDAC) is the most common and lethal form of pancreatic cancer, characterised by stromal remodelling, elevated matrix stiffness and high metastatic rate. Retinoids, compounds derived from vitamin A, have a history of clinical use in cancer for their anti-proliferative and differentiation effects, and more recently have been explored as anti-stromal therapies in PDAC for their ability to induce mechanical quiescence in cancer associated fibroblasts. Here we demonstrate that retinoic acid receptor β (RAR-β) transcriptionally represses myosin light chain 2 (MLC-2) expression, a key regulatory component of the contractile actomyosin machinery. In turn, MLC-2 downregulation results in decreased cytoskeletal stiffness and traction force generation, impaired response to mechanical stimuli via mechanosensing and reduced ability to invade through the basement membrane.
EEG signature of breaks in embodiment in VR

The brain mechanism of embodiment in a virtual body has grown a scientific interest recently, with a particular focus on providing optimal virtual reality (VR) experiences. Disruptions from an embodied state to a less- or non-embodied state, denominated Breaks in Embodiment (BiE), are however rarely studied despite their importance for designing interactions in VR. Here we use electroencephalography (EEG) to monitor the brain’s reaction to a BiE, and investigate how this reaction depends on previous embodiment conditions. The experimental protocol consisted of two sequential steps; an induction step where participants were either embodied or non-embodied in an avatar, and a monitoring step where, in some cases, participants saw the avatar’s hand move while their hand remained still. Our results show the occurrence of error-related potentials linked to observation of the BiE event in the monitoring step. Importantly, this EEG signature shows amplified potentials following the non-embodied condition, which is indicative of an accumulation of errors across steps. These results provide neurophysiological indications on how progressive disruptions impact the expectation of embodiment for a virtual body.
Oncogenic ERRB2 signals through the AP-1 transcription factor to control mesenchymal-like properties of oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is a deadly disease with poor survival statistics and few targeted therapies available. One of the most common molecular aberrations in OAC is amplification or activation of the gene encoding the receptor tyrosine kinase ERBB2, and ERBB2 is targeted in the clinic for this subset of patients. However, the downstream consequences of these ERBB2 activating events are not well understood. Here we used a combination of phosphoproteomics, open chromatin profiling and transcriptome analysis on cell line models and patient-derived datasets to interrogate the molecular pathways operating downstream from ERBB2. Integrated analysis of these data sets converge on a model where dysregulated ERBB2 signalling is mediated at the transcriptional level by the transcription factor AP-1. AP-1 in turn controls cell behaviour by acting on cohorts of genes that regulate cell migration and adhesion, features often associated with EMT. Our study therefore provides a valuable resource for the cancer cell signalling community and reveals novel molecular determinants underlying the dysregulated behaviour of OAC cells.
Identification of β2 microglobulin, the product of B2M gene, as a Host Factor for Vaccinia Virus Infection by Genome-Wide CRISPR genetic screens

