Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression

Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the convergence rate of Back-fitting in training additive GPs is still an open problem. By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-\mathcal{O}(\frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively. Consequently, Back-fitting requires a minimum of $\mathcal{O}(n\log n)$ iterations to achieve convergence. Based on KPs, we further propose an algorithm called Kernel Multigrid (KMG). This algorithm enhances Back-fitting by incorporating a sparse Gaussian Process Regression (GPR) to process the residuals subsequent to each Back-fitting iteration. It is applicable to additive GPs with both structured and scattered data. Theoretically, we prove that KMG reduces the required iterations to $\mathcal{O}(\log n)$ while preserving the time and space complexities at $\mathcal{O}(n\log n)$ and $\mathcal{O}(n)$ per iteration, respectively. Numerically, by employing a sparse GPR with merely 10 inducing points, KMG can produce accurate approximations of high-dimensional targets within 5 iterations.
Improving the Robustness of Object Detection and Classification AI models against Adversarial Patch Attacks

Adversarial patch attacks, crafted to compromise the integrity of Deep Neural Networks (DNNs), significantly impact Artificial Intelligence (AI) systems designed for object detection and classification tasks. The primary purpose of this work is to defend models against real-world physical attacks that target object detection and classification. We analyze attack techniques and propose a robust defense approach. We successfully reduce model confidence by over 20% using adversarial patch attacks that exploit object shape, texture and position. Leveraging the inpainting pre-processing technique, we effectively restore the original confidence levels, demonstrating the importance of robust defenses in mitigating these threats. Following fine-tuning of an AI model for traffic sign classification, we subjected it to a simulated pixelized patch-based physical adversarial attack, resulting in misclassifications. Our inpainting defense approach significantly enhances model resilience, achieving high accuracy and reliable localization despite the adversarial attacks. This contribution advances the resilience and reliability of object detection and classification networks against adversarial challenges, providing a robust foundation for critical applications.
Robust Hepatitis B Virus Genotyping by Mass Spectrometry

ABSTRACT Genotyping of hepatitis B virus (HBV) is important for tracking HBV infections, prognosticating the development of severe liver disease, and predicting outcomes of therapy. Current genotyping methods can be laborious and costly and rely on subjective data interpretation. To identify less expensive but equally reliable alternatives, we compared “gold standard” sequencing to a novel mass spectrometry approach. Sera from individuals with acute or chronic HBV infection (n = 756), representing all genotypes, were used to PCR amplify the HBV S gene. All amplicons were subjected to base-specific cleavage and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS). The resulting mass peak patterns were used to identify HBV genotype by automated comparison to peak patterns simulated from reference sets of HBV sequences of known genotypes. The MALDI-TOF MS data and phylogenetic analysis of HBV sequences produced completely concordant results. Several parameters such as genetic relatedness of tested HBV variants to the reference set, chronic infections, and the quality of PCR products can lower the MS score but never affected the accuracy of the genotype call. This new streamlined MS-based method provides for rapid and accurate HBV genotyping, produces automated data reports, and is therefore suitable for routine use in diagnostic settings.
Quality and efficiency in high dimensional nearest neighbor search

Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sub-linearly with the dataset size, regardless of the data and query distributions. Despite the bulk of NN literature, no solution fulfills both requirements, except locality sensitive hashing (LSH). The existing LSH implementations are either rigorous or adhoc. Rigorous-LSH ensures good quality of query results, but requires expensive space and query cost. Although adhoc-LSH is more efficient, it abandons quality control, i.e., the neighbor it outputs can be arbitrarily bad. As a result, currently no method is able to ensure both quality and efficiency simultaneously in practice. Motivated by this, we propose a new access method called the locality sensitive B-tree (LSB-tree) that enables fast high-dimensional NN search with excellent quality. The combination of several LSB-trees leads to a structure called the LSB-forest that ensures the same result quality as rigorous-LSH, but reduces its space and query cost dramatically. The LSB-forest also outperforms adhoc-LSH, even though the latter has no quality guarantee. Besides its appealing theoretical properties, the LSB-tree itself also serves as an effective index that consumes linear space, and supports efficient updates. Our extensive experiments confirm that the LSB-tree is faster than (i) the state of the art of exact NN search by two orders of magnitude, and (ii) the best (linear-space) method of approximate retrieval by an order of magnitude, and at the same time, returns neighbors with much better quality.
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System

Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management. The code is available at https://github.com/jmyissb/HealthLLM.
Look Before You Leap: Socially Acceptable High-Speed Ground Robot Navigation in Crowded Hallways

To operate safely and efficiently, autonomous warehouse/delivery robots must be able to accomplish tasks while navigating in dynamic environments and handling the large uncertainties associated with the motions/behaviors of other robots and/or humans. A key scenario in such environments is the hallway problem, where robots must operate in the same narrow corridor as human traffic going in one or both directions. Traditionally, robot planners have tended to focus on socially acceptable behavior in the hallway scenario at the expense of performance. This paper proposes a planner that aims to address the consequent"robot freezing problem"in hallways by allowing for"peek-and-pass"maneuvers. We then go on to demonstrate in simulation how this planner improves robot time to goal without violating social norms. Finally, we show initial hardware demonstrations of this planner in the real world.
Atypical retinal function in a mouse model of Fragile X syndrome

Altered function of peripheral sensory neurons is an emerging mechanism for symptoms of autism spectrum disorders. Visual sensitivities are common in autism, but whether differences in the retina might underlie these sensitivities is not well-understood. We explored retinal function in the Fmr1 knockout model of Fragile X syndrome, focusing on a specific type of retinal neuron, the “sustained On alpha” retinal ganglion cell. We found that these cells exhibit changes in dendritic structure and dampened responses to light in the Fmr1 knockout. We show that decreased light sensitivity is due to increased inhibitory input and reduced E-I balance. The change in E-I balance supports maintenance of circuit excitability similar to what has been observed in cortex. These results show that loss of Fmr1 in the mouse retina affects sensory function of one retinal neuron type. Our findings suggest that the retina may be relevant for understanding visual function in Fragile X syndrome.
Dynamic role of GlyT1 as glycine sink or source: pharmacological implications for the gain control of NMDA receptors

Glycine transporter 1 (GlyT1) mediates termination of inhibitory glycinergic receptors signaling in the spinal cord and brainstem, and is also diffusely present in the forebrain. Here, it regulates the ambient glycine concentration influencing the ‘glycine’-site occupancy of N -methyl-d-aspartate (NMDARs). GlyT1 is a reversible transporter with a substantial, but not excessive, sodium-motive force for uphill transport. This study examines its potential role as a glycine source, either by reversed-uptake or by heteroexchange. I explored how glycine accumulation triggers its release, facilitating the activation of NMDARs by glutamate applied alone. Indeed, glutamate evokes no current in “naive” oocytes coexpressing GluN1/GluN2A and GlyT1, a previously characterized cellular model, but now using GlyT1 as the only potential source of coagonist for NMDAR activation. After glycine up-take, however, glutamate evokes large currents, blocked by ALX-5407 and potentiated by sarcosine, a specific inhibitor and substrate of GlyT1, respectively. These results suggest higher occupancy of the co-agonist site when GlyT1 functions as a glycine source either by reversed-uptake or by heteroexchange. A difference between these two glycine-release mechanisms occurs at hyperpolarized potentials, which induce an apparent voltage-dependent block of NMDAR currents, whereas heteroexchange preserves NMDAR activation at these potentials. Together, these results confirm GlyT1-mediated efflux as a positive regulator of NMDAR co-agonist site occupancy, and demonstrate sarcosine heteroexchange effectiveness in enhancing coagonist site occupancy. Depending on its actual mode of transport, GlyT1-inhibitors and sarcosine may have distinct effects on ambient glycine and NMDAR facilitation, and be a source of variation in reversing NMDAR hypofunction in schizophrenia.
Human spermatogonial stem cells retain states with a foetal-like signature

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling it has been proposed that undifferentiated human spermatogonia can be sub-classified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc)RNA-seq libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate ‘state 0B’ with a suppressive transcriptomic program that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells – which are mitotically arrested – can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mouse (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive life while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.
A systems-biology approach connects aging mechanisms with Alzheimer’s disease pathogenesis

