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Dwarf spheroidal galaxies provide well-known challenges to the standard cold and collisionless dark matter scenario 1 , 2 : the too-big-to-fail problem (namely the mismatch between the observed mass enclosed within their half-light radius 3 , 4 and cold dark matter N -body predictions 5 , 6 ) and the hints for inner constant-density cores 7 – 10 . While these controversies may be alleviated by baryonic physics and environmental effects 11 – 15 , revisiting the standard lore of cold and collisionless dark matter remains an intriguing possibility. Self-interacting dark matter 16 , 17 may be the successful proposal to such a small-scale crisis 18 , 19 . Self-interactions correlate dark matter and baryon distributions, allowing for constant-density cores in low-surface-brightness galaxies 20 – 23 . Here, we perform a data-driven study of the too-big-to-fail problem of Milky Way dwarf spheroidals within the self-interacting dark matter paradigm. We find a good description of their stellar kinematics and compatibility with the concentration–mass relation from the pure cold dark matter simulation in ref. 24 . Within this concentration–mass relation, a subset of Milky Way dwarfs are well fitted by cross-sections of 0.5–3.0 cm 2  g −1 , while others point to values greater than 10 cm 2  g −1 . A data-driven study of the too-big-to-fail problem of Milky Way dwarf spheroidals within the self-interacting dark matter paradigm finds a good description of their stellar kinematics and compatibility with the concentration–mass relation of pure cold dark matter simulations.

Galactic rotation curves exhibit diverse behavior in the inner regions while obeying an organizing principle; i.e., they can be approximately described by a radial acceleration relation or the modified Newtonian dynamics phenomenology. We analyze the rotation curve data from the SPARC sample and explicitly demonstrate that both the diversity and uniformity are naturally reproduced in a hierarchical structure formation model with the addition of dark matter self-interactions. The required concentrations of the dark matter halos are fully consistent with the concentration-mass relation predicted by the Planck cosmological model. The inferred stellar mass-to-light (3.6μm) ratios scatter around0.5M⊙/L⊙, as expected from population synthesis models, leading to a tight radial acceleration relation and a baryonic Tully-Fisher relation. The inferred stellar-halo mass relation is consistent with the expectations from abundance matching. These results provide compelling arguments in favor of the idea that the inner halos of galaxies are thermalized due to dark matter self-interactions.

Ion channels are involved in a variety of fundamental physiological processes, and their malfunction causes numerous human diseases. Therefore, ion channels represent a class of attractive drug targets and a class of important off-targets for in vitro pharmacological profiling. In the past decades, the rapid progress in developing functional assays and instrumentation has enabled high throughput screening （HTS） campaigns on an expanding list of channel types. Chronologically, HTS methods for ion channels include the ligand binding assay, flux-based assay, fluorescence-based assay, and automated electrophysiological assay. In this review we summarize the current HTS technologies for different ion channel classes and their applications.

Background For many years, the role of the microbiome in tumor progression, particularly the tumor microbiome, was largely overlooked. The connection between the tumor microbiome and the tumor genome still requires further investigation. Methods The TCGA microbiome and genome data were obtained from Haziza et al.’s article and UCSC Xena database, respectively. Separate WGCNA networks were constructed for the tumor microbiome and genomic data after filtering the datasets. Correlation analysis between the microbial and mRNA modules was conducted to identify oncogenome associated microbiome module (OAM) modules, with three microbial modules selected for each tumor type. Reactome analysis was used to enrich biological processes. Machine learning techniques were implemented to explore the tumor type-specific enrichment and prognostic value of OAM, as well as the ability of the tumor microbiome to differentiate TP53 mutations. Results We constructed a total of 182 tumor microbiome and 570 mRNA WGCNA modules. Our results show that there is a correlation between tumor microbiome and tumor genome. Gene enrichment analysis results suggest that the genes in the mRNA module with the highest correlation with the tumor microbiome group are mainly enriched in infection, transcriptional regulation by TP53 and antigen presentation. The correlation analysis of OAM with CD8+ T cells or TAM1 cells suggests the existence of many microbiota that may be involved in tumor immune suppression or promotion, such as Williamsia in breast cancer, Biostraticola in stomach cancer, Megasphaera in cervical cancer and Lottiidibacillus in ovarian cancer. In addition, the results show that the microbiome-genome prognostic model has good predictive value for short-term prognosis. The analysis of tumor TP53 mutations shows that tumor microbiota has a certain ability to distinguish TP53 mutations, with an AUROC value of 0.755. The tumor microbiota with high importance scores are Corallococcus , Bacillus and Saezia . Finally, we identified a potential anti-cancer microbiota, Tissierella, which has been shown to be associated with improved prognosis in tumors including breast cancer, lung adenocarcinoma and gastric cancer. Conclusion There is an association between the tumor microbiome and the tumor genome, and the existence of this association is not accidental and could change the landscape of tumor research.

The efficiency of industrial fermentation process mainly depends on carbon yield, final titer and productivity. To improve the efficiency of l -lysine production from mixed sugar, we engineered carbohydrate metabolism systems to enhance the effective use of sugar in this study. A functional metabolic pathway of sucrose and fructose was engineered through introduction of fructokinase from Clostridium acetobutylicum . l -lysine production was further increased through replacement of phosphoenolpyruvate-dependent glucose and fructose uptake system (PTS Glc and PTS Fru ) by inositol permeases (IolT1 and IolT2) and ATP-dependent glucokinase (ATP-GlK). However, the shortage of intracellular ATP has a significantly negative impact on sugar consumption rate, cell growth and l -lysine production. To overcome this defect, the recombinant strain was modified to co-express bifunctional ADP-dependent glucokinase (ADP-GlK/PFK) and NADH dehydrogenase (NDH-2) as well as to inactivate SigmaH factor (SigH), thus reducing the consumption of ATP and increasing ATP regeneration. Combination of these genetic modifications resulted in an engineered C. glutamicum strain K-8 capable of producing 221.3 ± 17.6 g/L l -lysine with productivity of 5.53 g/L/h and carbon yield of 0.71 g/g glucose in fed-batch fermentation. As far as we know, this is the best efficiency of l -lysine production from mixed sugar. This is also the first report for improving the efficiency of l -lysine production by systematic modification of carbohydrate metabolism systems.

A high-fat diet (HFD) causes obesity-associated morbidities involved in macroautophagy and chaperone-mediated autophagy (CMA). AMPK, the mediator of macroautophage, has been reported to be inactivated in HFD-caused renal injury. However, PAX2, the mediator for CMA, has not been reported in HFD-caused renal injury. Here we report that HFD-caused renal injury involved the inactivation of Pax2 and Ampk, and the activation of soluble epoxide hydrolase (sEH), in a murine model. Specifically, mice fed on an HFD for 2, 4, and 8 wk showed time-dependent renal injury, the significant decrease in renal Pax2 and Ampk at both mRNA and protein levels, and a significant increase in renal sEH at mRNA, protein, and molecular levels. Also, administration of an sEH inhibitor, 1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl)urea, significantly attenuated the HFDcaused renal injury, decreased renal sEH consistently at mRNA and protein levels, modified the renal levels of sEH-mediated epoxyeicosatrienoic acids (EETs) and dihydroxyeicosatrienoic acids (DHETs) as expected, and increased renal Pax2 and Ampk at mRNA and/or protein levels. Furthermore, palmitic acid (PA) treatment caused significant increase in Mcp-1, and decrease in both Pax2 and Ampk in murine renal mesangial cells (mRMCs) time- and dose-dependently. Also, 14(15)-EET (a major substrate of sEH), but not its sEH-mediated metabolite 14,15-DHET, significantly reversed PA-induced increase in Mcp-1, and PA-induced decrease in Pax2 and Ampk. In addition, plasmid construction revealed that Pax2 may positively regulate Ampk transcriptionally in mRMCs. This study provides insights into and therapeutic target for the HFD-mediated renal injury.

A bstract We study self-interacting dark matter signatures at the Large Hadron Collider. A light dark photon, mediating dark matter self-interactions, can bind dark matter particles to form a bound state when they are produced via a heavy pseduoscalar in pp collisions. The bound state can further annihilate into a pair of boosted dark photons, which subsequently decay into charged leptons through a kinetic mixing portal, resulting in striking displaced lepton jet signals. After adapting the analysis used in the ATLAS experiment, we explore the reach of the model parameters at the 13 TeV run with an integrated luminosity of 300 fb −1 . For heavy dark matter, the displaced lepton jet searches can surpass traditional monojet signals in setting the lower bound on the pseduoscalar mass. If a positive signal is detected, we can probe the dark matter mass and the dark coupling constant after combining both the displaced lepton jet and monojet searches. We further show the CMS dimuon search can be sensitive to the final state radiation of the dark photon. Our results demonstrate terrestrial collider experiments complement astronomical observations of galaxies in the search of the self-interacting nature of dark matter.