Genome-wide genetic screens are powerful tools to identify genes that act as host factors of viruses. We have applied this technique to the analyze the infection of HeLa cells by Vaccinia virus, in an attempt to find genes necessary for infection. Infection of cell populations harboring single gene inactivations resulted in no surviving cells, suggesting that no single gene knock-out was able to provide complete resistance to Vaccinia virus and thus allow cells to survive infection. In the absence of an absolute infection blockage, we explored if some gene inactivations could provide partial protection leading to a reduced probability of infection. Multiple experiments using modified screening procedures involving replication restricted viruses led to the identification of multiple genes whose inactivation potentially increase resistance to infection and therefore cell survival. As expected, significant gene hits were related to proteins known to act in virus entry, such as ITGB1 and AXL as well as genes belonging to their downstream related pathways. Additionally, we consistently found β2-microglobulin, encoded by the B2M gene, among the screening top hits, a novel finding that was further explored. Inactivation of B2M resulted in 54% and 91% reduced VV infection efficiency in HeLa and HAP1 cell lines respectively. In the absence of B2M, while virus binding to the cells was unaffected, virus internalization and early gene expression were significantly diminished. These results point to β2-microglobulin as a relevant factor in the Vaccinia virus entry process. Author summary Orthopoxviruses, a genus belonging to the family Poxviridae, include human pathogens like Variola virus, the causative agent of the now eradicated Smallpox, and Monkeypox virus that cause human outbreaks of zoonotic origin. Being the prototype Poxvirus, Vaccinia virus has been extensively used as the ideal model to study infection. For Poxviruses, both fluid phase endocytosis and direct fusion at the plasma membrane have been described as modes of entry. To date, only a few cellular factors have been identified in the vaccinia virus entry pathway. In this study, we report that blind genome-wide genetic screens allowed us to identify several cellular factors involved in Vaccinia Virus infection, of which many could be related to known factors in virus entry. In addition, we found that β2-microglobulin constitute a novel player for Poxvirus entry not related to previously described cellular pathways involved in the entry process. These findings add new information to the complex picture of Poxvirus entry and open the door to the discovery of new entry mechanisms used by Poxviruses.
Dendritic cell-natural killer cell cross-talk modulates T cell activation in response to influenza A viral infection

Influenza viruses lead to substantial morbidity and mortality including ~3-5 million cases of severe illness and ~290,000-650,000 deaths annually. One of the major hurdles regarding influenza vaccine efficacy is generating a durable, robust cellular immune response. Appropriate stimulation of the innate immune system is key to generating cellular immunity. Crosstalk between innate dendritic cells (DC) and natural killer (NK) cells plays a key role in activating virus-specific T cells, yet the mechanisms used by influenza A viruses (IAV) to govern this process remain incompletely understood. Here, we used an ex vivo autologous human primary immune cell culture system to evaluate the impact of genetically distinct IAV strains on DC-NK cell crosstalk and subsequent T cell activation. We report that the addition of NK cells to cultures containing both DCs and naïve T cells led to an increase in the frequency of CD69+ and CD25+ T cells and elevated levels of IFN-γ, TNF, and IL-10. However, upon IAV infection of DCs, the addition of NK cells to cultures no longer increased the frequency of CD25+ T cells nor elevated IFN-γ, TNF, and IL-10 cytokine levels. Investigation of the impact of IAV infection on DC-NK crosstalk revealed that exposure of DCs to influenza virus in co-culture led to an increased frequency of HLA-DR+ and a decreased frequency of CD83+ and CD86+ cells–molecules involved in stimulating T cell activation. An expansion of an HLA-DR+ NK cell subset was observed following culture with influenza-infected DCs in a contact-dependent and cytokine independent-manner. Overall, our results indicate a role for DC-NK cell crosstalk in T cell priming in the context of influenza infection, informing the immunological mechanisms that could be manipulated for the next generation influenza vaccine or immunotherapeutic.
HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning

Randomized least-square value iteration (RLSVI) is a provably efficient exploration method. However, it is limited to the case where (1) a good feature is known in advance and (2) this feature is fixed during the training. If otherwise, RLSVI suffers an unbearable computational burden to obtain the posterior samples. In this work, we present a practical algorithm named HyperDQN to address the above issues under deep RL. In addition to a non-linear neural network (i.e., base model) that predicts Q-values, our method employs a probabilistic hypermodel (i.e., meta model), which outputs the parameter of the base model. When both models are jointly optimized under a specifically designed objective, three purposes can be achieved. First, the hypermodel can generate approximate posterior samples regarding the parameter of the Q-value function. As a result, diverse Q-value functions are sampled to select exploratory action sequences. This retains the punchline of RLSVI for efficient exploration. Second, a good feature is learned to approximate Q-value functions. This addresses limitation (1). Third, the posterior samples of the Q-value function can be obtained in a more efficient way than the existing methods, and the changing feature does not affect the efficiency. This deals with limitation (2). On the Atari suite, HyperDQN with 20M frames outperforms DQN with 200M frames in terms of the maximum human-normalized score. For SuperMarioBros, HyperDQN outperforms several exploration bonus and randomized exploration methods on 5 out of 9 games.
Optimization inspired Multi-Branch Equilibrium Models

