Erich Kummerfeld

RNA editing is a post-transcriptional process that alters RNA nucleotide sequences, potentially affecting protein coding and RNA function. In this study, we identified 2132 senescence-associated RNA editing sites, defined as editing events present in highly senescent WI-38 fibroblasts at population doubling level (PDL) 50 but absent at earlier passages (PDL 20 or 25), by analyzing time-course bulk RNA-seq data from replicative senescence. Approximately 92% of these sites were located within Alu elements, predominantly in 3’ untranslated regions (3’ UTRs), suggesting widespread post-transcriptional regulatory remodeling during senescence. Numerous senescence-associated editing events exhibited elevated editing levels in tissues from older individuals in the Genotype-Tissue Expression (GTEx) project. Among these events, F11 receptor (F11R) mRNA editing was observed across multiple tissues and exhibited higher editing levels in older individuals, showing a more consistent association with aging than F11R gene expression in human tissues. These findings reveal transcriptome-wide RNA editing patterns associated with cellular senescence and suggest that RNA editing may provide a complementary layer of molecular information for studying aging and senescence-related molecular changes.

A bstract: Background: Unobtrusive recognition of activities of daily living (ADLs) from ambient sensors may support longitudinal assessment of everyday functioning in adults with mild neurocognitive disorder (mild NCD). However, real-world activity recognition is complicated by heterogeneous home layouts, sensor configurations, routines, and class distributions. Objective: This study evaluated whether a room-aware Transformer that preserves location-specific sensor structure improves participant-level ADL recognition relative to a global Transformer and a stacked unidirectional long short-term memory (LSTM) network. Methods: Following pilot feasibility testing with two adult volunteers, we analyzed five homes of adults with mild NCD, comprising 92,765 minute-level observations across 67 annotated home-days. Motion, temperature, illuminance, humidity, and door-contact data were aligned to participant activity logs. The original labels were harmonized into eight categories. Models were developed separately for each home using 10-, 30-, and 60-minute windows. Complete calendar days were assigned to mutually exclusive training, validation, and chronological test sets, and no sequence crossed a day or partition boundary. The same partitions and evaluation procedures were used for all three architectures. Performance was assessed on held-out test days using accuracy, per-class F1 score, and micro- and macro-averaged one-versus-rest area under the receiver operating characteristic curve (AUC). Results: The room-aware Transformer achieved the highest or tied accuracy in 9 of 15 home-window comparisons. Its accuracy ranged from 0.27 to 0.57, micro-AUC from 0.57 to 0.82, and macro-AUC from 0.54 to 0.78. Across the 15 settings, mean accuracy was similar for the room-aware and global Transformers and higher than for the LSTM. Mean micro-AUC and macro-AUC were marginally higher for the global Transformer than for the room-aware Transformer. The room-aware model showed its clearest gains in selected contexts, including Homes C and E at 10 minutes and Home A at 60 minutes, but no architecture or temporal window was uniformly optimal. Sleep/Rest was the most consistently recognized activity, whereas low-support activities frequently had low F1 scores despite moderate or high AUC values. Conclusions: Room-specific feature processing provided context-dependent gains in participant-level ADL classification, particularly for accuracy in several home-window settings. The marked variation across homes, activities, and temporal windows supports personalized model development, class-sensitive evaluation, and validation in larger and more diverse residential samples before clinical use.

The senescence-associated secretory phenotype (SASP) is a hallmark of senescent cells and plays a critical role in the development and progression of various age-related diseases, including cancer, cardiovascular disorders, and neurodegenerative diseases. In this study, we characterize SASP heterogeneity using single-cell RNA sequencing (scRNA-seq) data, focusing on the transcriptional signatures associated with elevated expression of individual SASP genes in mature senescent cells, as well as time-dependent variation in SASP expression across the early and mature senescent states in the WI-38 human lung fibroblast cell line. We generated multiple gene sets, each representing the transcriptional landscape linked to high expression of a specific SASP gene, and integrated them into an ensemble that reflects the temporal dynamics of SASP gene expression. Applying SASP scores derived from this ensemble of gene sets (SASP scores/EGS) to publicly available scRNA-seq datasets from human lung, skin, and eye tissues enabled the identification of senescent fibroblasts and revealed IGFBP7 as a consistently upregulated marker in p21⁺ or p16⁺ fibroblasts across diverse human tissues. Our framework supports improved detection of both early and mature fibroblast replicative senescent cells, offering valuable insights into aging and age-related disease research.

