Factor analysis is a method that condenses multiple variables into a few latent factors. It can be used to extract the underlying sources of biological variation in high-dimensional data and distill them into interpretable gene programs. However, existing factorization methods lack adaptability in selecting the optimal number of factors and interpretability in capturing biological variation. To address these concerns, we propose Deep Beta Process (DBP), a deep probabilistic framework for adaptive and interpretable factor analysis of single-cell transcriptomic data. DBP achieves adaptive selection of factors through a stick-breaking Beta process and performs batch correction using an adversarial learning strategy. We validate the flexible factor extraction and robust batch correction capabilities of DBP on simulated datasets. We also demonstrate its superior performance in dimensionality reduction and biological interpretability while explaining biological variation from both cell and gene perspectives using factor and loading matrices. The application of DBP to a gastric adenocarcinoma dataset reveals malignant epithelial cell heterogeneity, providing valuable insights for investigating the molecular mechanisms of disease onset and progression. DBP is available at https://github.com/labomics/DBP and https://ngdc.cncb.ac.cn/biocode/tool/BT007954.

For highly reliable products that degrade over time, it is not uncommon to observe an initial period during which no failure occurs. In this paper, a degradation model that gives rise to a first passage time distribution with failure-free life is proposed as a more plausible model compared to many common models. We call it Beta process as its increments are approximated by three-parameter Beta distributions. We start with a discrete time process as a degradation process is commonly measured at regular time intervals. We derive a closed-form first passage time distribution under a constant failure threshold. Closed-form maximum likelihood (ML)-type estimators are developed for the parameters of the Beta process. Then, the discrete time process is extended to the case of a continuous time process. Comprehensive simulations and case studies show that the Beta processes outperform many common degradation processes in reliability estimation.

The neighborhood scope (i.e., number of hops) where graph neural networks (GNNs) aggregate information to characterize a node's statistical property is critical to GNNs' performance. Two-stage approaches, training and validating GNNs for every pre-specified neighborhood scope to search for the best setting, is a time-consuming task and tends to be biased due to the search space design. How to adaptively determine proper neighborhood scopes for the aggregation process for both homophilic and heterophilic graphs remains largely unexplored. We thus propose to model the GNNs' message-passing behavior on a graph as a stochastic process by treating the number of hops as a beta process. This Bayesian framework allows us to infer the most plausible neighborhood scope for message aggregation simultaneously with the optimization of GNN parameters. Our theoretical analysis shows that the scope inference improves the expressivity of a GNN. Experiments on benchmark homophilic and heterophilic datasets show that the proposed method is compatible with state-of-the-art GNN variants, achieving competitive or superior performance on the node classification task, and providing well-calibrated predictions.

In this article, we present current results of the experiment searching for double beta decay of 106Cd with the help of an enriched 106CdWO4 crystal scintillator in coincidence with two CdWO4 scintillation detectors. The experiment is carried out at the Gran Sasso underground laboratory of the National Institute for Nuclear Physics (LNGS INFN, Italy). After 1075 days of data-taking, no double-beta effects were observed. New half-life limits have been set for the different modes and channels of double beta processes in 106Cd at the level of limT1/2=1020−1022 years.

High-dimensional data have attracted considerable interest from researchers, especially in the area of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. Moreover, in microarray research, it is common to identify groupings of genes involved in the same biological pathways. These gene groupings frequently collaborate and operate as a unified entity. Therefore, this study is motivated to adopt the idea of a penalized semi-parametric Bayesian Cox (PSBC) model through elastic-net and group lasso penalty functions (PSBC-EN and PSBC-GL) to incorporate the grouping structure of the covariates (genes) and optimally perform variable selection. The proposed methods assign a beta process prior to the cumulative baseline hazard function (PSBC-EN-B and PSBC-GL-B), instead of the gamma process prior used in existing methods (PSBC-EN-G and PSBC-GL-G). Three real-life datasets and simulation scenarios were considered to compare and validate the efficiency of the modified methods with existing techniques, using Bayesian information criteria (BIC). The results of the simulated studies provided empirical evidence that the proposed methods performed better than the existing methods across a wide range of data scenarios. Similarly, the results of the real-life study showed that the proposed methods revealed a substantial improvement over the existing techniques in terms of feature selection and grouping behavior.

