Miracle-dz/UniFAR-FTUs huggingface dataset:
Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems

—In medical cyber-physical systems (CPS), where patient safety is a top priority, the robustness of learning-enabled components (LECs) becomes crucial. Therefore, a comprehensive robustness evaluation is necessary for the successful deployment of these systems. Existing research predominantly focuses on robustness to synthetic adversarial examples , crafted by adding imperceptible perturbations to clean input data. However, these synthetic adversarial examples do not accurately reflect the most challenging real-world scenarios, especially in the context of healthcare data. Consequently, robustness to synthetic adversarial examples may not necessarily translate to robustness against naturally occurring adversarial examples . We propose a method to curate datasets comprised of natural adversarial examples to evaluate the robustness of LECs. The method relies on probabilistic labels obtained from automated weakly-supervised labeling that combines noisy and cheap-to-obtain labeling heuristics. Based on these labels, our method adversarially orders the input data and uses this ordering to construct a sequence of increasingly adversarial datasets. Our evaluation on six medical CPS case studies and three non-medical case studies demonstrates the efficacy and statistical validity of our approach to generating naturally adversarial datasets.
Transcriptional Control of Neocortical Size and Microcephaly

The mammalian neocortex differs vastly in size and complexity between mammalian species, yet the mechanisms that lead to an increase in brain size during evolution are not known. We show here that two transcription factors coordinate gene expression programs in progenitor cells of the neocortex to regulate their proliferative capacity and neuronal output in order to determine brain size. Comparative studies in mice, ferrets and macaques demonstrate an evolutionary conserved function for these transcription factors to regulate progenitor behaviors across the mammalian clade. Strikingly, the two transcriptional regulators control the expression of large numbers of genes linked to microcephaly suggesting that transcriptional deregulation as an important determinant of the molecular pathogenesis of microcephaly, which is consistent with the finding that genetic manipulation of the two transcription factors leads to severe microcephaly. Summary The neocortex varies in size and complexity among mammals due to the tremendous variability in the number and diversity of neuronal subtypes across species1,2. The increased cellular diversity is paralleled by the expansion of the pool of neocortical progenitors2–5 and the emergence of indirect neurogenesis6 during brain evolution. The molecular pathways that control these biological processes and are disrupted in neurological and psychiatric disorders remain largely unknown. Here we show that the transcription factors BRN1 (POU3F3) and BRN2 (POU3F2) act as master regulators of the transcriptional programs in progenitors linked to neuronal specification and neocortex expansion. Using genetically modified lissencephalic and gyrencephalic animals, we found that BRN1/2 establish transcriptional programs in neocortical progenitors that control their proliferative capacity and the switch from direct to indirect neurogenesis. Functional studies in genetically modified mice and ferrets show that BRN1/2 act in concert with NOTCH and primary microcephaly genes to regulate progenitor behavior. Analysis of transcriptomics data from genetically modified macaques provides evidence that these molecular pathways are conserved in non-human primates. Our findings thus establish a mechanistic link between BRN1/2 and genes linked to microcephaly and demonstrate that BRN1/2 are central regulators of gene expression programs in neocortical progenitors critical to determine brain size during evolution.
Land, carbon and biodiversity data for supply chain impact calculations

Monitoring, halting and reversing land conversion is fundamental to meeting international biodiversity and climate targets, and agriculture is the major driver of land conversion. We present an open access set of global data for calculating land use change impacts of agricultural supply chains. These data, originally prepared for the LandGriffon service, include indicators of deforestation, conversion of natural ecosystems, greenhouse gas emissions, and loss of intact or high integrity ecosystems following international standards and guidelines for reporting and target setting in the agriculture, forestry, and land use sector. In order to assign impacts to agricultural production, we prepare data using a spatial adaptation of the statistical Land Use Change (sLUC) accounting approach distributing impact to human activities across the local area using a 50km radius. The results are high resolution global maps of impact per hectare of land occupation. These can then be combined with land footprint data, cropland extent, or productivity maps to calculate land use change related impacts for specific crop volumes sourced from specific regions. Carbon and deforestation results are validated against FAO statistics at the national level.
Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics

Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods, and has quickly become the current state-of-the-art in the field. Nevertheless, tight control of proximity labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein-of-interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split-link design, we applied it to identify protein interactions of the glucocorticoid receptor and SARS-CoV-2 nucleocapsid and NSP7 proteins by TurboID proximity labeling. Our results demonstrate that our T2A split-link provides an opportune control that builds upon previously established control requirements in the field. Motivation In proximity labeling proteomics protein-protein interactions are identified by in vivo biotinylation. However, the current lack of a universally applicable negative control for differential analysis affects accurate mapping of the interactome. To bridge this gap, we conceptualized a system based on the T2A self-cleaving peptide to match expression levels between control and bait protein setups while using the same bait protein. In addition, we implemented a versatile modular cloning system to build mammalian expression vectors for, but not limited to, proximity labeling assays.
Is M1-L121E a good mimic on microbial rhodopsin? A viewpoint from excited-state dynamics

Microbial rhodopsin, an important photoreceptor protein, has been widely used in several fields, such as optogenetics, biotechnology, and biodevices etc. However, current microbial rhodopsins are all transmembrane proteins, which both complicates the investigation on the photoreaction mechanism and limits their further applications. Therefore, a suitable mimic for microbial rhodopsin can not only provide a better model for understanding the mechanism, but also can extend the applications. The human protein CRABPII turns out to be a good template for design mimics on rhodopsin, due to the convenience in synthesis and the stability after mutations. Recently, Geiger et al. designed a new CRABPII-based mimic M1-L121E on microbial rhodopsin with the correct 13-cis (13C) isomerization after irritation. However, it still remains a question how similar it is compared with the natural microbial rhodopsin, in particular in the aspect of the photoreaction dynamics. In this article, we investigated the excited-state dynamics of this mimic by measuring its transient absorption spectra. Our results reveal that there are two components in the solution of mimic M1-L121E at PH=8, known as protonated Schiff base (PSB) and unprotonated Schiff base (USB) states. In both states, the photoreaction process from 13-cis (13C) to all-trans (AT) is faster than that from the inverse direction. In addition, the photoreaction process in PSB state is faster than that in the USB state. In the end, we compared the isomerization time of the PSB state with the properties of the microbial rhodopsin, and confirmed that the mimic M1-L121E indeed captures the main feature of the rhodopsin and is a good model of microbial rhodopsin in the photoreaction dynamics. However, our results also reveal significant differences in the excited-state dynamics of the mimic relative to the natural microbial rhodopsin, including the slower PSB isomerization rates in both 13C-AT and AT-13C directions, as well as the unusual USB photoreaction dynamics at PH=8. Such unique properties have not been observed in the natural rhodopsin, which could further deepen the understanding in photoreaction mechanism of the photosensitive proteins.
Genomic fingerprints of the world’s soil ecosystems

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1512 manually-curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (1) microbial nutrient acquisition across pH gradients; (2) stress, transport, and redox-based processes across changes in soil bulk density; and (3) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes –– more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling. Importance We address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. This reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step towards comprehending the intricate world of soil microorganisms and its role in the global climate.
Indistinguishable network dynamics can emerge from unalike plasticity rules

Synaptic plasticity is thought to be critical for building and maintaining brain circuits. Models of plasticity, or plasticity rules, are typically designed by hand, and evaluated based on their ability to elicit similar neuron or circuit properties to ground truth. While this approach has provided crucial insights into plasticity mechanisms, it is limited in its scope by human intuition and cannot identify all plasticity mechanisms that are consistent with the empirical data of interest. In other words, focusing on individual hand-crafted rules ignores the potential degeneracy of plasticity mechanisms that explain the same empirical data, and may thus lead to inaccurate experimental predictions. Here, we use an unsupervised, adversarial approach to infer plasticity rules directly from neural activity recordings. We show that even in a simple, idealised network model, many mechanistically different plasticity rules are equally compatible with empirical data. Our results suggest the need for a shift in the study of plasticity rules, considering as many degenerate plasticity mechanisms consistent with data as possible, before formulating experimental predictions.
Defining the in vivo role of mTORC1 in thyrocytes by studying the TSC2 conditional knockout mouse model

The thyroid gland is susceptible to abnormal epithelial cell growth, often resulting in thyroid dysfunction. The serine-threonine protein kinase mechanistic target of rapamycin (mTOR) regulates cellular metabolism, proliferation, and growth through two different protein complexes, mTORC1 and mTORC2. The PI3K-Akt-mTORC1 pathway’s overactivity is well associated with heightened aggressiveness in thyroid cancer, but recent studies indicate the involvement of mTORC2 as well. To elucidate mTORC1’s role in thyrocytes, we developed a novel mouse model with mTORC1 gain of function in thyrocytes by deleting Tuberous Sclerosis Complex 2 (TSC2), an intracellular inhibitor of mTORC1. The resulting TPO-TSC2KO mice exhibited a significant reduction in TSC2 levels, leading to a six-fold increase in mTORC1 activity. Thyroid glands of both male and female TPO-TSC2KO mice displayed rapid enlargement and continued growth throughout life, accompanied by heterogeneity among thyroid follicles, larger follicles, increased colloid and epithelium. We observed elevated thyrocyte proliferation as indicated by Ki67 staining and elevated Cyclin D3 expression in the TPO-TSC2KO mice. mTORC1 activation resulted in a progressive downregulation of key genes involved in thyroid hormone (TH) biosynthesis, including thyroglobulin, thyroid peroxidase, and sodium-iodide symporter (NIS), while TTF1, PAX8, and MCT8 mRNA levels remained unaffected. NIS protein expression was also diminished in TPO-TSC2KO mice. Treatment with the mTORC1 inhibitor rapamycin prevented thyroid mass expansion and restored the gene expression alterations in TPO-TSC2KO mice. Although T4, T3 and TSH plasma levels were normal at 2 months of age, a slight decrease in T4 and an increase in TSH levels were observed at 6 and 12 months of age while T3 remained similar in TPO-TSC2KO compared to littermate control mice. TPO-TSC2KO mice aged to 12 months or older developed aberrant thyroid conditions, including follicular hyperplasia, inflammation, and thyroid tumors. In conclusion, our thyrocyte-specific mouse model reveals that mTORC1 activation inhibits TH biosynthesis, suppresses thyrocyte gene expression, and promotes growth and proliferation. Chronic mTORC1 activation leads to thyroid tumor formation, highlighting the role of mTORC1 in thyroid dysfunction and tumorigenesis.
Tonically active GABAergic neurons in the dorsal periaqueductal gray control the initiation and execution of instinctive escape

To avoid predation, animals perform defensive actions that are both instinctive and adaptable to the environment. In mice, the decision to escape from imminent threats is implemented by a feed-forward circuit in the midbrain, where excitatory VGluT2+ neurons in the dorsal periaqueductal gray (dPAG) compute escape initiation and escape vigour from threat evidence. Here we show that GABAergic VGAT+ neurons in the dPAG dynamically control this process by modulating the excitability of excitatory escape neurons. Using in vitro patchclamp and in vivo neural activity recordings in freely behaving mice we found that VGAT+ dPAG neurons fire action potentials tonically in the absence of synaptic inputs and are a major source of synaptic inhibition to VGluT2+ dPAG neurons. Activity in these spontaneously firing VGAT+ cells transiently decreases at escape onset and increases during escape, peaking at escape termination. Optogenetically increasing or decreasing VGAT+ dPAG activity bidirectionally changes the probability of escape when the stimulation is delivered at the time of threat onset, and the duration of escape when delivered after escape initiation. We conclude that the activity of tonically firing VGAT+ dPAG neurons sets a threshold for escape initiation and controls the execution of the flight locomotor action.
UPF1 ATPase autoinhibition and activation modulate RNA binding kinetics and NMD efficiency

