Is exponential gravity a viable description for the whole cosmological history?

Here we analysed a particular type of $F(R)$ gravity, the so-called exponential gravity which includes an exponential function of the Ricci scalar in the action. Such term represents a correction to the usual Hilbert-Einstein action. By using Supernovae Ia, Barionic Acoustic Oscillations, Cosmic Microwave Background and $H(z)$ data, the free parameters of the model are well constrained. The results show that such corrections to General Relativity become important at cosmological scales and at late-times, providing an alternative to the dark energy problem. In addition, the fits do not determine any significant difference statistically with respect to the $\Lambda$CDM model. Finally, such model is extended to include the inflationary epoch in the same gravitational Lagrangian. As shown in the paper, the additional terms can reproduce the inflationary epoch and satisfy the constraints from Planck data.Comment: 20 pages, 6 figures, analysis extended, version published in EPJ

Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales

Segmentation of kidney lesions with attention model based on Deeplab

We participate this challenge by developing a hierarchical framework. We build the model from two fully convolutional networks: (1) a simple Unet model to normalize the input iamges, (2) a segmentaion network which is an attention model based on Deeplab model. Two models are connected in tandem and trained end-to-end. To ensure a better results, we use the preprocess method proposed by nnUnet in our experiments

B L O O D M A N A G E M E N T Failure mode and effect analysis in blood transfusion: a proactive tool to reduce risks

BACKGROUND: The aim of blood transfusion risk management is to improve the quality of blood products and to assure patient safety. We utilize failure mode and effect analysis (FMEA), a tool employed for evaluating risks and identifying preventive measures to reduce the risks in blood transfusion. STUDY DESIGN AND METHODS: The failure modes and effects occurring throughout the whole process of blood transfusion were studied. Each failure mode was evaluated using three scores: severity of effect (S), likelihood of occurrence (O), and probability of detection (D). Risk priority numbers (RPNs) were calculated by multiplying the S, O, and D scores. The plan-do-checkact cycle was also used for continuous improvement. RESULTS: Analysis has showed that failure modes with the highest RPNs, and therefore the greatest risk, were insufficient preoperative assessment of the blood product requirement (RPN, 245), preparation time before infusion of more than 30 minutes (RPN, 240), blood transfusion reaction occurring during the transfusion process (RPN, 224), blood plasma abuse (RPN, 180), and insufficient and/or incorrect clinical information on request form (RPN, 126). After implementation of preventative measures and reassessment, a reduction in RPN was detected with each risk. The failure mode with the second highest RPN, namely, preparation time before infusion of more than 30 minutes, was shown in detail to prove the efficiency of this tool. CONCLUSIONS: FMEA evaluation model is a useful tool in proactively analyzing and reducing the risks associated with the blood transfusion procedure

tRNA-Derived Fragments in Podocytes with Adriamycin-Induced Injury Reveal the Potential Mechanism of Idiopathic Nephrotic Syndrome

Idiopathic nephrotic syndrome (INS) is a disease involving injury to podocytes in the glomerular filtration barrier, and its specific causes have not been elucidated. Transfer RNA-derived fragments (tRFs), products of precise tRNA cleavage, have been indicated to play critical roles in various diseases. Currently, there is no relevant research on the role of tRFs in INS. This study intends to explore the changes in and importance of tRFs during podocyte injury in vitro and to further analyze the potential mechanism of INS. Differentially expressed tRFs in the adriamycin-treated group were identified by high-throughput sequencing and further verified by quantitative RT-PCR. In total, 203 tRFs with significant differential expression were identified, namely, 102 upregulated tRFs and 101 downregulated tRFs (q<0.05, ∣log2FC∣≥2). In particular, AS-tDR-008924, AS-tDR-011690, tDR-003634, AS-tDR-013354, tDR-011031, AS-tDR-001008, and AS-tDR-007319 were predicted to be involved in podocyte injury by targeting the Gpr, Wnt, Rac1, and other genes. Furthermore, gene ontology analysis showed that these differential tRFs were strongly associated with podocyte injury processes such as protein binding, cell adhesion, synapses, the actin cytoskeleton, and insulin-activate receptor activity. KEGG pathway analysis predicted that they participated in the PI3K-Akt signaling pathway, Wnt signaling pathway, and Ras signaling pathway. It was reported that these pathways contribute to podocyte injury. In conclusion, our study revealed that changes in the expression levels of tRFs might be involved in INS. Seven of the differentially expressed tRFs might play important roles in the process of podocyte injury and are worthy of further study

Additional file 1: Table S1. of The common promoter polymorphism rs11666254 downregulates FPR2/ALX expression and increases risk of sepsis in patients with severe trauma

Distribution of SNPs within the FPR2/ALX gene and in 3-kb regions upstream and downstream of the HapMap database for the CHB population. Tag SNPs were selected according to the HapMap CHB (Han Chinese in Beijing) data (version 3, release R2) using Haploview version 4.2. (DOCX 13 kb

Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales