On a problem of Henning and Yeo about the transversal number of uniform linear systems whose 2-packing number is fixed

For $r\geq2$, let $(P,\mathcal{L})$ be an $r$-uniform linear system. The transversal number $\tau(P,\mathcal{L})$ of $(P,\mathcal{L})$ is the minimum number of points that intersect every line of $(P,\mathcal{L})$. The 2-packing number $\nu_2(P,\mathcal{L})$ of $(P,\mathcal{L})$ is the maximum number of lines such that the intersection of any three of them is empty. In [Discrete Math. 313 (2013), 959--966] Henning and Yeo posed the following question: Is it true that if $(P,\mathcal{L})$ is a $r$-uniform linear system then $\tau(P,\mathcal{L})\leq\displaystyle\frac{|P|+|\mathcal{L}|}{r+1}$ holds for all $k\geq2$?. In this paper, some results about of $r$-uniform linear systems whose 2-packing number is fixed which satisfies the inequality are given

Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus)

[EN] Skin is an important trait for Rex rabbits and skin development is influenced by many processes, including hair follicle cycling, keratinocyte differentiation and formation of coat colour and skin morphogenesis. We identified differentially expressed microRNAs (miRNAs) between the back and belly skin in Rex rabbits. In total, 211 miRNAs (90 upregulated miRNAs and 121 downregulated miRNAs) were identified with a |log2 (fold change)|>1 and P-value<0.05. Using target gene prediction for the miRNAs, differentially expressed predicted target genes were identified and the functional enrichment and signalling pathways of these target genes were processed to reveal their biological functions. A number of differentially expressed miRNAs were found to be involved in regulation of the cell cycle, skin epithelium differentiation, keratinocyte proliferation, hair follicle development and melanogenesis. In addition, target genes regulated by miRNAs play key roles in the activities of the Hedgehog signalling pathway, Wnt signalling pathway, Osteoclast differentiation and MAPK pathway, revealing mechanisms of skin development. Nine candidate miRNAs and 5 predicted target genes were selected for verification of their expression by quantitative reverse transcription polymerase chain reaction. A regulation network of miRNA and their target genes was constructed by analysing the GO enrichment and signalling pathways. Further studies should be carried out to validate the regulatory relationships between candidate miRNAs and their target genes.This study was supported by the Modern Agricultural Industrial System Special Funding (CARS-44-A-1), the Priority Academic Programme Development of Jiangsu Higher Education Institutions (2014-134) and the General Programme of Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJB230001).Zhao, B.; Chen, Y.; Mu, L.; Hu, S.; Wu, X. (2018). Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus). World Rabbit Science. 26(2):179-190. https://doi.org/10.4995/wrs.2018.7058SWORD179190262Adamidi C. 2008. Discovering microRNAs from deep sequencing data using miRDeep. Nature Biotechnol., 26: 407-415. https://doi.org/10.1038/nbt1394Adijanto J., Castorino J.J., Wang Z.X., Maminishkis A., Grunwald G.B., Philp N.J. 2012. Microphthalmia-associated transcription factor (MITF) promotes differentiation of human retinal pigment epithelium (RPE) by regulating microRNAs-204/211 expression. J. Biol. Chem., 287: 20491-https://doi.org/10.1074/jbc.M112.354761Ahmed M.I., Alam M., Emelianov V.U., Poterlowicz K., Patel A., Sharov A.A., Mardaryev A.N., Botchkareva N.V. 2014. MicroRNA-214 controls skin and hair follicle development by modulating the activity of the Wnt pathway. J. Cell Biol., 207: 549-567. https://doi.org/10.1083/jcb.201404001Alexander M., Kawahara G., Motohashi N., Casar J., Eisenberg I., Myers J., Gasperini M., Estrella E., Kho A., Mitsuhashi S. 2013. MicroRNA-199a is induced in dystrophic