Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms

Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by leveraging the unique hardware-level imperfections imposed on the received wireless signal by the transmitter's radio circuitry. Most of existing approaches utilize hand-tailored protocol-specific feature extraction techniques, which can identify devices operating under a pre-defined wireless protocol only. Conversely, by mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard. One of the most crucial challenges in radio fingerprinting is to counteract the action of the wireless channel, which decreases fingerprinting accuracy significantly by disrupting hardware impairments. On the other hand, due to their sheer size, deep learning algorithms are hardly re-trainable in real-time. Another aspect that is yet to be investigated is whether an adversary can successfully impersonate another device's fingerprint. To address these key issues, this paper proposes DeepRadioID, a system to optimize the accuracy of deep-learning-based radio fingerprinting algorithms without retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter's side, we can apply tiny modifications to the waveform to strengthen its fingerprint according to the current channel conditions. We mathematically formulate the Waveform Optimization Problem (WOP) as the problem of finding, for a given trained neural network, the optimum FIR to be used by the transmitter to improve its fingerprinting accuracy. We extensively evaluate DeepRadioID on a experimental testbed of 20 nominally-identical software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices provided by the DARPA RFMLS program. Experimental results show that DeepRadioID (i) increases fingerprinting accuracy by about 35%, 50% and 58% on the three scenarios considered; (ii) decreases an adversary's accuracy by about 54% when trying to imitate other device's fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset.
Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion

Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In Part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In Part 2, sixteen individuals performed plantarflexion at three ankle angles while we recorded electromyographic signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable with no significant differences in the distribution of motor neuron weights across ankle angles (p=0.62). When the cross-correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. Key points summary The central nervous system may generate movements by activating groups of motor neurons (synergies) with common inputs. We show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions. We report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e., the ankle angle. These results suggest the functional relevance of the modular organization of the central nervous system to control movements.
Consequences of blindness for neural dynamics in mouse visual cortex

Early-life blindness causes lasting visual impairment, for which the circuit basis is only partially understood. Degradation of visual connections as well as the network dynamics supporting neural oscillations and arousal states are likely contributors. To define how blindness affects dynamics, we examined the effects of two forms of blindness, bilateral loss of retinal input (enucleation) and degradation of visual input (eyelid-suture), on emergent network properties and their state-dependence in the visual cortex of awake head-fixed mice. Neither form of early visual deprivation fundamentally altered the state-dependent regulation of firing-rates or local field potential oscillations. However, each form of deprivation did cause a unique set of changes in network behavior. Enucleation caused a loss of low-frequency synchronization specifically during movement, suggesting a mouse model for human alpha oscillations. Neurons were also less correlated and fired more regularly, with no change in mean firing rates. Chronic lid-suture decreased firing rates during quiet-wakefulness, but not during movement, and had no effect on neural correlations or firing regularity. Sutured animals also had a broadband increase in LFP power and increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary, rather than additive, effects of lid-suture vs.enucleation suggest that the development of these emergent network properties does not require vision but is plastic to modified input. Our results suggest that the etiology of human blindness will be a crucial determinant of circuit pathology and its capacity to respond to clinical interventions. SIGNIFICANCE STATEMENT Early life blindness alters brain circuits in a way that prevents a full return to normal vision even when sight is restored. We examined the circuit changes caused by two forms of blindness in the mouse as reflected in the spontaneous behavior (network dynamics) of the visual cortex. We found that, as in humans, vision is largely unnecessary for the establishment of cortical network dynamics and their modulation by cortical states like arousal. However, blindness did induce several important changes, such as loss of alpha rhythms. There was no overlap in the changes caused by each deprivation, suggesting that etiology matters. Understanding the lasting consequences of early blindness on neural function is essential for treatment of devastating early visual disorders.
Mutation-Guided Vaccine Design: A Strategy for Developing Boosting Immunogens for HIV Broadly Neutralizing Antibody Induction

A major goal of HIV-1 vaccine development is induction of broadly neutralizing antibodies (bnAbs). While success has been achieved in initiating bnAb B cell lineages, design of boosting immunogens that select for bnAb B cell receptors with improbable mutations required for bnAb affinity maturation remains difficult. Here we demonstrate a process for designing boosting immunogens for a V3-glycan bnAb B cell lineage. The immunogens induced affinity-matured antibodies by selecting for functional improbable mutations in bnAb precursor knock-in mice. Moreover, we show similar success in prime and boosting with nucleoside-modified mRNA-encoded HIV-1 envelope trimer immunogens, with improved selection by mRNA immunogens of improbable mutations required for bnAb binding to key envelope glycans. These results demonstrate the ability of both protein and mRNA prime-boost immunogens for selection of rare B cell lineage intermediates with neutralizing breadth after bnAb precursor expansion, a key proof-of concept and milestone towards development of an HIV vaccine. One-Sentence Summary A vaccine strategy for selecting key rare antibody mutations is shown to induce HIV broadly neutralizing antibodies in mice.
Neuromodulatory organization in the developing rat somatosensory cortex

The vast majority of cortical synapses are found in the neuropil which is implicated in multiple and diverse functions underlying brain computation. Unraveling the organizing principles of the cortical neuropil requires an intricate characterization of synaptic connections established by excitatory and inhibitory axon terminals, of intrinsic and extrinsic origin and from ascending projections that govern the function of cortical microcircuits through the release of neuromodulators either through point-to-point chemical synapses or diffuse volume transmission (VT). Even though neuromodulatory release has been studied for almost a century it is still not clear if one modality prevails upon the other. The hindlimb representation of the somatosensory cortex (HLS1) of two-week old Wistar rats has served as a model system to dissect the microcircuitry of neurons and their synaptic connections. In the present study, we quantified the fiber length per cortical volume and the density of varicosities for cholinergic, catecholaminergic and serotonergic neuromodulatory systems in the cortical neuropil using immunocytochemical staining and stereological techniques. Acquired data were integrated into a novel computational framework to reconcile the specific modalities and predict the effects of neuromodulatory release in shaping neocortical network activity. We predict that acetylcholine (ACh), dopamine (DA), serotonin (5-HT) release desynchronizes cortical activity by inhibiting slow oscillations (delta range), and that 5-HT triggers faster oscillations (theta). Moreover, we found that high levels (>40%) of neuromodulatory VT are sufficient to induce network desynchronization, but also that combining volume release with synaptic inputs leads to more robust and stable effects, meaning that lower levels of VT are needed to achieve the same outcome (10%).
FINDEL: A Deep Learning Approach to Efficient Artifact Removal From Cancer Genomes

Next-generation sequencing technologies have increased sequencing throughput by 100-1000 folds and subsequently reduced the cost of sequencing a human genome to approximately US$1,000. However, the existence of sequencing artifacts can cause erroneous identification of variants and adversely impact the downstream analyses. Currently, the manual inspection of variants for additional refinement is still necessary for high-quality variant calls. The inspection is usually done on large binary alignment map (BAM) files which consume a huge amount of labor and time. It also suffers from a lack of standardization and reproducibility. Here we show that the use of mutational signatures coupled with deep learning can replace the current standards in the bioinformatics workflow. This software, called FINDEL, can efficiently remove sequencing artifacts from cancer samples. It queries the variant call format file which is much more compact than BAM files. The software automates the variant refinement process and produces high-quality variant calls.
Developmental and MAPK-responsive transcription factors drive distinct malignant subtypes and genetic dependencies in pancreatic cancer

Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of regulatory protein activity in single cells identified three PDA Developmental Lineages (PDLs), coexisting in virtually all tumors, whose transcriptional states are mechanistically driven by aberrant activation of Master Regulator (MR) proteins associated with gastrointestinal lineages (GLS state), morphogen and EMT pathways (MOS state), and acinar-to-ductal metaplasia (ALS state), respectively. Each PDL is further subdivided into sub-states characterized by low vs. high MAPK pathway activity. This taxonomy was remarkably conserved across multiple cohorts, cell lines, and PDX models, and harmonized with bulk profile analyses. Cross-state plasticity and MR essentiality was confirmed by barcode-based lineage tracing and CRISPR/Cas9 assays, respectively, while MR ectopic expression induced PDL transdifferentiation. Together these data provide a mechanistic foundation for PDA heterogeneity and a roadmap for targeting PDA cellular subtypes.
Different Control Strategies Drive Interlimb Differences in Performance and Adaptation during Reaching Movements in Novel Dynamics

Abstract Humans exhibit lateralization such that most individuals typically show a preference for using one arm over the other for a range of movement tasks. The computational aspects of movement control leading to these differences in skill are not yet understood. It has been hypothesized that the dominant and nondominant arms differ in terms of the use of predictive or impedance control mechanisms. However, previous studies present confounding factors that prevented clear conclusions: either the performances were compared across two different groups, or in a design in which asymmetrical transfer between limbs could take place. To address these concerns, we studied a reach adaptation task during which healthy volunteers performed movements with their right and left arms in random order. We performed two experiments. Experiment 1 (18 participants) focused on adaptation to the presence of a perturbing force field (FF) and experiment 2 (12 participants) focused on rapid adaptations in feedback responses. The randomization of the left and right arm led to simultaneous adaptation, allowing us to study lateralization in single individuals with symmetrical and minimal transfer between limbs. This design revealed that participants could adapt control of both arms, with both arms showing similar performance levels. The nondominant arm initially presented a slightly worst performance but reached similar levels of performance in late trials. We also observed that the nondominant arm showed a different control strategy compatible with robust control when adapting to the force field perturbation. EMG data showed that these differences in control were not caused by differences in co-contraction across the arms. Thus, instead of assuming differences in predictive or reactive control schemes, our data show that in the context of optimal control, both arms can adapt, and that the nondominant arm uses a more robust, model-free strategy likely to compensate for less accurate internal representations of movement dynamics.
Expression bias in retinoic acid responsive genes defines variations in neural differentiation of human pluripotent stem cells

