Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development.

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration .Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation.Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.2

Computational vision-based flame detection has drawn significant attention in the past decade with camera surveillance systems becoming ubiquitous. Whereas many discriminating features, such as color, shape, texture, etc., have been employed in the literature, this paper proposes a set of motion features based on motion estimators. The key idea consists of exploiting the difference between the turbulent, fast, fire motion, and the structured, rigid motion of other objects. Since classical optical flow methods do not model the characteristics of fire motion (e.g., non-smoothness of motion, non-constancy of intensity), two optical flow methods are specifically designed for the fire detection task: optimal mass transport models fire with dynamic texture, while a data-driven optical flow scheme models saturated flames. Then, characteristic features related to the flow magnitudes and directions are computed from the flow fields to discriminate between fire and non-fire motion. The proposed features are tested on a large video database to demonstrate their practical usefulness. Moreover, a novel evaluation method is proposed by fire simulations that allow for a controlled environment to analyze parameter influences, such as flame saturation, spatial resolution, frame rate, and random noise.

Gastric acid secretion is not only stimulated via the classical known neuronal and hormonal pathways but also by the Ca²⁺-Sensing Receptor (CaSR) located at the basolateral membrane of the acid-secretory gastric parietal cell. Stimulation of CaSR with divalent cations or the potent agonist Gd³⁺ leads to activation of the H⁺/K⁺-ATPase and subsequently to gastric acid secretion. Here we investigated the intracellular mechanism(s) mediating the effects of the CaSR on H⁺/K⁺-ATPase activity in freshly isolated human gastric glands. Inhibition of heterotrimeric G-proteins (G_i and G_o) with pertussis toxin during stimulation of the CaSR with Gd³⁺ only partly reduced the observed stimulatory effect. A similar effect was observed with the PLC inhibitor U73122. The reduction of the H⁺/K⁺-ATPase activity measured after incubation of gastric glands with BAPTA-AM, a chelator of intracellular Ca²⁺, showed that intracellular Ca²⁺ plays an important role in the signalling cascade. TMB-8, a ER Ca²⁺store release inhibitor, prevented the stimulation of H⁺/K⁺-ATPase activity. Also verapamil, an inhibitor of L-type Ca²⁺-channels reduced stimulation suggesting that both the release of intracellular Ca²⁺ from the ER as well as Ca²⁺ influx into the cell are involved in CaSR-mediated H⁺/K⁺-ATPase activation. Chelerythrine, a general inhibitor of protein kinase C, and Gö 6976 which selectively inhibits Ca²⁺-dependent PKC_α and PKC_βI-isozymes completely abolished the stimulatory effect of Gd³⁺. In contrast, Ro 31-8220, a selective inhibitor of the Ca²⁺-independent PKCε and PKC-δ isoforms reduced the stimulatory effect of Gd³⁺ only about 60 %. On the other hand, activation of PKC with DOG led to an activation of H⁺/K⁺-ATPase activity which was only about 60 % of the effect observed with Gd³⁺. Incubation of the parietal cells with PD 098059 to inhibit ERK1/2 MAP-kinases showed a significant reduction of the Gd³⁺ effect. Thus, in the human gastric parietal cell the CaSR is coupled to pertussis toxin sensitive heterotrimeric G-Proteins and requires calcium to enhance the activity of the proton-pump. PLC, ERK 1/2 MAP-kinases as well as Ca²⁺ dependent and Ca²⁺-independent PKC isoforms are part of the down-stream signalling cascade.