muscle and affects WNT signaling, cell proliferation, and myogenic differentiation. Cell Death Diff., 20: 1194-1208. https://doi.org/10.1038/cdd.2013.62Anders S. 2010. Analysing RNA-Seq data with the DESeq package. Mol. Biol., 43: 1-17.Andl T., Botchkareva N.V. 2015. MicroRNAs (miRNAs) in the control of HF development and cycling: the next frontiers in hair research. Exp. Dermatol., 24: 821-826. https://doi.org/10.1111/exd.12785Andl T., Reddy S.T., Gaddapara T., Millar S.E. 2002. WNT signals are required for the initiation of hair follicle development. Develop. Cell, 2: 643-653. https://doi.org/10.1016/S1534-5807(02)00167-3Antonini D., Russo MT., De Rosa L., Gorrese M., Del Vecchio L., Missero C. 2010. Transcriptional repression of miR-34 family contributes to p63-mediated cell cycle progression in epidermal cells. J. Invest. Dermatol., 130: 1249-1257. https://doi.org/10.1038/jid.2009.438Athar M., Tang X., Lee J.L., Kopelovich L., Kim AL. 2006. Hedgehog signalling in skin development and cancer. Exp. Dermatol., 15: 667-677. https://doi.org/10.1111/j.1600-0625.2006.00473.xBartel D.P. 2004. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116: 281-297.https://doi.org/10.1016/S0092-8674(04)00045-5Bashirullah A., Pasquinelli A.E., Kiger A.A., Perrimon N., Ruvkun G., Thummel C.S. 2003. Coordinate regulation of small temporal RNAs at the onset of Drosophila metamorphosis. Dev. Biol., 259: 1-8. https://doi.org/10.1016/S0012-1606(03)00063-0Bommer GT., Gerin I., Feng Y., Kaczorowski AJ., Kuick R., Love RE., Zhai Y., Giordano TJ., Qin ZS., Moore BB. 2007. p53-mediated activation of miRNA34 candidate tumor-suppressor genes. Curr. Biol., 17: 1298-1307. https://doi.org/10.1016/j.cub.2007.06.068Braun C.J., Zhang X., Savelyeva I., Wolff S., Moll U.M., Schepeler T., Ørntoft T.F., Andersen C.L., Dobbelstein M. 2008. p53-Responsive micrornas 192 and 215 are capable of inducing cell cycle arrest. Cancer Res., 68: 10094-10104.https://doi.org/10.1158/0008-5472.CAN-08-1569Callis T.E., Chen J.F., Wang D.Z. 2007. MicroRNAs in skeletal and cardiac muscle development. Dna Cell Biol., 26: 219-225. https://doi.org/10.1089/dna.2006.0556Caramuta S., Egyházi S., Rodolfo M., Witten D., Hansson J., Larsson C., Lui W.O. 2010. MicroRNA expression profiles associated with mutational status and survival in malignant melanoma. J. Invest. Dermatol., 130: 2062-2070. https://doi.org/10.1038/jid.2010.63Chen C.H., Sakai Y., Demay M.B. 2001. Targeting expression of the human vitamin D receptor to the keratinocytes of vitamin D receptor null mice prevents alopecia. Endocrinology, 142: 5386-5386. https://doi.org/10.1210/endo.142.12.8650D'Juan T.F., Shariat N., Park C.Y., Liu H.J., Mavropoulos A., McManus M.T. 2013. Partially penetrant postnatal lethality of an epithelial specific MicroRNA in a mouse knockout. Plos One 8: e76634. https://doi.org/10.1371/journal.pone.0076634DeYoung M.P., Johannessen C.M., Leong C.O., Faquin W., Rocco J.W., Ellisen L.W. 2006. Tumor-specific p73 up-regulation mediates p63 dependence in squamous cell carcinoma. Cancer Res., 66: 9362-9368. https://doi.org/10.1158/0008-5472.CAN-06-1619Eckert R.L., Welter J.F. 1996. Transcription factor regulation of epidermal keratinocyte gene expression. Mol. Biol. Rep., 23: 59-70. https://doi.org/10.1007/BF00357073Enright A.J., Bino J., Ulrike G., Thomas T., Chris S., Marks D.S. 2004. MicroRNA targets in Drosophila. Gen. Biol., 5: R1-R1. https://doi.org/10.1186/gb-2003-5-1-r1Fontanesi L., Scotti E., Allain D., Dall'Olio S. 2014. A frameshift mutation in the melanophilin gene causes the dilute coat colour in rabbit (Oryctolagus cuniculus) breeds. Anim. Genet., 45: 248-255. https://doi.org/10.1111/age.12104Fontanesi L., Vargiolu M., Scotti E., Latorre R., Pellegrini M.S.F., Mazzoni M., Asti M., Chiocchetti R., Romeo G., Clavenzani P. 2014. The KIT gene is associated with the English spotting coat color