Works have shown the strong connections between some implicit models and optimization problems. However, explorations on such relationships are limited. Most works pay attention to some common mathematical properties, such as sparsity. In this work, we propose a new type of implicit model inspired by the designing of the systems’ hidden objective functions, called the Multi-branch Optimization induced Equilibrium networks (MOptEqs). The model architecture is designed based on modelling the hidden objective function for the multi-resolution recognition task. Furthermore, we also pro-pose a new strategy inspired by our understandings of the hidden objective function. In this manner, the proposed model can better utilize the hierarchical patterns for recognition tasks and retain the abilities for interpreting the whole structure as trying to obtain the minima of the problem’s goal. Comparing with the state-of-the-art models, our MOptEqs not only enjoys better explainability but are also superior to MDEQ with less parameter consumption and better performance on practical tasks. Furthermore, we also implement various experiments to demonstrate the eﬀectiveness of our new methods and explore the applicability of the model’s hidden objective function.
Dorsolateral striatum, not motor cortex, is a bottleneck for responding to task-relevant stimuli in a learned whisker detection task in mice

A learned sensory-motor behavior engages multiple brain regions, including the neocortex and the basal ganglia. How a target stimulus is selected by these regions remains poorly understood. Here, we performed electrophysiological recordings and pharmacological inactivations of motor cortex and dorsolateral striatum to determine the representations within and functions of each region during performance in a selective whisker detection task in male and female mice. From the recording experiments, peak pre-response activity and significant choice probability emerged in the motor cortex before the dorsolateral striatum, suggesting a sensory-to-motor transformation in which the striatum is downstream of motor cortex. We performed pharmacological inactivation studies to determine the necessity of these brain regions for this task. We found that suppressing the dorsolateral striatum, but not motor cortex, severely disrupts responding to task-relevant stimuli, without disrupting the ability to respond. Together these data support the dorsolateral striatum, and not motor cortex, as an essential node in the sensory-to- motor transformation of this whisker detection task. Significance Statement We learn to do various sensory-motor behavior in our daily life, such as clicking on a journal article that looks interesting, among other articles. There are parts of our brain that are active when we do these learned behaviors, such as motor cortex and basal ganglia. But what is the order of activation of these regions? Which of them is necessary for responding to task-relevant sensory information? To answer these questions, we trained mice in a whisker-based target selection task and used recording of neural activity and inactivation of subregions within motor cortex and basal ganglia in expert mice. Our findings show dorsolateral striatum, a region within basal ganglia, is a bottleneck for performing task-related sensory-to-motor transformation.
Model-augmented Prioritized Experience Replay

Experience replay is an essential component in off-policy model-free reinforcement learning (MfRL). Due to its effectiveness, various methods for calculating priority scores on experiences have been proposed for sampling. Since critic networks are crucial to policy learning, TD-error, directly correlated to Q-values, is one of the most frequently used features to compute the scores. However, critic networks often underor overestimate Q-values, so it is often ineffective to learn for predicting Q-values by sampled experiences based heavily on TD-error. Accordingly, it is valuable to find auxiliary features, which positively support TD-error in calculating the scores for efficient sampling. Motivated by this, we propose a novel experience replay method, which we call model-augmented prioritized experience replay (MaPER), that employs new learnable features driven from components in modelbased RL (MbRL) to calculate the scores on experiences. The proposed MaPER brings the effect of curriculum learning for predicting Q-values better by the critic network with negligible memory and computational overhead compared to the vanilla PER. Indeed, our experimental results on various tasks demonstrate that MaPER can significantly improve the performance of the state-of-the-art offpolicy MfRL and MbRL which includes off-policy MfRL algorithms in its policy optimization procedure.
Algorithm 944

