Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithm to Estimate the Power Spectral Density From Allan Deviation

Complex architectures for wireless communications, digital electronics, and space-based navigation interlink several oscillator-based devices such as clocks, transponders, and synthesizers. Estimators characterizing their stability are critical for addressing the impact of random fluctuations (noise) on the overall system performance. Manufacturers typically specify this as an Allan/Hadamard Variance (AVAR/HVAR) profile in the time domain. However, stochastic processes constituting the noise are more thoroughly described in the frequency domain by the power spectral density function (PSD). Both are second-moment measures of the time series, but it is only possible to translate unambiguously from the PSD to the AVAR/HVAR, not vice versa, except in the case of a single noise type, a rather unrealistic case. This note presents an analytical method to generate an approximated PSD expressed as a set of power-laws defined in specific intervals in the frequency domain, starting from an AVAR/HVAR expressed as a set of power-laws in the time domain. The proposed algorithm is straightforward to implement, applicable to all noise types (and combinations thereof), and can be self-validated by reconstructing the corresponding AVAR/HVAR by direct computation. Coupling with well-established algorithms relying on the PSD for power-law noise generation, the ensuing method encompasses the capability for generating multicolored noise in end-to-end simulations, as demonstrated hereby for NASA’s deep space atomic clock. We also report on the limitations of the algorithm and analytical expressions of the continuous version of the algorithm.
Adding diversity and realism to LAVA, a vulnerability addition system

In this thesis, I designed and implemented several modifications to LAVA, a vulnerability addition system, with the goal of improving realism and diversity of the injected bugs. Specifically, I describe three separate improvements: a method to add fake bugs alongside real ones in order to decrease bug discoverability, two approaches to increase the complexity of the data flow of the inserted bugs, and a standalone program that uses equality saturation to diversify C-source codebases that can be added as a final stage to LAVA. Finally, I present two instances of bug-finding competitions— AutoCTF and Rode0day—that I helped design and run, which leveraged LAVA and the augmentations described in this thesis in order to accomplish their respective goals. Thesis Supervisor: Timothy Leek Title: Technical Staff, MIT Lincoln Laboratory
International Journal of Public Health Science (IJPHS)

Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia Clinical Research Centre, Queen Elizabeth Hospital, Malaysia Ministry of Health, Kota Kinabalu, Malaysia Faculty of Sustainable Agriculture, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia Biotechnology Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia Pejabat Kesihatan Kota Kinabalu, Malaysia Ministry of Health, Kota Kinabalu, Malaysia
A host AAA-ATPase exhibits bacteriolytic activity for clearance of microbial infection

An array of host cytosol guarding factors impede bacterial proliferation and preserve cellular sterility. Amongst them, proteasomal degradation of ubiquitinated pathogens has emerged as a critical mechanism for ensuring cytosolic sanctity. We wondered how proteasomes, with their small size and inability to extract membrane-bound proteins, can eradicate pathogens. Here, we unveil a unique strategy, wherein VCP/p97, a host AAA-ATPase, eliminates pathogens by exerting mechanical force that physically unfolds and pulls out ubiquitinated proteins from bacterial membrane. Combining a single-molecule approach along with molecular dynamic simulation and in-vitro reconstitution, we demonstrate that protein extraction by p97 causes extensive membrane lysis and release of cytosolic contents from phylogenetically diverse microbes. Additionally, in an in-vivo mouse sepsis model, this segregase-dependent bactericidal effect of p97 abrogated microbial proliferation in host tissues. Overall, we discovered a distinct innate antimicrobial function of p97, that protects the host against lethal bacterial infections. One Sentence Summary A host AAA-ATPase exhibits bacteriolytic activity.
Diffusion kurtosis MRI tracks gray matter myelin content in the primate cerebral cortex