locus and congenital megacolon in Checkered Giant rabbits (Oryctolagus cuniculus). Plos One 9: e93750. https://doi.org/10.1371/journal.pone.0093750Fuchs E. 2007. Scratching the surface of skin development. Nature, 445: 834-842. https://doi.org/10.1038/nature05659Georges S.A., Chau B.N., Braun C.J., Zhang X., Dobbelstein M. 2009. Cell cycle arrest or apoptosis by p53: are microRNAs-192/215 and-34 making the decision? Cell Cycle 8: 677-682. https://doi.org/10.4161/cc.8.5.8076Jackson S.J., Zhang Z., Feng D., Flagg M., O'Loughlin E., Wang D., Stokes N., Fuchs E., Yi R. 2013. Rapid and widespread suppression of self-renewal by microRNA-203 during epidermal differentiation. Development, 140: 1882-1891. https://doi.org/10.1242/dev.089649Katoh Y., Katoh M. 2008. Hedgehog signaling, epithelial-tomesenchymal transition and miRNA (review). Int. J. Mol. Med., 22: 271-275. https://doi.org/10.3892/ijmm_00000019Kim K., Vinayagam A., Perrimon N. 2014. A rapid genomewide microRNA screen identifies miR-14 as a modulator of Hedgehog signaling. Cell Rep., 7: 2066-2077. https://doi.org/10.1016/j.celrep.2014.05.025Kochegarov A., Moses A., Lian W., Meyer J., Hanna M.C., Lemanski L.F. 2013. A new unique form of microRNA from human heart, microRNA-499c, promotes myofibril formation and rescues cardiac development in mutant axolotl embryos. J. Biomed. Sci., 20: 1. https://doi.org/10.1186/1423-0127-20-20Kozomara, A., Griffiths J. 2014. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res., 42: 68-73. https://doi.org/10.1093/nar/gkt1181Kureel J., Dixit M., Tyagi A., Mansoori M., Srivastava K., Raghuvanshi A., Maurya R., Trivedi R., Goel A., Singh D. 2014. miR-542-3p suppresses osteoblast cell proliferation and differentiation, targets BMP-7 signaling and inhibits bone formation. Cell Death Dis., 5: e1050. https://doi.org/10.1038/cddis.2014.4Langmead B., Salzberg S.L. 2012. Fast gapped-read alignment with Bowtie 2. Nat. Methods, 9: 357-359. https://doi.org/10.1038/nmeth.1923Lim X., Nusse R. 2013. Wnt signaling in skin development, homeostasis, and disease. CSH Perspect. Biol., 5: a008029. https://doi.org/10.1101/cshperspect.a008029Liu Z., Xiao H., Li H., Zhao Y., Lai S., Yu X., Cai T., Du C., Zhang W., Li J. 2012. Identification of conserved and novel microRNAs in cashmere goat skin by deep sequencing. Plos One 7: e50001. https://doi.org/10.1371/journal.pone.0050001Mardaryev A.N., Ahmed M.I., Vlahov N.V., Fessing M.Y., Gill J.H., Sharov A.A., Botchkareva N.V. 2010. Micro-RNA-31 controls hair cycle-associated changes in gene expression programs of the skin and hair follicle. FASEB J. 24: 3869-3881. https://doi.org/10.1096/fj.10-160663Mills A.A., Zheng B., Wang X.J., Vogel H., Roop D.R., Bradley A. 1999. p63 is a p53 homologue required for limb and epidermal morphogenesis. Nature, 398: 708-713. https://doi.org/10.1038/19531Mueller D.W., Rehli M., Bosserhoff A.K. 2009. miRNA expression profiling in melanocytes and melanoma cell lines reveals miRNAs associated with formation and progression of malignant melanoma. J. Invest. Dermatol., 129: 1740-1751. https://doi.org/10.1038/jid.2008.452Naeem H., Küffner R., Csaba G., Zimmer R. 2010. miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature. Bmc Bioinformatics, 11: 135. https://doi.org/10.1186/1471-2105-11-135Neilson J.R., Zheng G.X., Burge CB., Sharp P.A. 2007. Dynamic regulation of miRNA expression in ordered stages of cellular development. Gene. Dev., 21: 578-589. https://doi.org/10.1101/gad.1522907Oda Y., Ishikawa M.H., Hawker N.P., Yun Q.C., Bikle D.D. 2007. Differential role of two VDR coactivators, DRIP205 and SRC-3, in keratinocyte proliferation and differentiation. J. Steroid Biochem., 103: 776-780. https://doi.org/10.1016/j.jsbmb.2006.12.069Pan L., Liu Y., Wei Q., Xiao C., Ji Q., Bao G., Wu X. 2015. Solexa-Sequencing Based Transcriptome Study of Plaice Skin Phenotype in Rex Rabbits (Oryctolagus cuniculus). Plos One: 10. https://doi.org/10.1371/journal.pone.0124583Rosenfield R.L., Deplewski D., Greene M.E. 2001. Peroxisome proliferator-activated receptors and skin development. Horm. Res. Paediat., 54: 269-274. https://doi.org/10.1159/000053270Schneider M.R. 2012. MicroRNAs as novel players in skin development, homeostasis and disease. Brit. J. Dermatol., 166: 22-28. https://doi.org/10.1111/j.1365-2133.2011.10568.xSenoo M., Pinto F., Crum C.P., McKeon F. 2007. p63 Is essential for the proliferative potential of stem cells in stratified epithelia. Cell, 129: 523-536. https://doi.org/10.1016/j.cell.2007.02.045Song B., Wang Y., Kudo K., Gavin E.J., Xi Y., Ju J. 2008. miR-192 Regulates dihydrofolate reductase and cellular proliferation through the p53-microRNA circuit. Clin. Cancer Res., 14: 8080-8086. https://doi.org/10.1158/1078-0432.CCR-08-1422Suh K.S., Mutoh M., Mutoh T., Li L., Ryscavage A., Crutchley J.M., Dumont R.A., Cheng C., Yuspa S.H. 2007. CLIC4 mediates and is required for Ca2+-induced keratinocyte differentiation. J. Cell Sci., 120: 2631-2640. https://doi.org/10.1242/jcs.002741Tao Y. 2010. Studies on the quality of rex rabbit fur. World Rabbit Sci., 2: 21-24. https://doi.org/10.4995/wrs.1994.213Tian X., Jiang J., Fan R., Wang H., Meng X., He X., He J., Li H., Geng J., Yu X. 2012. Identification and characterization of microRNAs in white and brown alpaca skin. BMC genomics 13: 1.https://doi.org/10.1186/1471-2164-13-555Vadlakonda L., Pasupuleti M., Pallu R. 2014. Role of PI3K-AKTmTOR and Wnt signaling pathways in transition of G1-S phase of cell cycle in cancer cells. Front. Oncol., 3: 85. https://doi.org/10.3389/fonc.2013.00085van Amerongen R., Fuerer C., Mizutani M., Nusse R. 2012. Wnt5a can both activate and repress Wnt/β-catenin signaling during mouse embryonic development. Dev. Biol., 369: 101-114. https://doi.org/10.1016/j.ydbio.2012.06.020Vousden K.H., Lane D.P. 2007. p53 in health and disease. Nat. Rev. Mol. Cell Biol., 8: 275-283. https://doi.org/10.1038/nrm2147Wang P., Li Y., Hong W., Zhen J., Ren J., Li Z., Xu A. 2012. The changes of microRNA expression profiles and tyrosinase related proteins in MITF knocked down melanocytes. Mol. BioSyst., 8: 2924-2931. https://doi.org/10.1039/c2mb25228gWhelan J.T., Hollis S.E., Cha D.S., Asch A.S., Lee M.H. 2012. Post‐transcriptional regulation of the Ras‐ERK/MAPK signaling pathway. J. Cell Physiol., 227: 1235-1241. https://doi.org/10.1002/jcp.22899Xia H., Ooi L.L.P.J., Hui K.M. 2013. MicroRNA-216a/217-induced epithelial-mesenchymal transition targets PTEN and SMAD7 to promote drug resistance and recurrence of liver cancer. Hepatology, 58: 629-641. https://doi.org/10.1002/hep.26369Yang A., Schweitzer R., Sun D., Kaghad M., Walker N., Bronson R.T., Tabin C., Sharpe A., Caput D., Crum C. 1999. p63 is essential for regenerative proliferation in limb, craniofacial and epithelial development. Nature, 398: 714-718. https://doi.org/10.1038/19539Yu J., Peng H., Ruan Q., Fatima A., Getsios S., Lavker R.M. 2010. MicroRNA-205 promotes keratinocyte migration via the lipid phosphatase SHIP2. FASEB J. 24: 3950-3959. https://doi.org/10.1096/fj.10-157404Yu J., Ryan D.G., Getsios S., Oliveira-Fernandes M., Fatima A., Lavker R.M. 2008. MicroRNA-184 antagonizes microRNA-205 to maintain SHIP2 levels in epithelia. In Proc.: National Academy of Sciences 105: 19300-19305. https://doi.org/10.1073/pnas.0803992105Zhang L., Nie Q., Su Y., Xie X., Luo W., Jia X., Zhang X. 2013. MicroRNA profile analysis on duck feather follicle and skin with high-throughput sequencing technology. Gene, 519: 77-81. https://doi.org/10.1016/j.gene.2013.01.043Zhao Y., Wang P., Meng J., Ji Y., Xu D., Chen T., Fan R., Yu X., Yao J., Dong C. 2015. MicroRNA-27a-3p Inhibits Melanogenesis in Mouse Skin Melanocytes by Targeting Wnt3a. Int. J. Mol. Sci., 16: 10921-10933. https://doi.org/10.3390/ijms16051092

Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks

Recent advancements in foundation models, typically trained with self-supervised learning on large-scale and diverse datasets, have shown great potential in medical image analysis. However, due to the significant spatial heterogeneity of medical imaging data, current models must tailor specific structures for different datasets, making it challenging to leverage the abundant unlabeled data. In this work, we propose a universal foundation model for medical image analysis that processes images with heterogeneous spatial properties using a unified structure. To accomplish this, we propose spatially adaptive networks (SPAD-Nets), a family of networks that dynamically adjust the structures to adapt to the spatial properties of input images, to build such a universal foundation model. We pre-train a spatial adaptive visual tokenizer (SPAD-VT) and then a spatial adaptive Vision Transformer (SPAD-ViT) via masked image modeling (MIM) on 55 public medical image datasets. The pre-training data comprises over 9 million image slices, representing the largest, most comprehensive, and most diverse dataset to our knowledge for pre-training universal foundation models for medical image analysis. The experimental results on downstream medical image classification and segmentation tasks demonstrate the superior performance and label efficiency of our model. Our code is available at https://github.com/function2-llx/PUMIT

Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints

Conveying the linguistic content and maintaining the source speech's speaking style, such as intonation and emotion, is essential in voice conversion (VC). However, in a low-resource situation, where only limited utterances from the target speaker are accessible, existing VC methods are hard to meet this requirement and capture the target speaker's timber. In this work, a novel VC model, referred to as MFC-StyleVC, is proposed for the low-resource VC task. Specifically, speaker timbre constraint generated by clustering method is newly proposed to guide target speaker timbre learning in different stages. Meanwhile, to prevent over-fitting to the target speaker's limited data, perceptual regularization constraints explicitly maintain model performance on specific aspects, including speaking style, linguistic content, and speech quality. Besides, a simulation mode is introduced to simulate the inference process to alleviate the mismatch between training and inference. Extensive experiments performed on highly expressive speech demonstrate the superiority of the proposed method in low-resource VC.Comment: Accepted by ICASSP 202

Characterisation and functional analysis of the WIF1 gene and its role in hair follicle growth and development of the Angora rabbit

[EN] Growth and development of hair follicles (HF) is a complex and dynamic process in most mammals. As HF growth and development regulate rabbit wool yield, exploring the role of genes involved in HF growth and development may be relevant. In this study, the coding sequence of the Angora rabbit (Oryctolagus cuniculus) WIF1 gene was cloned. The length of the coding region sequence was found to be 1140 bp, which encodes 379 amino acids. Bioinformatics analysis indicated that the WIF1 protein was unstable, hydrophilic and located in the extracellular region, contained a putative signal peptide and exhibited a high homology in different mammals. Moreover, WIF1 was significantly downregulated in the high wool production in the Angora rabbit group. Overexpression and knockdown studies revealed that WIF1 regulates HF growth and development-related genes and proteins, such as LEF1 and CCND1. WIF1 activated β-catenin/TCF transcriptional activity, promoted cell apoptosis and inhibited cellular proliferation. These results indicate that WIF1 might be important for HF development. This study, therefore, provides a theoretical foundation for investigating WIF1 in HF growth and development.This research was funded by This research was funded by National Natural Science Foundation of China (Grant No. 32102529), China Agriculture Research System of MOF and MARA (CARS-43-A-1).Zhao, B.; Li, J.; Zhang, X.; Bao, Z.; Chen, Y.; Wu, X. (2022). Characterisation and functional analysis of the WIF1 gene and its role in hair follicle growth and development of the Angora rabbit. World Rabbit Science. 30(3):209-218. https://doi.org/10.4995/wrs.2022.1735320921830

Fe, N, S-doped porous carbon as oxygen reduction reaction catalyst in acidic medium with high activity and durability synthesized using CaCl2 as template

燃料电池是一种可将化学能通过电催化反应直接转化成电能的装置,具有能量密度高和清洁无污染等优点.燃料电池阴极氧还原反应(ORR)的动力学较迟缓,是; 电池能量效率损失的主要原因.目前ORR催化活性最高的是铂基催化剂,但由于贵金属铂价格昂贵,储量稀少,且对燃料小分子渗透的抗性较差,严重制约了燃料; 电池的大规模应用.因此,高性能、低成本的非贵金属催化剂成为燃料电池领域的研究热点.本文选用含氮量高达45%的三聚氰胺-甲醛树脂为碳源和氮源,Fe; (SCN)_3为铁源和硫源,以CaCl_2为模板,在高温和铁的催化作用下将树脂碳化,经酸洗和二次热处理工艺,制备出铁、氮、硫共掺杂的多孔碳(Fe; NS-PC).干燥后的CaCl_2颗粒可防止树脂在高温下交联形成块状碳颗粒,同时起到造孔模板的作用.CaCl_2颗粒在温和条件下即可除去,无需强; 腐蚀性条件,因此不会对催化活性中心造成破坏.在Fe/N/C催化剂中掺杂S可进一步提高催化活性,不添加碳载体可避免低活性的碳载体降低质量活性,多孔; 结构可促进传质,充分利用活性位点.我们优化了热处理温度,并对催化剂的结构、组分及催化性能等进行了表征分析.结果表明,热处理温度为900; °C时,可将树脂完全转化成多孔碳,并获得较高的杂原子掺杂量,可达到最优活性.CaCl_2为模板剂可避免使用强腐蚀性试剂去除模板,有利于保留活性位; ,并得到多孔结构.FeNS-PC-900的比表面积可达775; m~2/g.得益于原位掺杂的合成工艺,各掺杂元素在多孔碳表面均匀分布.在酸性介质中,FeNS-PC-900的半波电位可达到0.811; V,仅比商业Pt/C催化剂低78 mV;在0.8 V电位下的质量活性为10.2; A/g,表现出优异的催化活性.经过10000圈加速衰减测试后,其半波电位仅下降了20 mV,在0.75 V电位下持续放电10000; s后,其ORR电流仍保持初始电流的84.4%,具有比Pt/C更加优异的稳定性.以FeNS-PC-900为阴极催化剂的质子交换膜燃料电池的最大功率; 密度可达到0.49 W/cm~2,并在0.6 V电压下持续放电10; h后,其电流仍可保持初始电流的65%,表现出良好的应用潜力.FeNS-PC-900具有高掺杂含量、高比表面积和多孔结构,并且杂原子在催化剂表面均; 匀分散,在半电池和燃料电池测试中都表现出优异的催化活性和稳定性,表明其是一种非常有潜力应用于燃料电池的非贵金属氧还原催化剂.Proton exchange membrane fuel cells suffer from the sluggish kinetics of the oxygen reduction reaction (ORR) and the high cost of Pt catalysts. In the present work, a high-performance ORR catalyst based on Fe, N, S-doped porous carbon (FeNS-PC) was synthesized using melamine formaldehyde resin as C and N precursors, Fe(SCN)(3) as Fe and S precursors, and CaCl2 as a template via a two-step heat treatment without a harsh template removal step. The results show that the catalyst treated at 900 degrees C (FeNS-PC-900) had a high surface area of 775 m(2)/g, a high mass activity of 10.2 A/g in an acidic medium, and excellent durability; the half-wave potential decreased by only 20 mV after 10000 potential cycles. The FeNS-PC-900 catalyst was used as the cathode in a proton exchange membrane fuel cell and delivered a peak power density of 0.49 W/cm(2). FeNS-PC-900 therefore has good potential for use in practical applications. (C) 2017, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.National Basic Research Program of China [2015CB932303]; National; Natural Science Foundation of China [21373175, 21621091