We present Talbot Suite, a C parallel software collection for the numerical inversion of Laplace Transforms, based on Talbot's method. It is designed to fit both single and multiple Laplace inversion problems, which arise in several application and research fields. In our software, we achieve high accuracy and efficiency, making full use of modern architectures and introducing two different levels of parallelism: coarse and fine grained parallelism. They offer a reasonable tradeoff between accuracy, the main aspect for a few inversions, and efficiency, the main aspect for multiple inversions. To take into account modern high-performance computing architectures, Talbot Suite provides different software versions: an OpenMP-based version for shared memory machines and a MPI-based version for distributed memory machines. Moreover, oriented to hybrid architectures, a combined MPI/OpenMP-based implementation is provided too. We describe our parallel algorithms and the software organization. We also report some performance results. Our software includes sample programs to call the Talbot Suite functions from C and from MATLAB.
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training

Designing accurate and efﬁcient vision transformers (ViTs) is an important but challenging task. Supernet-based one-shot neural architecture search (NAS) enables fast architecture optimization and has achieved state-of-the-art results on convolutional neural networks (CNNs). However, directly applying the supernet-based NAS to optimize ViTs leads to poor performance - even worse compared to training single ViTs. In this work, we observe that the poor performance is due to a gradient conﬂict issue: the gradients of different sub-networks conﬂict with that of the supernet more severely in ViTs than CNNs, which leads to early saturation in training and inferior convergence. To alleviate this issue, we propose a series of techniques, including a gradient projection algorithm, a switchable layer scaling design, and a simpliﬁed data augmentation and regularization training recipe. The proposed techniques signiﬁcantly improve the convergence and the performance of all sub-networks. Our discovered hybrid ViT model family, dubbed NASViT, achieves top-1 accuracy from 78.2% to 81.8% on ImageNet from 200M to 800M FLOPs, and outperforms all the prior art CNNs and ViTs, including AlphaNet and LeViT. When transferred to semantic segmentation tasks, NASViTs also out-perform previous backbones on both Cityscape and ADE20K datasets, achieving 73.2% and
Relational Multi-Task Learning: Modeling Relations between Data and Tasks

A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point's labels from other tasks. This presents an opportunity to extend multi-task learning to utilize data point's labels from other auxiliary tasks, and this way improves performance on the new task. Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task. We develop MetaLink, where our key innovation is to build a knowledge graph that connects data points and tasks and thus allows us to leverage labels from auxiliary tasks. The knowledge graph consists of two types of nodes: (1) data nodes, where node features are data embeddings computed by the neural network, and (2) task nodes, with the last layer's weights for each task as node features. The edges in this knowledge graph capture data-task relationships, and the edge label captures the label of a data point on a particular task. Under MetaLink, we reformulate the new task as a link label prediction problem between a data node and a task node. The MetaLink framework provides flexibility to model knowledge transfer from auxiliary task labels to the task of interest. We evaluate MetaLink on 6 benchmark datasets in both biochemical and vision domains. Experiments demonstrate that MetaLink can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC.
NASPY: Automated Extraction of Automated Machine Learning Models

We present NASPY , an end-to-end adversarial framework to extract the network architecture of deep learning models generated by Neural Architecture Search (NAS). Existing model extraction attacks mainly focus on conventional DNN models with very simple operations, or require heavy manual analysis with lots of prior knowledge. In contrast, NASPY introduces seq2seq models to automatically identify novel and complicated operations (e.g., separable convolution, dilated convolution) from hardware side-channel sequences of model inference. We design two models (RNN-CTC and transformer), which can achieve only 3.2% and 11.3% error rates for operation prediction. We further present methods to recover the model hyper-parameters and topology from the operation sequence. With these techniques, NASPY is able to extract the complete NAS model architecture with high fidelity and automation, which are rarely analyzed before.