Age is the strongest risk factor for developing Alzheimer’s disease, the most common neurodegenerative disorder. However, the mechanisms connecting advancing age to neurodegeneration in Alzheimer’s disease are incompletely understood. We conducted an unbiased, genome-scale, forward genetic screen for age-associated neurodegeneration in Drosophila to identify the underlying biological processes required for maintenance of aging neurons. To connect genetic screen hits to Alzheimer’s disease pathways, we measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer’s disease. We further identified Alzheimer’s disease human genetic variants that modify expression in disease-vulnerable neurons. Through multi-omic, multi-species network integration of these data, we identified relationships between screen hits and tau-mediated neurotoxicity. Furthermore, we computationally and experimentally identified relationships between screen hits and DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to attenuate the effects of age on neurodegeneration and Alzheimer’s disease.
Expected Error Minimization with Ultraconservative Update for SMT

Minimum error rate training is a popular method for parameter tuning in statistical machine translation (SMT). However, the optimization objective function may change drastically at each optimization step, which may induce MERT instability. We propose an alternative tuning method based on an ultraconservative update, in which the combination of an expected task loss and the distance from the parameters in the previous round are minimized with a variant of gradient descent. Experiments on test datasets of both Chinese-to-English and Spanish-toEnglish translation show that our method can achieve improvements over MERT under the Moses system.
Dissecting the binding mechanisms of synaptic membrane adhesion complexes using a micropattern based cellular model

The formation of adhesive cell-cell contacts is based on the intrinsic binding properties between specific transmembrane ligand-receptor pairs. In neurons, synaptic adhesion molecules provide a physical linkage between pre- and post-synaptic compartments, but the strength and the dynamic of these complexes in their actual membrane environments remain essentially unknown. To access such information, we developed a versatile assay to measure the affinity and binding kinetics of synaptic ligand-receptor interactions, based on the immobilization of Fc-tagged ligands on micropatterned substrates combined with live imaging of fluorescently-tagged counter receptors in heterologous cells. We applied this strategy to study the heterophilic complex formed between neurexin-1β (Nrx1β) and neuroligin-1 (Nlg1), compared to the homophilic SynCAM1 complex. First, the control of ligand density combined to the measurement of steady-state receptor enrichment at micropatterns demonstrates the high specificity of the matching molecular interactions and allows for the quantification of the two-dimensional affinity of the interaction in a membrane environment. Second, long-term FRAP experiments performed on the two molecular complexes and fitted with analytical models, demonstrate a diffusion-limited regime for SynCAM1 and a reaction-limited regime for Nlg1. This analysis provides a very long bond lifetime of the Nrx1β-Nlg1 complex, which by comparison with a monomeric mutant of Nlg1, can be attributed to the constitutive dimerization of Nlg1. Finally, we used the stable Nrx1β-Nlg1 complex as a pseudo-synaptic platform to analyze the rapid binding kinetics between the scaffolding protein PSD-95 and the intracellular domain of Nlg1, dissecting the contribution of the different PDZ domains through the use of specific PSD-95 point mutants.
Twist - torsion coupling in beating axonemes

Motile cilia and flagella are ubiquitous cell appendages whose regular bending waves pump fluids across tissue surfaces and enable single-cell navigation. Key to these functions are their non-planar waveforms with characteristic torsion. It is not known how torsion, a purely geometric property of the shape, is related to mechanical deformations of the axoneme, the conserved cytoskeletal core of cilia and flagella. Here, we assess torsion and twist in reactivated axonemes isolated from the green alga Chlamydomonas reinhardtii. Using defocused darkfield microscopy and beat-cycle averaging, we resolve the 3D shapes of the axonemal waveform with nanometer precision at millisecond timescales. Our measurements reveal regular hetero-chiral torsion waves propagating base to tip with a peak-to-peak amplitude of 22 º/µm. To investigate if the observed torsion results from axonemal twist, we attach gold nanoparticles to axonemes to measure its cross-section rotation during beating. We find that locally, the axonemal cross-section co-rotates with the bending plane. This co-rotation presents the first experimental evidence for twist-torsion coupling and indicates that twist waves propagate along the axoneme during beating. Our work thus links shape to mechanical deformation of beating axonemes, informing models of motor regulation that shape the beat of motile cilia.
Insight from Wild Yeast Isolates into the Molecular Mechanisms of Lifespan Variation Mediated by Caloric Restriction

Caloric restriction (CR) is known to extend lifespan across different species and holds great promise for preventing human age-onset pathologies. However, two major challenges exist. First, despite extensive research, the mechanisms of lifespan extension in response to CR remain elusive. Second, genetic differences causing variations in response to CR and genetic factors contributing to variability of CR response on lifespan are largely unknown. Here, we took advantage of natural genetic variation across 46 diploid wild yeast isolates of Saccharomyces species and the lifespan variation under CR conditions to uncover the molecular factors associated with CR response types. We identified genes and metabolic pathways differentially regulated in CR-responsive versus non-responsive strains. Our analysis revealed that altered mitochondrial function and activation of GCN4-mediated environmental stress response are inevitably linked to lifespan variation in response to CR and a unique mitochondrial metabolite might be utilized as a predictive marker for CR response rate. In sum, our data suggests that the effects of CR on longevity may not be universal, even among closely related species or strains of a single species. Since mitochondrial mediated signaling pathways are evolutionarily conserved, the dissection of related genetic pathways will be relevant to understanding the mechanism by which CR elicits its longevity effect.
Viral challenges and adaptations between Central Arctic Ocean and atmosphere

Aquatic viruses act as key players in shaping microbial communities. In polar environments, they face significant challenges like limited host availability and harsh conditions. However, due to restricted ecosystem accessibility, our understanding of viral diversity, abundance, adaptations, and host interactions remains limited. To fill this knowledge gap, we studied viruses from atmosphere-close aquatic ecosystems in the Central Arctic and Northern Greenland. Aquatic samples for virus-host analysis were collected from ~60 cm depth and the submillimeter surface microlayer (SML) during the Synoptic Arctic Survey 2021 on icebreaker Oden in Arctic summer. Water was sampled from a melt pond and open water before undergoing size-fractioned filtration and followed by genome-resolved metagenomic and cultivation investigations. The prokaryotic diversity in the melt pond was considerably lower compared to open water. The melt pond was dominated by a Flavobacterium sp. and Aquiluna sp. , the latter having a relatively small genome size of 1.2 Mb and the metabolic potential to generate ATP using the phosphate acetyltransferase-acetate kinase pathway. Viral diversity on the host fraction (0.2 – 5 µm) of the melt pond was strikingly limited compared to open water. From 1154 dereplicated viral operational taxonomic units (vOTUs), of which two-thirds were predicted bacteriophages, 17.2% encoded for auxiliary metabolic genes (AMGs) with metabolic functions. Some AMGs like glycerol-3-phosphate cytidylyltransferase and ice-binding like proteins might serve cryoprotection of the host. Prophages were often associated with SML genomes, and two active prophages of a new viral genera from the Arctic SML strain Leeuwenhoekiella aequorea Arc30 were induced. We found evidence that vOTU abundance in the SML compared to ~60 cm depth was more positively correlated to the distribution of a vOTU across five different Arctic stations. The results indicate that viruses employ elaborated strategies to endure in extreme and host-limited environments. Moreover, our observations suggest that the immediate air-sea interface serves as a platform for viral distribution in the Central Arctic.
Mitochondria drive microglial NRLP3 inflammasome activation via TSPO

Uncontrolled microglial response is core to neuroinflammatory brain diseases. The correlation between the mitochondrial protein TSPO and inflammation has so far failed to explain whether TSPO positively or negatively regulates microglial function. The recent evidence on the species specificity of TSPO in microglia demands a deeper understanding of the protein biology in these brain-resident macrophages. To this end, we have here enrolled a murine model of microglial cells showing that TSPO is required for the priming of mitochondria to inflammation and a conduit for its escalation. Namely, in response to inflammatory cues TSPO is stabilised on the mitochondria where it binds and sequesters NOD-like receptor (NLR) protein (i), represses the PARK2-mediated mitophagy (ii) and engages the retrograde communication with the nucleus via the accumulation of the Nf-kB to promote the expression of pro-inflammatory genes (iii). Notably, the TSPO sustained inflammatory response drives cellular demise and ultimately leads to excitotoxicity (iv). Our findings advance the current knowledge of TSPO widening the understanding of mitochondria in inflammation and indicating a target for their regulation.