Miracle-dz/UniFAR-FTUs huggingface dataset:
Divergent patterns of healthy aging across human brain regions at single-cell resolution reveal links to neurodegenerative disease

Age is a major common risk factor underlying neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Previous studies reported that chronological age correlates with differential gene expression across different brain regions. However, prior datasets have not disambiguated whether expression associations with age are due to changes in cell numbers and/or gene expression per cell. In this study, we leveraged single nucleus RNA-sequencing (snRNAseq) to examine changes in cell proportions and transcriptomes in four different brain regions, each from 12 donors aged 20-30 years (young) or 60-85 years (old). We sampled 155,192 nuclei from two cortical regions (entorhinal cortex and middle temporal gyrus) and two subcortical regions (putamen and subventricular zone) relevant to neurodegenerative diseases or the proliferative niche. We found no changes in cellular composition of different brain regions with healthy aging. Surprisingly, we did find that each brain region has a distinct aging signature, with only minor overlap in differentially associated genes across regions. Moreover, each cell type shows distinct age-associated expression changes, including loss of protein synthesis genes in cortical inhibitory neurons, axonogenesis genes in excitatory neurons and oligodendrocyte precursor cells, enhanced gliosis markers in astrocytes and disease-associated markers in microglia, and genes critical for neuron-glia communication. Importantly, we find cell type-specific enrichments of age associations with genes nominated by Alzheimer’s disease and Parkinson’s disease genome-wide association studies (GWAS), such as apolipoprotein E (APOE), and leucine-rich repeat kinase 2 (LRRK2) in microglia that are independent of overall expression levels across cell types. We present this data as a new resource which highlights, first, region- and cell type-specific transcriptomic changes in healthy aging that may contribute to selective vulnerability and, second, provide context for testing GWAS-nominated disease risk genes in relevant subtypes and developing more targeted therapeutic strategies. The data is readily accessible without requirement for extensive computational support in a public website, https://brainexp-hykyffa56a-uc.a. run.app/ Graphical Abstract *Created using Biorender.com Highlights Establishment of a single nuclei atlas of human aging in four brain regions Each region and cell type exhibits a unique aging-associated transcriptome signature Gene expression changes occur in absence of overt cell loss and are categorically unique across cell types Neurological disease-associated genes have age-associated expression patterns in specific cell types in the context of healthy aging
High-Level Synthesis of Irregular Applications: A Case Study on Influence Maximization

FPGAs are promising platforms for accelerating irregular applications due to their ability to implement highly specialized hardware designs for each kernel. However, the design and implementation of FPGA-accelerated kernels can take several months using hardware design languages. High Level Synthesis (HLS) tools provide fast, high quality results for regular applications, but lack the support to effectively accelerate more irregular, complex workloads. This work analyzes the challenges and benefits of using a commercial state-of-the-art HLS tool and its available optimizations to accelerate graph sampling. We evaluate the resulting designs and their effectiveness when deployed in a state-of-the-art heterogeneous framework that implements the Influence Maximization with Martingales (IMM) algorithm, a complex graph analytics algorithm. We discuss future opportunities for improvement in hardware, HLS tools, and hardware/software co-design methodology to better support complex irregular applications such as IMM.
IST1 regulates select recycling pathways

ESCRTs (Endosomal Sorting Complex Required for Transport) are a modular set of protein complexes with membrane remodeling activities that include the formation and release of intralumenal vesicles (ILVs) to generate multivesicular endosomes. While most of the 12 ESCRT-III proteins are known to play roles in ILV formation, IST1 has been associated with a wider range of endosomal remodeling events. Here, we extend previous studies of IST1 function in endosomal trafficking and confirm that IST1, along with its binding partner CHMP1B, contributes to scission of early endosomal carriers. Depleting IST1 impaired delivery of transferrin receptor from early/sorting endosomes to the endocytic recycling compartment and instead increased its rapid recycling to the plasma membrane via peripheral endosomes enriched in the clathrin adaptor AP-1. IST1 is also important for export of mannose 6-phosphate receptor from early/sorting endosomes. Examination of IST1 binding partners on endosomes revealed that IST1 interacts with the MIT domain-containing sorting nexin SNX15, a protein previously reported to regulate endosomal recycling. Our kinetic and spatial analyses establish that SNX15 and IST1 occupy a clathrin-containing subdomain on the endosomal perimeter distinct from those previously implicated in cargo retrieval or degradation. Using live-cell microscopy we see that SNX15 and CHMP1B alternately recruit IST1 to this subdomain or the base of endosomal tubules. These findings indicate that IST1 contributes to a subset of recycling pathways from the early/sorting endosome.
A Note on Cycles in Locally Hamiltonian and Locally Hamilton-Connected Graphs