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. We identified differences in the early lineage emergence across hPSC lines that mapped on the antero-posterior axis of embryonic development. RNA-seq analysis revealed dynamic transcriptomic patterns that defined the emergence of mesendodermal versus neuroectodermal lineages conserved across hPSC lines and cell line-specific transcriptional signatures that were invariant across differentiation. The stable cell line-specific transcriptomic patterns predicted the retinoic acid (RA) response of the cell lines, resulting in distinct bias towards fore-versus hind-brain fates. Replicate hPSC lines and paired adult donor tissue demonstrated that cells from individual humans expressed unique and long-lasting transcriptomic signatures associated with evolutionarily recent genes. In addition to this genetic contribution, we found that replicate lines from a single donor showed divergent brain regional fates linked to distinct chromatin states, indicating that epigenetic mechanisms also contribute to neural fate differences. This variation in lineage bias and its correlation with RA responsive gene expression was also observed in a large collection of hPSC lines. These results define transcriptomic differences in hPSCs that initiate a critical early step specifying anterior or posterior neural fates.
Convergent evolution and B-cell recirculation in germinal centers in a human lymph node

The study characterizes an ongoing immune response by sequencing B-cells from 10 germinal centers and finds diverse clonal patterns and convergent evolution, indicating re-engagement and rediversification of B-cell clones across GCs. Germinal centers (GCs) play a central role in generating an effective immune response against infectious pathogens, and failures in their regulating mechanisms can lead to the development of autoimmune diseases and cancer. Although previous works study experimental systems of the immune response with mouse models that are immunized with specific antigens, our study focused on a real-life situation, with an ongoing GC response in a human lymph node (LN) involving multiple asynchronized GCs reacting simultaneously to unknown antigens. We combined laser capture microdissection of individual GCs from human LN with next-generation repertoire sequencing to characterize individual GCs as distinct evolutionary spaces. In line with well-characterized GC responses in mice, elicited by immunization with model antigens, we observe a heterogeneous clonal diversity across individual GCs from the same human LN. Still, we identify shared clones in several individual GCs, and phylogenetic tree analysis combined with paratope modeling suggest the re-engagement and rediversification of B-cell clones across GCs and expanded clones exhibiting shared antigen responses across distinct GCs, indicating convergent evolution of the GCs.
A machine-learning tool to identify bistable states from calcium imaging data

Mapping neuronal activation using calcium imaging in vivo during behavioral tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities since action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. While a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons (PNs) in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In this manuscript we develop a tool, CaMLSort which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analyzing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviors. Key Points Summary Calcium imaging – the gold standard of inferring neuronal activity – does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool offers a new way of analyzing calcium imaging data from populations of bistable neurons, thereby facilitating our understanding of how these neurons carry out their functions in a circuit.
Single-cell gene expression and chromatin accessibility profiling of human pancreatic islets at basal and stimulatory conditions nominates mechanisms of type 1 diabetes genetic risk

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the loss of pancreatic islet beta cells. The mechanisms of T1D genetic risk remain poorly understood. Here, we present a multi-omic integrative study of single-cell/nucleus molecular profiles of gene expression and chromatin accessibility in the same biological samples from healthy and beta cell autoantibody+ (AAB+) human pancreatic islets to characterize mechanisms of islet-mediated T1D genetic risk. We additionally performed single-cell/nucleus multi-omic profiling of healthy islets under two stimulatory conditions used as in vitro models of T1D (cytokine cocktail and CVB4 infection) to evaluate how environmental exposures recapitulate multi-omic signatures of T1D. In total, we analyzed 121,272 cells/nuclei across 34 libraries, identifying 10 distinct cell types. We identified cell-type-specific and disease-associated cis-regulatory elements and nominated likely target genes. We provide evidence that T1D genetic risk is mediated through multiple pancreatic cell populations, including islet endocrine cells (beta, alpha, gamma, and delta), exocrine acinar and ductal cells, and immune cells. Finally, we identified three independent T1D risk variants acting through pancreatic islet endocrine cells at the TOX, RASGRP1, and DLK1/MEG3 loci. Together, this work improves our understanding of how non-coding genetic variants encode T1D risk through a complex interplay of different cell types in the pancreas.