Intervertebral disc degeneration and pain are associated with the nucleotide-binding domain, leucine-rich repeat, and pyrin domain-containing 3 (NLRP3) inflammasome activation and the processing of interleukin-1 beta (IL-1β). Activation of thehm inflammasome is triggered by Toll-like receptor stimulation and requires the cofactor receptor cluster of differentiation 14 (CD14). Short Link N (sLN), a peptide derived from link protein, has been shown to modulate inflammation and pain in discs in vitro and in vivo; however, the underlying mechanisms remain elusive. This study aims to assess whether sLN modulates IL-1β and inflammasome activity through interaction with CD14. Disc cells treated with lipopolysaccharides (LPS) with or without sLN were used to assess changes in Caspase-1, IL-1β, and phosphorylated nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB). Peptide docking of sLN to CD14 and immunoprecipitation were performed to determine their interaction. The results indicated that sLN inhibited LPS-induced NFκB and Caspase-1 activation, reducing IL-1β maturation and secretion in disc cells. A significant decrease in inflammasome markers was observed with sLN treatment. Immunoprecipitation studies revealed a direct interaction between sLN and the LPS-binding pocket of CD14. Our results suggest that sLN could be a potential therapeutic agent for discogenic pain by mitigating IL-1β and inflammasome activity within discs.

MT-RSL: A multitasking-oriented robot skill learning framework based on continuous dynamic movement primitives for improving efficiency and quality in robot-based intelligent operation

A demonstration trajectory segmentation approach for wheelchair‐mounted assistive robots

This review provides a comprehensive examination of the role of clathrin-mediated endocytosis (CME) in Alzheimer's disease (AD) pathogenesis, emphasizing its impact across various cellular contexts beyond neuronal dysfunction. In neurons, dysregulated CME contributes to synaptic dysfunction, amyloid beta (Aβ) processing, and Tau pathology, highlighting its involvement in early AD pathogenesis. Furthermore, CME alterations extend to non-neuronal cell types, including astrocytes and microglia, which play crucial roles in Aβ clearance and neuroinflammation. Dysregulated CME in these cells underscores its broader implications in AD pathophysiology. Despite significant progress, further research is needed to elucidate the precise mechanisms underlying CME dysregulation in AD and its therapeutic implications. Overall, understanding the complex interplay between CME and AD across diverse cell types holds promise for identifying novel therapeutic targets and interventions.

Abstract This paper is about the modeling of cumulative hazard functions using martingale posterior distributions. The focus is on uncertainty quantification from a nonparametric perspective. The foundational Bayesian model in this case is the beta process and the classic estimator is the Nelson–Aalen. We use a sequence of estimators which form a martingale in order to obtain a random cumulative hazard function from the martingale posterior. The connection with the beta process is established and a number of illustrations is presented.

BPFAaC: A radar intelligent perception algorithm in 6G integrated sensing and communications scenarios

Infinite-activity completely random measures (CRMs) have become important building blocks of complex Bayesian nonparametric models. They have been successfully used in various applications such as clustering, density estimation, latent feature models, survival analysis or network science. Popular infinite-activity CRMs include the (generalized) gamma process and the (stable) beta process. However, except in some specific cases, exact simulation or scalable inference with these models is challenging and finite-dimensional approximations are often considered. In this work, we propose a general and unified framework to derive both series representations and finite-dimensional approximations of CRMs. Our framework can be seen as an extension of constructions based on size-biased sampling of Poisson point process [Perman1992]. It includes as special cases several known series representations as well as novel ones. In particular, we show that one can get novel series representations for the generalized gamma process and the stable beta process. We also provide some analysis of the truncation error.