The RNA helicase UPF1 interacts with mRNAs, mRNA decay machinery, and the terminating ribosome to promote nonsense-mediated mRNA decay (NMD). Structural and biochemical data have revealed that UPF1 exists in an enzymatically autoinhibited “closed” state. Upon binding the NMD protein UPF2, UPF1 undergoes an extensive conformational change into a more enzymatically active “open” state, which exhibits enhanced ATPase and helicase activity. However, mechanically deficient UPF1 mutants can support efficient NMD, bringing into question the roles of UPF1 enzymatic autoinhibition and activation in NMD. Here, we identify two additional important features of the activated open state: slower nucleic acid binding kinetics and enhanced ATP-stimulated nucleic acid dissociation kinetics. Computational modeling based on empirical measurements of UPF1, UPF2, and RNA interaction kinetics predicts that the majority of UPF1-RNA binding and dissociation events in cells occur independently of UPF2 binding. We find that UPF1 mutants with either reduced or accelerated dissociation from RNA have NMD defects, whereas UPF1 mutants that are more dependent on UPF2 for catalytic activity remain active on well-established NMD targets. These findings support a model in which the kinetics of UPF1-mRNA interactions are important determinants of cellular NMD efficiency.
Cas13-based amplification-free quantification of aberrant RNAs during influenza virus infection

Influenza A virus RNA synthesis produces full-length and aberrant RNA molecules, which include defective viral genomes (DVG) and mini viral RNAs (mvRNA). Sequencing approaches have shown that several hundred unique aberrant RNA species may be present during infection, and that they can vary in size, segment origin, and sequence. Moreover, a subset of aberrant RNA molecules can bind and activate host pathogen receptor retinoic acid-inducible gene I (RIG-I), leading to innate immune signaling and the expression of type I and III interferons. Understanding the kinetics and distribution of these immunostimulatory aberrant RNA sequences is important for modeling the outcomes of IAV infection. We here first show that reverse transcription and PCR steps can yield imperfect aberrant RNA quantification data in a sequence-dependent manner. Next, we developed an amplification-free LbuCas13a-based detection method to quantify mvRNA amplification kinetics and subcellular distributions. We show that our assay can quantify the copy numbers of 10 specific mvRNA sequences in total RNA from cell culture, animal tissue or clinical nasopharyngeal swab extracts. In addition, we find kinetic and distribution differences between immunostimulatory and non-immunostimulatory mvRNAs, as well as mvRNAs derived from different segments, during infection. Overall, our results reveal a hitherto hidden diversity in the behavior of IAV mvRNAs and they suggest that their production is linked to replication of the individual viral segments. Cas13 is therefore a valuable new tool in our repertoire for investigating the impact of aberrant RNAs on RNA virus infection.
Characterisation of ligand gating, ion conduction and the ion selectivity mechanism in the endo-lysosomal ion channel hTPC2

Two pore channels (TPCs) are two-fold symmetric endo-lysosomal cation channels forming important drug targets especially for antiviral drugs. They are activated by calcium, ligand binding, and membrane voltage, and to date, are the only ion channels shown to alter their ion selectivity depending on the type of bound ligand. However, despite their importance in the field, ligand activation of TPCs and the molecular mechanisms underlying their ion selectivity are still poorly understood. Here, we set out to elucidate the mechanistic basis for the ion selectivity of human TPC2 (hTPC2) and the molecular mechanism of ligand-induced channel activation by the lipid PI(3,5)P2. We performed all-atom in silico electrophysiology simulations to study Na+ and Ca2+ permeation across hTPC2 in real-time and to investigate the conformational changes induced by the presence or absence of bound PI(3,5)P2. Our findings reveal that hTPC2 adopts distinct structures depending on the presence of PI(3,5)P2 and elucidate the conformational transition pathways between these structures. Additionally, we examined the permeation mechanism, solvation states, and binding sites of ions during ion permeation through the pore. Our simulations reproduce the experimental observation that hTPC2 is more selective for Na+ over Ca2+ ions in the presence of PI(3,5)P2 and explain the mechanism of this ion selectivity. They highlight the importance of specific ion binding sites at the luminal channel entrance, the selectivity filter, and the central channel cavity for ion conduction, enabling a distant knock-on mechanism for efficient permeation of Na+ ions.
Structure prediction of protein-ligand complexes from sequence information with Umol

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly, given a multiple sequence alignment representation of the protein and a SMILES string representing the ligand. At a high accuracy threshold, unseen protein-ligand complexes can be predicted more accurately than for RoseTTAFold-AA, and at medium accuracy even classical docking methods that use known protein structures as input are surpassed. The high accuracy presented here suggests that the goal of AI-based drug discovery is one step closer, but there is still a way to go to fully grasp the complexity of protein-ligand interactions. Umol is available at: https://github.com/patrickbryant1/Umol