Abstract Traditional amino acid producers typically exhibit the low glucose uptake rate and growth deficiency, resulting in a long fermentation time because of the accumulation of side mutations in breeding of strains. In this study, we demonstrate that the efficiency of l-lysine production in traditional l-lysine producer Corynebacterium glutamicum ZL-9 can be improved by rationally engineering glucose uptake systems. To do this, different bypasses for glucose uptake were investigated to reveal the best glucose uptake system for l-lysine production in traditional l-lysine producer. This study showed that overexpression of the key genes in PTSGlc or non-PTSGlc increased the glucose consumption, growth rate, and l-lysine production. However, increasing the function of PTSGlc in glucose uptake led to the increase of by-products, especially for plasmid-mediated expression system. Increasing the participation of non-PTSGlc in glucose utilization showed the best glucose uptake system for l-lysine production. The final strain ZL-92 with increasing the expression level of iolT1, iolT2 and ppgK could produce 201.6 ± 13.8 g/L of l-lysine with a productivity of 5.04 g/L/h and carbon yield of 0.65 g/(g glucose) in fed-batch culture. This is the first report of a rational modification of glucose uptake systems that improve the efficiency of l-lysine production through increasing the participation of non-PTSGlc in glucose utilization in traditional l-lysine producer. Similar strategies can be also used for producing other amino acids or their derivatives.

Aim： hERG potassium channels display miscellaneous interactions with diverse chemical scaffolds. In this study we assessed the hERG inhibition in a large compound library of diverse chemical entities and provided data for better understanding of the mechanisms underlying promiscuity of hERG inhibition. Methods： Approximately 300 000 compounds contained in Molecular Library Small Molecular Repository （MLSMR） library were tested. Compound profiling was conducted on hERG-CHO cells using the automated patch-clamp platform-lonWorks QuattroTM. Results： The compound library was tested at I and 10 pmol/L. IC50 values were predicted using a modified 4-parameter logistic model. Inhibitor hits were binned into three groups based on their potency： high （IC50〈1 μmol/L）, intermediate （1 μmol/L〈 IC50〈10 μmol/L）, and low （IC50〉10 μmol/L） with hit rates of 1.64%, 9.17% and 16.63%, respectively. Six physiochemical properties of each compound were acquired and calculated using ACD software to evaluate the correlation between hERG inhibition and the properties： hERG inhibition was positively correlative to the physiochemical properties ALogP, molecular weight and RTB, and negatively correlative to TPSA. Conclusion： Based on a large diverse compound collection, this study provides experimental evidence to understand the promiscuity of hERG inhibition. This study further demonstrates that hERG liability compounds tend to be more hydrophobic, high-molecular, flexible and polarizable.

In this paper, we develop a more general framework of block-structured Markov processes in the queueing study of blockchain systems, which can provide analysis both for the stationary performance measures and for the sojourn time of any transaction or block. In addition, an original aim of this paper is to generalize the two-stage batch-service queueing model studied in Li et al. (Blockchain queue theory. In: International conference on computational social networks. Springer: New York; 2018 . p. 25–40) both “from exponential to phase-type” service times and “from Poisson to MAP” transaction arrivals. Note that the MAP transaction arrivals and the two stages of PH service times make our blockchain queue more suitable to various practical conditions of blockchain systems with crucial factors, for example, the mining processes, the block generations, the blockchain building and so forth. For such a more general blockchain queueing model, we focus on two basic research aspects: (1) using the matrix-geometric solution, we first obtain a sufficient stable condition of the blockchain system. Then, we provide simple expressions for the average stationary number of transactions in the queueing waiting room and the average stationary number of transactions in the block. (2) However, on comparing with Li et al. ( 2018 ), analysis of the transaction–confirmation time becomes very difficult and challenging due to the complicated blockchain structure. To overcome the difficulties, we develop a computational technique of the first passage times by means of both the PH distributions of infinite sizes and the RG factorizations. Finally, we hope that the methodology and results given in this paper will open a new avenue to queueing analysis of more general blockchain systems in practice and can motivate a series of promising future research on development of blockchain technologies.