Explore the vast range of titles available.

Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information. PASTE aligns and integrates spatial transcriptomics data generated from adjacent tissue slices by leveraging their transcriptomic similarity and spatial coordinates, which ultimately increases the power for downstream analysis.

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis 1 – 4 . Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA ; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19 , ASAP2 and EN1 , and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial–mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions. A pan-cancer epigenetic and transcriptomic atlas identifies epigenetic drivers associated with cancer transitions.

Requirements for iron and docosahexaenoic acid (DHA) content of infant formula varies by country. Powdered full-term infant formula purchase data from all major physical stores in the US between 2017–2019 were obtained from CIRCANA, Inc. Iron and DHA composition and scoop sizes for each formula were obtained from manufacturers. The equivalent liquid ounces of prepared formula were calculated. Average iron and DHA content were compared between formula types and to both US and European formula composition requirements. These data represent 55.8 billion ounces of formula. The average iron content of all formula purchased was: 1.80 mg/100 kcal. This iron concentration is within the FDA regulations. However, it exceeds the maximum allowable iron concentration of infant formula (Stage 1) set by the European Commission of 1.3 mg/100 kcal. A total of 96% of formula purchased had an iron concentration of >1.3 mg/100 kcal. DHA is not a required ingredient in US formulas. The average DHA content of all formula purchased was: 12.6 mg/100 kcal. This DHA concentration is far below the minimum required DHA concentrations of infant formula (Stage 1) and follow-on formula (Stage 2) set by the European Commission of 20 mg/100 kcal. These are novel insights into the iron and DHA intake of formula-fed infants in the US. As international infant formulas have entered the US market due to the formula shortage, parents and providers need to be aware of regulatory differences in formula nutrient composition.

Single-cell omics measurements have exploded in growth over the past decade. This explosion has allowed researchers to probe human health and biology with unprecedented resolution. Currently, all these types of measurements are destructive, thus they only provide static snapshots of important dynamic biological processes such as development, cancer progression, and cell cycle. As cells differentiate/progress biologically asynchronously in most tissues, a major computational task often called trajectory inference is to infer the latent biological time also known as pseudotime of every cell. This inverse problem in general is quite challenging and is further complicated by the fact that single-cell omics measurements like scRNA-seq and scATAC-seq are highly sparse and highdimensional. Much of my work has shown that simple linear supervised dimensionality reduction techniques that rely on cell type information can outperform complex non-linear dimensionality reduction techniques when used in conjunction with state-of-the-art trajectory inference methods in a large benchmark. Moreover, we investigate the difficulties of benchmarking trajectory inference methods in the absence of ground truth showcasing that the implicit goal of many methods is not to identify intermediate/transient cell types but rather order cell types. In addition, we introduce a novel supervised linear dimensionality reduction technique called BCA that when applied to simulated and real datasets is better able to uncover intermediate cell types. Lastly, we have been interested in modeling the relationship between cell lineages of inferred phylogenies from single-cell lineage tracing data and scRNA-seq trajectories (the partial ordering of cells induced by pseudotimes). We have found that by using a novel irreversible continuous state model of pseudotime on a rooted tree that we are better able to model unobserved ancestral pseudotimes in simulated and real phylogenies.

During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, , that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors.

Selecting powerful predictors for an outcome is a cornerstone task for machine learning. However, some types of questions can only be answered by identifying the predictors that causally affect the outcome. A recent approach to this causal inference problem leverages the invariance property of a causal mechanism across differing experimental environments (Peters et al., 2016; Heinze-Deml et al., 2018). This method, invariant causal prediction (ICP), has a substantial computational defect -- the runtime scales exponentially with the number of possible causal variables. In this work, we show that the approach taken in ICP may be reformulated as a series of nonparametric tests that scales linearly in the number of predictors. Each of these tests relies on the minimization of a novel loss function -- the Wasserstein variance -- that is derived from tools in optimal transport theory and is used to quantify distributional variability across environments. We prove under mild assumptions that our method is able to recover the set of identifiable direct causes, and we demonstrate in our experiments that it is competitive with other benchmark causal discovery algorithms.

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, \"snapshot\" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and next-state pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model.

To assess long-time results of primary surgical treatment in children with glaucoma after congenital cataract surgery. A retrospective study of 37 eyes from 35 children with glaucoma after congenital cataract surgery, who underwent surgery between 2011 and 2021 at the Childhood Glaucoma Center, University Medical Center Mainz, Germany. Only children, who received a primary glaucoma surgery in our clinic within the given time (n = 25) and had at least one-year follow-up (n = 21), were included in the further analysis. The mean follow-up time was 40.4±35.1 months. The primary outcome was the mean reduction in IOP (in mmHg) from baseline to follow-up visits after the surgery, measured with Perkins tonometry. 8 patients (38%) were treated with probe trabeculotomy (probe TO), 6 (29%) with 360° catheter-assisted trabeculotomy (360° TO) and 7 (33%) with cyclodestructive procedures. IOP was significantly reduced after probe TO and 360° TO after 2 years, from 26.9 mmHg to 17.4 mmHg (p<0.01) and 25.2 mmHg to 14.1 mmHg (p<0.02), respectively. There was no significant IOP reduction after cyclodestructive procedures after 2 years. Both, probe TO and 360° TO led descriptively to eye drops reduction after 2 years, from 2.0 to 0.7 and 3.2 to 1.1. The reduction was not significant. In glaucoma after congenital cataract surgery, both trabeculotomy techniques, lead to good reduction of IOP after 2 years. There is a need for a prospective study with comparison to the use of glaucoma drainage implants.

Background Large language models (LLMs) such as ChatGPT-4 and Google Gemini show potential for patient health education, but concerns about their accuracy require careful evaluation. This study evaluates the readability and accuracy of ChatGPT-4 and Google Gemini in answering questions about retinal detachment. Methods Comparative study analyzing responses from ChatGPT-4 and Google Gemini to 13 retinal detachment questions, categorized by difficulty levels (D1, D2, D3). Masked responses were reviewed by ten vitreoretinal specialists and rated on correctness, errors, thematic accuracy, coherence, and overall quality grading. Analysis included Flesch Readability Ease Score, word and sentence counts. Results Both Artificial Intelligence tools required college-level understanding for all difficulty levels. Google Gemini was easier to understand ( p  = 0.03), while ChatGPT-4 provided more correct answers for the more difficult questions ( p  = 0.0005) with fewer serious errors. ChatGPT-4 scored highest on most challenging questions, showing superior thematic accuracy ( p  = 0.003). ChatGPT-4 outperformed Google Gemini in 8 of 13 questions, with higher overall quality grades in the easiest ( p  = 0.03) and hardest levels ( p  = 0.0002), showing a lower grade as question difficulty increased. Conclusions ChatGPT-4 and Google Gemini effectively address queries about retinal detachment, offering mostly accurate answers with few critical errors, though patients require higher education for comprehension. The implementation of AI tools may contribute to improving medical care by providing accurate and relevant healthcare information quickly.