Abstract Let 𝒫 be a property of a graph. A graph G is said to be locally 𝒫, if the subgraph induced by the open neighbourhood of every vertex in G has property 𝒫. Ryjáček conjectures that every connected, locally connected graph is weakly pancyclic. Motivated by the above conjecture, van Aardt et al. [S.A. van Aardt, M. Frick, O.R. Oellermann and J.P.de Wet, Global cycle properties in locally connected, locally traceable and locally Hamiltonian graphs, Discrete Appl. Math. 205 (2016) 171–179] investigated the global cycle structures in connected, locally traceable/Hamiltonian graphs. Among other results, they proved that a connected, locally Hamiltonian graph G with maximum degree at least |V (G)| − 5 is weakly pancyclic. In this note, we improve this result by showing that such a graph with maximum degree at least |V (G)|−6 is weakly pancyclic. Furthermore, we show that a connected, locally Hamilton-connected graph with maximum degree at most 7 is fully cycle extendable.
Distributed neuro‐adaptive control protocols for non‐strict feedback non‐linear MASs with input saturation

This study develops distributed neuro-adaptive control protocols for synchronisation of leader–follower multi-agent systems (MASs) with non-linear dynamics in non-strict-feedback form. The communication graph is assumed to be directed, and the agents are considered heterogeneous. The leader's dynamics are also assumed non-linear, and the state derivative of the leader is unknown to its immediate followers. The well-known command filtered backstepping approach, presented for single-agent systems in the literature, is extended to the synchronisation of MASs with non-strict-feedback non-linear dynamics. This obviates the requirement of computing the n th-order derivatives of the local neighbourhood synchronisation error at the step n of the design. The number of adaptive tuning laws and consequently the complexity of the neuro-adaptive design is reduced by estimating only a positive scalar related to unknown non-linear dynamics of each agent. An auxiliary system is then introduced into the control design to compensate for the discrepancy between designed and saturated control signals. The proposed distributed control protocol guarantees that all signals in the closed-loop system are cooperatively semi-globally uniformly ultimately bounded, and the consensus error for all agents converge to a small neighbourhood of the origin. Finally, a simulation example demonstrates the effectiveness of the proposed control scheme.
Activation of cannabinoid CB2 receptors by (−)-cannabichromene but not (+)-cannabichromene

Introduction Cannabichromene (CBC) is a minor constituent of Cannabis that is a selective cannabinoid CB2 receptor agonist and activator of TRPA1. To date, it has not been shown whether (−)-CBC, (+)-CBC (or both) can mediate these effects. In this study we investigate the activity of the CBC enantiomers at CB1, CB2 and TRPA1 in vitro. Materials and Methods CBC enantiomers were purified from synthetic CBC by chiral chromatography, and their optical activity was confirmed by spectroscopy. Human CB1 and CB2 receptor activity was measured using a fluorescent assay of membrane potential in stably transfected AtT20 cells. TRPA1 activation was measured using a fluorescent assay of intracellular calcium in stably transfected HEK293 cells. Results (−)-CBC activated CB2 with an EC50 of 1.5 µM, to a maximum of 60 % of CP55940. (+)-CBC did not activate CB2 at concentrations up to 30 µM. Only 30 µM (−)– CBC produced detectable activation of CB1, (+)-CBC was inactive. Both (−)-CBC and (+)– CBC activated TRPA1; at 30 µM (−)-CBC produced an activation 50% of that of the reference agonist cinnamaldehyde (300 µM), 30 µM (+)-CBC activated TRPA1 to 38% of the cinnamaldehyde maximum. Discussion It is unclear whether (−)-CBC is the sole or even the predominant enantiomer of CBC enzymatically synthesized in Cannabis. This study shows that (−)-CBC is the active isomer at CB2 receptors, while both isomers activate TRPA1. The results suggest that medicinal preparations of CBC that target cannabinoid receptors would be most effective when (−)-CBC is the dominant isomer.
Dalek - a deep-learning emulator for TARDIS

Supernova spectral time series contain a wealth of information about the progenitor and explosion process of these energetic events. The modeling of these data requires the exploration of very high dimensional posterior probabilities with expensive radiative transfer codes. Even modest parametrizations of supernovae contain more than ten parameters and a detailed exploration demands at least several million function evaluations. Physically realistic models require at least tens of CPU minutes per evaluation putting a detailed reconstruction of the explosion out of reach of traditional methodology. The advent of widely available libraries for the training of neural networks combined with their ability to approximate almost arbitrary functions with high precision allows for a new approach to this problem. Instead of evaluating the radiative transfer model itself, one can build a neural network proxy trained on the simulations but evaluating orders of magnitude faster. Such a framework is called an emulator or surrogate model. In this work, we present an emulator for the TARDIS supernova radiative transfer code applied to Type Ia supernova spectra. We show that we can train an emulator for this problem given a modest training set of a hundred thousand spectra (easily calculable on modern supercomputers). The results show an accuracy on the percent level (that are dominated by the Monte Carlo nature of TARDIS and not the emulator) with a speedup of several orders of magnitude. This method has a much broader set of applications and is not limited to the presented problem.
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation - Supplementary Material

As stated in the main paper (Section 3.1), the key idea is to disentangle the rotation and translation, and further discretize each individual degree of freedom for the actions. At each step, for each of the rotation and translation, we provide 13 actions to rotate (or translate) the object along an axis in a directional small or large step, or stay still (See the Figure 1). The discretization converts the pose regression to a classification task, which tremendously reduces the training difficulty.
pymgpipe: microbiome metabolic modeling in Python

Microbially-produced metabolites and microbiome metabolism in general are strongly linked to ecosystem-level phenotypes, including the health of the human host (Bar et al., 2020; Villanueva-Millán et al., 2015). To aid in the study of microbial metabolism from observational, human-derived data, a variety of computational methods that predict microbial community metabolic output from microbial abundances have been developed (Baldini et al., 2019; Diener et al., 2020; Mallick et al., 2019; Noecker et al., 2022). Several of these methods rely on community-scale metabolic models, which are mechanistic, knowledge-based models that enable the formulation and in silico testing of biological hypotheses regarding the metabolism of microbial communities (Baldini et al., 2019; Diener et al., 2020). Community-scale models primarily use Flux Balance Analysis, a technique that infers the metabolic fluxes in a system by optimizing an objective function, typically growth rate, subject to an assumption of a steady state and constraints imposed by the metabolic reactions present in the system (Orth et al., 2010). These metabolic reactions are obtained from genome-scale metabolic networks (GEMs), knowledge-based computational models encompassing the known biochemical reactions present within an organism (Thiele & Palsson, 2010). In recent years, curated GEMs for thousands of human-associated microbial organisms have become increasingly available, enabling a more in-depth exploration of the human microbiome (Heinken et al., 2023; Machado et al., 2018; Norsigian et al., 2020). In addition, several community-scale metabolic modeling methods specifically tailored to the human microbiome have emerged, such as MICOM and mgPipe (Baldini et al., 2019; Diener et al., 2020).
Using public clinical trial reports to evaluate observational study methods

: Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiability makes it difficult both to compare different observational study methods and to trust the results of any particular observational study. In this work, we propose TrialVerify, a new approach for evaluating observational study methods based on ground truth sourced from clinical trial reports. We process trial reports into a denoised collection of known causal relationships that can then be used to estimate the precision and recall of various observational study methods. We then use TrialVerify to evaluate multiple observational study methods in terms of their ability to identify the known causal relationships from a large national insurance claims dataset. We found that inverse propensity score weighting is an effective approach for accurately reproducing known causal relationships and outperforms other observational study methods. TrialVerify is made freely available for others to evaluate observational study methods.
Assessing the impact of Word Embeddings for Relation Classification: An Empirical Study

This paper presents an empirical study on the value of concept embeddings for relation classification, with a particular focus on hypernymy detection. Knowledge Graphs have become popular due to their ability to represent knowledge in a standardized form that enables reasoning, but often suffer from incompleteness. Relation Classification is a task that can help alleviate this issue. Recent advances in Deep Learning applied to Natural Language Processing have provided researchers with powerful tools for detecting relations between concepts: word embeddings. We investigate the effectiveness of different types and combinations of embeddings for the automatic relation classification task. We conduct experiments on two datasets based on WordNet and the AI-KG Knowledge Base. Our results confirm previous results that it is challenging to deduce the semantic relations from embeddings alone. We observe that hypernymy cannot be captured solely by a sub-space of the embedding space, despite specific dimensions carrying more information about this relation than others. Additionally, we show that it is difficult to apply a model learned on a general ontology to other domains, and that imbalance problems are aggravated in large knowledge bases where one relation dominates over all the others.
Overleaf Example

In everyday life, we often find that we can maintain an object’s equilibrium on a tray by adjusting its orientation. Building upon this observation and extending the method we previously proposed to suppress sloshing in a moving vessel, this paper presents a feedforward control approach for transporting objects with a robot that are not firmly grasped but simply placed on a tray. The proposed approach combines smoothing actions and end-effector re-orientation to prevent object sliding. It can be integrated into existing robotic systems as a plug-in element between the reference trajectory generator and the robot control. To demonstrate the effectiveness of the proposed methods, particularly when dealing with unknown reference signals, we embed them in a direct teleoperation scheme. In this scheme, the user commands the robot carrying the tray by simply moving their hand in free space, with the hand’s 3D position detected by a motion capture system. Furthermore, in the case of point-to-point motions, the same feedforward control, when fed with step inputs representing the desired goal position, dynamically generates the minimumtime reference trajectory that complies with velocity and acceleration constraints, thus avoiding sloshing and slipping. More information and accompanying videos can be found at https://sites.google. com/view/robotwaiter/.
Leveraging ChatGPT API for Enhanced Data Preprocessing in NatUKE