Diffusion magnetic resonance imaging (dMRI) has been widely employed to model the trajectory of myelinated fiber bundles in white matter. Increasingly, dMRI is also used to assess local tissue properties throughout the brain. In the cerebral cortex, myelin content is a critical indicator of the maturation, regional variation, and disease related degeneration of gray matter tissue. Gray matter myelination can be measured and mapped using several non-diffusion MRI strategies; however, first order diffusion statistics such as fractional anisotropy (FA) show only weak spatial correlation with cortical myelin content. Here we show that a simple higher order diffusion parameter, the mean diffusion kurtosis (MK), is strongly correlated with the laminar and regional variation of myelin in the primate cerebral cortex. We carried out ultra-high resolution, multi-shelled dMRI in ex vivo marmoset monkey brains and compared dMRI parameters from a number of higher order models (diffusion kurtosis, NODDI and MAP MRI) to the distribution of myelin obtained using histological staining, and via Magnetization Transfer Ratio MRI (MTR), a non-diffusion MRI method. In contrast to FA, MK closely matched the myelin content assessed by histology and by MTR in the same sample. The parameter maps from MAP-MRI and NODDI also showed good correspondence with cortical myelin content. The results demonstrate that dMRI can be used to assess the variation of local myelin content in the primate cortical cortex, which may be of great value for assessing tissue integrity and tracking disease in living human patients.
Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 µm, 1mm). Overall, we demonstrate the marked potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.
LPOR and the membranes – evolutionary pathway towards prolamellar body formation

Light-dependent protochlorophyllide oxidoreductase (LPOR) has captivated the interest of the research community for decades. One reason is the photocatalytic nature of the reaction catalyzed by the enzyme, and the other is the involvement of LPOR in the formation of a paracrystalline lattice called a prolamellar body (PLB) that disintegrates upon illumination, initiating a process of photosynthetic membrane formation. In this paper, we have integrated three traditional methods previously employed to study the properties of the enzyme to investigate how LPOR evolved and how PLB forms. We found that in cyanobacteria, LPOR activity appears to be independent of lipids, with membrane interaction primarily affecting the enzyme post-reaction, with MGDG and PG having opposite effects on SynPOR. In contrast, plant isoforms exhibit sequence alterations, rendering the enzyme effective in substrate binding mainly in the presence of anionic lipids, depending on residues at positions 122, 312, and 318. Moreover, we demonstrated that the interaction with MGDG could initially serve as enhancement of the substrate specificity towards monovinyl-protochlorophyllide (Pchlide). We have shown that the second LPOR isoforms of eudicots and monocots accumulated mutations that made these variants less and more dependent on anionic lipids, respectively. Finally, we have shown that in the presence of Pchlide, NADP+, and the lipids, plant but not cyanobacterial LPOR homolog remodel membranes into the cubic phase. The cubic phase is preserved if samples supplemented with NADP+ are enriched with NADPH. The results are discussed in the evolutionary context, and the model of PLB formation is presented. Significance LPOR is a unique enzyme with photocatalytic properties, developed by cyanobacteria and inherited by algae and plants. In this study, we investigated the properties of the cyanobacterial homolog, revealing that two lipids, PG and MGDG, have opposite effects on enzyme activity. Additionally, we identified mutations in plant isoforms that render the enzyme dependent on anionic lipids. Moreover, we demonstrated that in the presence of NADP+, the plant homolog remodels lipids into a cubic phase, which appears to be the initial step of prolamellar body (PLB) formation. PLB is a unique paracrystalline arrangement of lipids and proteins found in immature chloroplasts, which disintegrates upon illumination, initiating photosynthetic membrane formation.
Convolutional neural network models of the primate retina reveal adaptation to natural stimulus statistics

The diverse nature of visual environments demands that the retina, the ﬁrst stage of the visual system, encodes a vast range of stimuli with various statistics. The retina adapts its computations to some speciﬁc features of the input, such as brightness, contrast or motion. However, it is less clear whether it also adapts to the statistics of natural scenes compared to white noise, the latter of which is often used to infer models of retinal computation. To address this question, we analyzed neural activity of retinal ganglion cells (RGCs) in response to both white noise and naturalistic movie stimuli. We performed a systematic comparative analysis of traditional linear-nonlinear (LN) and recent convolutional neural network (CNN) models and tested their generalization across stimulus domains. We found that no model type trained on one stimulus ensemble was able to accurately predict neural activity on the other, suggesting that retinal processing depends on the stimulus statistics. Under white noise stimulation, the receptive ﬁelds of the neurons were mostly lowpass, while under natural image statistics they exhibited a more pronounced surround resembling the whitening ﬁlters predicted by efficient coding. Together, these results suggest that retinal processing dynamically adapts to the stimulus statistics.
LncRNA BRE-AS1 Regulates the JAK2/STAT3-mediated Inflammatory Activation via the miR-30b-5p/SOC3 Axis in THP-1 cells

