FUNCTIONAL CHARACTERIZATION OF HEME TRANSPORTERS IN ZEBRAFISH

Hrg1 and Mrp5 are identified as eukaryotic heme importer and exporter, respectively. Two Hrg1 paralogs have been annotated in zebrafish genome, Hrg1a (Slc48a1b) and Hrg1b (Slc48a1a) with 84% homology in protein sequences. Hrg1a and hrg1b are widely expressed in embryonic and adult zebrafish. Yeast growth assays reveal that zebrafish Hrg1a and Hrg1b are both capable of heme import. However, hrg1a and hrg1b double knockout (hrg1 DKO) zebrafish generated by CRISPR/Cas9 has no overt defects in differentiation and maturation of erythroid cells. Knockdown of hrg1a in hrg1b mutants or vice versa does not impair erythroid lineage in zebrafish embryos. These genetic results suggest that Hrg1 is not required for maturation and hemoglobinization of primitive erythroid cells. Hrg1a and hrg1b mRNA are upregulated in adult kidneys and spleens upon PHZ-induced hemolysis, together with hmox1, a downstream heme degrading enzyme, suggesting that Hrg1 is involved in adult heme-iron recycling during erythrophagcytosis in kidney and spleen of adult zebrafish. DAB-enhanced Perl’s iron staining reveals that iron is accumulated in macrophages in the kidney and spleen in adult wild-type zebrafish. However, macrophages with positive Perl’s staining are rarely found in the kidney of hrg1 DKO and instead large amount of iron is deposited in renal tubules, suggesting defects in heme-iron recycling by kidney macrophages in hrg1 DKO under PHZ-induced hemolysis. Whole transcriptome sequencing of mRNA extracted from spleens and kidneys reveals massive differentially expressed genes in hrg1 DKO involved in immune response, lipid transport, oxidation-reduction process and proteolysis. These indicate that hrg1 DKO are deficient in recycling heme-iron derived from damaged RBCs in the absence of functional Hrg1. Phylogenetic analysis reveals that Mrp5 and Mrp9 are closed homologs in the zebrafish genome. Yeast growth assays reveal that both zebrafish Mrp5 and Mrp9 are capable of heme export. Morpholino knockdown of mrp5 and mrp9 in zebrafish showed severe anemia in developing embryos indicating their involvements in erythropoietic development. Subsequent generation and characterization of mrp5 and mrp9 mutants by CRISPR/Cas9 will further define the function of Mrp5 and Mrp9 during zebrafish development

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation

Referring video object segmentation (RVOS) aims at segmenting an object in a video following human instruction. Current state-of-the-art methods fall into an offline pattern, in which each clip independently interacts with text embedding for cross-modal understanding. They usually present that the offline pattern is necessary for RVOS, yet model limited temporal association within each clip. In this work, we break up the previous offline belief and propose a simple yet effective online model using explicit query propagation, named OnlineRefer. Specifically, our approach leverages target cues that gather semantic information and position prior to improve the accuracy and ease of referring predictions for the current frame. Furthermore, we generalize our online model into a semi-online framework to be compatible with video-based backbones. To show the effectiveness of our method, we evaluate it on four benchmarks, \ie, Refer-Youtube-VOS, Refer-DAVIS17, A2D-Sentences, and JHMDB-Sentences. Without bells and whistles, our OnlineRefer with a Swin-L backbone achieves 63.5 J&F and 64.8 J&F on Refer-Youtube-VOS and Refer-DAVIS17, outperforming all other offline methods.Comment: Accepted by ICCV2023. The code is at https://github.com/wudongming97/OnlineRefe