Introduction: Suicide is the second leading cause of death in adolescents in the United States. There is an urgent need to advance understanding of risk mechanisms in adolescents to guide early interventions. While prior research has implicated cognition, neural connectivity, and psychopathology in relation to adolescent suicidal ideation (SI) and nonsuicidal self‐injury (NSSI), there is a relative lack of clarity regarding the causal structure of these factors, particularly in early adolescence. Methods: Causal discovery analysis was applied to neuroimaging, neurocognition, and clinical assessment data from the baseline visit of the Adolescent Brain Cognitive Development Study when the participants were 9–10 years old ( N = 8937; 49.6% female) to produce models of causal relationships. Results: In the discovered model, causal pathways from resting state functional connectivity to externalizing and internalizing psychopathology were observed. Greater externalizing psychopathology increased SI and NSSI. Cognitive performance indirectly increased SI and NSSI via its negative relationship with externalizing psychopathology. Finally, more SI increased NSSI. Conclusions: In this developmental window prior to when the risk of suicide accelerates, it is critical to begin to advance our understanding of the processes that may undergird suicide risk (neural, cognitive performance), features of psychopathology and the potential progression of SI and NSSI (both risk factors for suicide). Future research should incorporate other factors related to SI and NSSI to produce a more comprehensive understanding of the mechanisms of risk. This line of research has the potential for a more comprehensive understanding of risk and provides avenues for prevention.

Data artifacts may induce errors in findings from any spatial transcriptomics platform. To provide protection from these errors, we have developed Border, Location, and edge Artifact DEtection (BLADE). BLADE is a novel collection of automated cross-platform statistical methods for detecting and removing three types of artifacts: (i) border effects, where total gene reads is modified at the border of the capture area; (ii) tissue edge effects, where total gene reads is modified at the edge of the tissue; (iii) location batch malfunctions, where there is a zone in the same location on all slides in a batch with substantially decreased sequencing depth. These artifacts are not mutually exclusive. BLADE has been applied to both Visium and CosMx data, and was used to evaluate our library of 37 10x Visium samples of liver and adipose tissue from humans and mice. Artifacts were found to be both common and impactful in those samples, indicating that artifact detection methods are critical for spatial transcriptomics quality control. Our BLADE software is publicly available.

We consider the following comparative effectiveness scenario. There are two treatments for a particular medical condition: a randomized experiment has demonstrated mediocre effectiveness for the first treatment, while a non-randomized study of the second treatment reports a much higher success rate. On what grounds might one justifiably prefer the second treatment over the first treatment, given only the information from those two studies, including design details? This situation occurs in reality and warrants study. We consider a particular example involving studies of treatments for Crohn's disease. In order to help resolve these cases of asymmetric evidence, we make three contributions and apply them to our example. First, we demonstrate the potential to improve success rates above those found in a randomized trial, given heterogeneous effects. Second, we prove that deliberate treatment assignment can be more efficient than randomization when study results are to be transported to formulate an intervention policy on a wider population. Third, we provide formal conditions under which a temporal-discontinuity design approximates a randomized trial, and we introduce a novel design parameter to inform researchers about the strength of that approximation. Overall, our results indicate that while randomization certainly provides special advantages, other study designs such as temporal-discontinuity designs also have distinct advantages, and can produce valuable evidence that informs treatment decisions and intervention policy.

The number of artificial intelligence algorithms for learning causal models from data is growing rapidly. Most ``causal discovery'' or ``causal structure learning'' algorithms are primarily validated through simulation studies. However, no widely accepted simulation standards exist and publications often report conflicting performance statistics -- even when only considering publications that simulate data from linear models. In response, several manuscripts have criticized a popular simulation design for validating algorithms in the linear case. We propose a new simulation design for generating linear models for directed acyclic graphs (DAGs): the DAG-adaptation of the Onion (DaO) method. DaO simulations are fundamentally different from existing simulations because they prioritize the distribution of correlation matrices rather than the distribution of linear effects. Specifically, the DaO method uniformly samples the space of all correlation matrices consistent with (i.e. Markov to) a DAG. We also discuss how to sample DAGs and present methods for generating DAGs with scale-free in-degree or out-degree. We compare the DaO method against two alternative simulation designs and provide implementations of the DaO method in Python and R: https://github.com/bja43/DaO_simulation. We advocate for others to adopt DaO simulations as a fair universal benchmark.

The present chapter covers the important dimension of causality in ML both in terms of causal structure discovery and causal inference. The vast majority of biomedical ML focuses on predictive modeling and does not address causal methods, their requirements and properties. Yet these are essential for determining and assisting patient-level or healthcare-level interventions toward improving a set of outcomes of interest. Moreover causal ML techniques can be instrumental for health science discovery.

Integrative experiment design is a needed improvement over ad hoc experiments, but the specific proposed method has limitations. We urge a further break with tradition through the use of an enormous untapped resource: Decades of causal discovery artificial intelligence (AI) literature on optimizing the design of systematic experimentation.