Long non-coding RNAs (lncRNAs) have emerged as pivotal regulators in numerous biological processes, including macrophage-mediated inflammatory responses, which play a critical role in the progress of diverse diseases. This study focuses on the regulatory function of lncRNA BRE-AS1 in modulating the inflammatory activation of monocytes/macrophages. Employing the THP-1 cell line as a model, we demonstrate that lipopolysaccharide (LPS) treatment significantly upregulates BRE-AS1 expression. Notably, specific knockdown of BRE-AS1 via siRNA transfection enhances LPS-induced expression of interleukin (IL)-6 and IL-1β, while not affecting tumor necrosis factor (TNF)-α levels. This selective augmentation of pro-inflammatory cytokine production coincides with increased phosphorylation of JAK2 and STAT3. Furthermore, BRE-AS1 suppression results in the downregulation of SOCS3, an established inhibitor of the JAK2/STAT3 pathway. Bioinformatics analysis identified binding sites for miR-30b-5p on both BRE-AS1 and SOCS3 mRNA. Intervention with a miR-30b-5p inhibitor and a synthetic RNA fragment that represents the miR-30b-5p binding site on BRE-AS1 attenuates the pro-inflammatory effects of BRE-AS1 knockdown. Conversely, a miR-30b-5p mimic replicated the BRE-AS1 attenuation outcomes. Our findings elucidate the role of lncRNA BRE-AS1 in modulating inflammatory activation in THP-1 cells via the miR-30b-5p/SOCS3/JAK2/STAT3 signaling pathway, proposing that manipulation of macrophage BRE-AS1 activity may offer a novel therapeutic avenue in diseases characterized by macrophage-driven pathogenesis.
MotifScope: a multi-sample motif discovery and visualization tool for tandem repeats

Tandem repeats (TRs) constitute a significant portion of the human genome, exhibiting high levels of polymorphism due to variations in size and motif composition. These variations have been associated with various neuropathological disorders, underscoring the clinical importance of TRs. Furthermore, the motif structure of these repeats can offer valuable insights into evolutionary dynamics and population structure. However, analysis of TRs has been hampered by the limitations of short-read sequencing technology, which lacks the ability to fully capture the complexity of these sequences. With long-read data becoming more accessible, there is now also a need for tools to explore and characterize these TRs. In this study, we introduce MotifScope, a novel algorithm for visualization of TRs in their population context based on a de novo k-mer approach for motif discovery. Comparative analysis against three established tools, uTR, TRF, and vamos, reveals that MotifScope can identify a greater number of motifs and more accurately represent the actual repeat sequence. Additionally, MotifScope enables comparison of sequencing reads within an individual and assemblies across different individuals, showing its applicability in diverse genomic contexts. We demonstrate potential applications of MotifScope in diverse fields, including population genetics, clinical settings, and forensic analyses.
Protein complex heterogeneity and topology revealed by electron capture charge reduction and surface induced dissociation

Herein, we focus on native mass spectrometry (nMS) combined with a fast, tunable gas-phase charge reduction, electron capture charge reduction (ECCR), and illustrate its utility in the characterization of protein complex topology and glycoprotein heterogeneity. ECCR is illustrated to effectively spread the charge states of tetradecameric GroEL, illustrating Orbitrap m/z measurement out to greater than 100,000 m/z. For both the pentameric C-reactive protein and tetradecameric GroEL, our novel device combining ECCR with surface induced dissociation (SID) lowers the charge states and produces more topologically informative fragmentation. While more native-like fragmentation has previously been illustrated for complexes charge reduced by proton abstraction in solution, this is the first illustration that ECCR can lead to more native-like SID fragmentation of protein complexes. Application to protein glycosylation, one of the most common and diverse protein posttranslational modifications, is also illustrated because glycosylation is important for structural and functional properties and plays essential roles in many key biological processes. The immense heterogeneity resulting from variability in glycosylation sites and glycan composition and structure poses significant analytical challenges that hinder a mechanistic understanding of the biological role of glycosylation. Data for stabilized heavily glycosylated SARS-CoV-2 spike protein trimer and thyroglobulin dimer illustrate that ECCR enables significantly improved resolution of glycan heterogeneity. Without ECCR, the charge states of a glycoprotein complex are not resolved and average mass determination is available only through the use of charge detection mass spectrometry or mass photometry. With ECCR after narrow m/z selection, multiple glycoform m/z values are apparent, providing quick global, glycoform profiling and providing a future path for glycan localization on individual intact glycoforms (e.g., though top-down dissociation).
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates

We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO). The algorithm alternates polynomial approximation with Bayesian optimization steps, using Gaussian processes to model the error between the objective and its polynomial fit. We describe the algorithmic design of PMBO and compare the results of the performance of PMBO with several optimization methods for a set of analytic test functions. The results show that PMBO outperforms the classic Bayesian optimization and is robust with respect to the choice of its correlation function family and its hyper-parameter setting, which, on the contrary, need to be carefully tuned in classic Bayesian optimization. Remarkably, PMBO performs comparably with state-of-the-art evolutionary algorithms such as the Covariance Matrix Adaptation -- Evolution Strategy (CMA-ES). This finding suggests that PMBO emerges as the pivotal choice among surrogate-based optimization methods when addressing low-dimensional optimization problems. Hereby, the simple nature of polynomials opens the opportunity for interpretation and analysis of the inferred surrogate model, providing a macroscopic perspective on the landscape of the objective function.
Noise-induced transitions past the onset of a steady symmetry-breaking bifurcation: the case of the sudden expansion

We consider fluid flows, governed by the Navier-Stokes equations, subject to a steady symmetry-breaking bifurcation and forced by a weak noise acting on a slow time scale. By generalizing the multiple-scale weakly nonlinear expansion technique employed in the literature for the response of the Duffing oscillator, we rigorously derive a stochastically forced Stuart-Landau equation for the dominant symmetry-breaking mode. The probability density function of the solution, and of the escape time from one attractor to the other, are then determined by solving the associated Fokker-Planck equation. The validity of this reduced order model is tested on the flow past a sudden expansion, for a given Reynolds number and different noise amplitudes. At a very low numerical cost, the statistics obtained from the amplitude equation accurately reproduce those of long-time direct numerical simulations.
Tuning diagonal scale matrices for HMC

Three approaches for adaptively tuning diagonal scale matrices for HMC are discussed and compared. The common practice of scaling according to estimated marginal standard deviations is taken as a benchmark. Scaling according to the mean log-target gradient (ISG), and a scaling method targeting that the frequency of when the underlying Hamiltonian dynamics crosses the respective medians should be uniform across dimensions, are taken as alternatives. Numerical studies suggest that the ISG method leads in many cases to more efficient sampling than the benchmark, in particular in cases with strong correlations or non-linear dependencies. The ISG method is also easy to implement, computationally cheap and would be relatively simple to include in automatically tuned codes as an alternative to the benchmark practice.
3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs

Generating molecules with desired properties is a critical task with broad applications in drug discovery and materials design. Inspired by recent advances in large language models, there is a growing interest in using natural language descriptions of molecules to generate molecules with the desired properties. Most existing methods focus on generating molecules that precisely match the text description. However, practical applications call for methods that generate diverse, and ideally novel, molecules with the desired properties. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to address this challenge. 3M-Diffusion first encodes molecular graphs into a graph latent space aligned with text descriptions. It then reconstructs the molecular structure and atomic attributes based on the given text descriptions using the molecule decoder. It then learns a probabilistic mapping from the text space to the latent molecular graph space using a diffusion model. The results of our extensive experiments on several datasets demonstrate that 3M-Diffusion can generate high-quality, novel and diverse molecular graphs that semantically match the textual description provided.
Supercomputer model of finite-dimensional quantum electrodynamics applications

A general scheme is given for supercomputer simulation of quantum processes, which are described by various modifications of finite-dimensional cavity quantum electrodynamics models, including Jaynes-Cummings-Hubbard model and Tavis-Cummings-Hubbard model. Conclusions and recommendations are illustrated using two examples: approximate model of hydrogen bonding and model of photon motion on a two-dimensional plane.
Stochastic gradient descent-based inference for dynamic network models with attractors

In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model to allow nodes to join and leave over time. Simulation results show that our extensions result in little loss of accuracy compared to MCMC, but can scale to much larger networks. We apply our approach to the longitudinal social networks of members of US Congress on the social media platform X. Accounting for node dynamics overcomes selection bias in the network and uncovers uniquely and increasingly repulsive forces within the Republican Party.