This scientific paper presents an approach for enhancing the performance of machine learning models by utilizing ChatGPT, a state-of-the-art language model developed by OpenAI, for data preprocessing. The study focuses on the existing Project NatUKE (A Benchmark for Natural Product Knowledge Extraction from Academic Literature) and investigates the impact of incorporating ChatGPT in the preprocessing pipeline. By leveraging the natural language processing capabilities of ChatGPT, we aim to improve the quality and relevance of the data used as input for the knowledge graph embedding algorithms. This paper provides a detailed description of the methodology employed, the experimental setup, and the results obtained, highlighting the benefits and limitations of this approach.
Ultrafast Hole Spin Qubit with Gate-Tunable Spin-Orbit Switch

A key challenge in quantum computation is the implementation of fast and local qubit control while simultaneously maintaining coherence. Qubits based on hole spins offer, through their strong spin-orbit interaction, a way to implement fast quantum gates. Strikingly, for hole spins in one-dimensional germanium and silicon devices, the spin-orbit interaction has been predicted to be exceptionally strong yet highly tunable with gate voltages. Such electrical control would make it possible to switch on demand between qubit idling and manipulation modes. Here, we demonstrate ultrafast and universal quantum control of a hole spin qubit in a germanium/silicon core/shell nanowire, with Rabi frequencies of several hundreds of megahertz, corresponding to spin-flipping times as short as ~1 ns - a new record for a single-spin qubit. Next, we show a large degree of electrical control over the Rabi frequency, Zeeman energy, and coherence time - thus implementing a switch toggling from a rapid qubit manipulation mode to a more coherent idling mode. We identify an exceptionally strong but gate-tunable spin-orbit interaction as the underlying mechanism, with a short associated spin-orbit length that can be tuned over a large range down to 3 nm for holes of heavy-hole mass. Our work demonstrates a spin-orbit qubit switch and establishes hole spin qubits defined in one-dimensional germanium/silicon nanostructures as a fast and highly tunable platform for quantum computation.
Masked ADMM regularization Masked retraining Thresholding mask updating

Deep neural networks (DNNs), although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work has been conducted on DNN model compression or pruning. However, most of the previous work has taken heuristic approaches. This work proposes a progressive weight pruning approach based on ADMM (Alternating Direction Method of Multipliers), a powerful technique to deal with non-convex optimization problems with potentially combinatorial constraints. Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates. Therefore, it resolves the accuracy degradation and long convergence time problems when pursuing extremely high pruning ratios. It achieves up to 34× pruning rate for ImageNet data set and 167× pruning rate for MNIST data set, significantly higher than those reached by existing work in the literature. Under the same number of epochs, the proposed method also achieves faster convergence and higher compression rates. The codes and pruned DNN models are avilable in the link: bit.ly/2zxdlss.
On the Parameterized Approximability of Contraction to Classes of Chordal Graphs ∗

A graph operation that contracts edges is one of the fundamental operations in the theory of graph minors. Parameterized Complexity of editing to a family of graphs by contracting k edges has recently gained substantial scientific attention, and several new results have been obtained. Some important families of graphs, namely the subfamilies of chordal graphs, in the context of edge contractions, have proven to be significantly difficult than one might expect. In this paper, we study the F -Contraction problem, where F is a subfamily of chordal graphs, in the realm of parameterized approximation. Formally, given a graph G and an integer k , F -Contraction asks whether there exists X ⊆ E ( G ) such that G / X ∈ F and | X | ≤ k . Here, G / X is the graph obtained from G by contracting edges in X . We obtain the following results for the F -Contraction problem.
Adversarial Defense by Latent Style Transformations

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution images by detecting suspicious inputs. The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e.g., slightly changing the facial expression of human portraits. In contrast, adversarial examples are generally sensitive to such transformations. In our approach to detect adversarial instances, we propose an in\underline{V}ertible \underline{A}utoencoder based on the \underline{S}tyleGAN2 generator via \underline{A}dversarial training (VASA) to inverse images to disentangled latent codes that reveal hierarchical styles. We then build a set of edited copies with non-essential style transformations by performing latent shifting and reconstruction, based on the correspondences between latent codes and style transformations. The classification-based consistency of these edited copies is used to distinguish adversarial instances.