Summary:Cellular perturbations underlying Alzheimer’s disease are primarily studied in human postmortem samples and model organisms. Here we generated a single-nucleus atlas from a rare cohort of cortical biopsies from living individuals with varying degrees of Alzheimer’s disease pathology. We next performed a systematic cross-disease and cross-species integrative analysis to identify a set of cell states that are specific to early AD pathology. These changes–which we refer to as the Early Cortical Amyloid Response—were prominent in neurons, wherein we identified a transient state of hyperactivity preceding loss of excitatory neurons, which correlated with the selective loss of layer 1 inhibitory neurons. Microglia overexpressing neuroinflammatory-related processes also expanded as AD pathological burden increased. Lastly, both oligodendrocytes and pyramidal neurons upregulated genes associated with amyloid beta production and processing during this early hyperactive phase. Our integrative analysis provides an organizing framework for targeting circuit dysfunction, neuroinflammation, and amyloid production early in AD pathogenesis.

Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.

We show that streams and lazy data structures are a natural idiom for programming with infinite-dimensional Bayesian methods such as Poisson processes, Gaussian processes, jump processes, Dirichlet processes, and Beta processes. The crucial semantic idea, inspired by developments in synthetic probability theory, is to work with two separate monads: an affine monad of probability, which supports laziness, and a commutative, non-affine monad of measures, which does not. (Affine means that T (1)≅ 1.) We show that the separation is important from a decidability perspective, and that the recent model of quasi-Borel spaces supports these two monads. To perform Bayesian inference with these examples, we introduce new inference methods that are specially adapted to laziness; they are proven correct by reference to the Metropolis-Hastings-Green method. Our theoretical development is implemented as a Haskell library, LazyPPL.

Change Point Detection in Beta Process with High Frequency Data

The ZnWO4crystal scintillator is a promising detector for low counting experiments thanks to the high level of radiopurity and reasonably high optical and scintillation properties. In particular, ZnWO4scintillators can be utilized in the searches for double beta processes, in the investigation of rare alpha decays and Dark Matter. In fact, one of its main characteristics is to be an anisotropic detector. In the case of interaction of heavy particles or nuclear recoils, the light output and the time profile of the scintillation pulse depend on the direction of the particles with respect to the crystal axes; no difference is observed for [Formula: see text] radiation. These anisotropic features can offer a unique possibility to exploit the so-called directionality approach in order to investigate the presence of the Dark Matter candidates which induces nuclear recoils. In fact, their use can overcome the difficulty of detecting extremely short nuclear recoil traces.

Synaptic integrity and function depend on myriad proteins - labile molecules with finite lifetimes that need to be continually replaced with freshly synthesized copies. Here we describe experiments designed to expose synaptic (and neuronal) properties and functions that are particularly sensitive to disruptions in protein supply, identify proteins lost early upon such disruptions, and uncover potential, yet currently underappreciated failure points. We report here that acute suppressions of protein synthesis are followed within hours by reductions in spontaneous network activity levels, impaired oxidative phosphorylation and mitochondrial function, and, importantly, destabilization and loss of both excitatory and inhibitory postsynaptic specializations. Conversely, gross impairments in presynaptic vesicle recycling occur over longer time scales (days), as does overt cell death. Proteomic analysis identified groups of potentially essential ‘early-lost’ proteins including regulators of synapse stability, proteins related to bioenergetics, fatty acid and lipid metabolism, and, unexpectedly, numerous proteins involved in Alzheimer’s disease pathology and amyloid beta processing. Collectively, these findings point to neuronal excitability, energy supply and synaptic stability as early-occurring failure points under conditions of compromised supply of newly synthesized protein copies.

Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the ‘Correlated Beta’ or ‘CoBe’ dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives

Differential N-terminal processing of beta and gamma actin