Systematic Analysis of Non-coding RNAs Involved in the Angora Rabbit (Oryctolagus cuniculus) Hair Follicle Cycle by RNA Sequencing

The hair follicle (HF) cycle is a complicated and dynamic process in mammals, associated with various signaling pathways and gene expression patterns. Non-coding RNAs (ncRNAs) are RNA molecules that are not translated into proteins but are involved in the regulation of various cellular and biological processes. This study explored the relationship between ncRNAs and the HF cycle by developing a synchronization model in Angora rabbits. Transcriptome analysis was performed to investigate ncRNAs and mRNAs associated with the various stages of the HF cycle. One hundred and eleven long non-coding RNAs (lncRNAs), 247 circular RNAs (circRNAs), 97 microRNAs (miRNAs), and 1,168 mRNAs were differentially expressed during the three HF growth stages. Quantitative real-time PCR was used to validate the ncRNA transcriptome analysis results. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses provided information on the possible roles of ncRNAs and mRNAs during the HF cycle. In addition, lncRNA–miRNA–mRNA and circRNA–miRNA–mRNA ceRNA networks were constructed to investigate the underlying relationships between ncRNAs and mRNAs. LNC_002919 and novel_circ_0026326 were found to act as ceRNAs and participated in the regulation of the HF cycle as miR-320-3p sponges. This research comprehensively identified candidate regulatory ncRNAs during the HF cycle by transcriptome analysis, highlighting the possible association between ncRNAs and the regulation of hair growth. This study provides a basis for systematic further research and new insights on the regulation of the HF cycle

Impression Management, Forward-Looking Strategy-Related Disclosure, and Excess Executive Compensation: Evidence from China

We investigate whether overpaid executives in Chinese listed firms engage in impression management by using forward-looking strategy-related disclosure (FLSD) in management discussion and analysis (MD&A) narratives to justify their excess compensation. Using a sample of 8,437 firm-year observations of Chinese nonfinancial listed firms from 2007 to 2016, we find a significant and positive relationship between executive overpayment and impression management in FLSD. This positive relationship is more pronounced in state-owned enterprises (SOEs) than non-SOEs. We also find that a higher degree of board independence, higher institutional shareholdings, auditors, analysts, and the introduction of the anti-corruption campaign could lower such a positive relationship. These findings suggest that impression management in FLSD is reduced when corporate governance is strengthened. We also find that CEO duality could enhance this positive relationship. Further examining how the market reacts to such impression management, we find an immediate positive and significant market reaction to such impression management at the time of the annual report filing, which could further mitigate the negative perceptions from stakeholders due to excessive pay. Such a positive market reaction is reversed over a longer time horizon, which supports the opportunistic/symbolic nature of impression management in FLSD

Experimental Implementation of Noncyclic and Nonadiabatic Geometric Quantum Gates in a Superconducting Circuit

Quantum gates based on geometric phases possess intrinsic noise-resilience features and therefore attract much attention. However, the implementations of previous geometric quantum computation typically require a long pulse time of gates. As a result, their experimental control inevitably suffers from the cumulative disturbances of systematic errors due to excessive time consumption. Here, we experimentally implement a set of noncyclic and nonadiabatic geometric quantum gates in a superconducting circuit, which greatly shortens the gate time. And also, we experimentally verify that our universal single-qubit geometric gates are more robust to both the Rabi frequency error and qubit frequency shift-induced error, compared to the conventional dynamical gates, by using the randomized benchmarking method. Moreover, this scheme can be utilized to construct two-qubit geometric operations, while the generation of the maximally entangled Bell states is demonstrated. Therefore, our results provide a promising routine to achieve fast, high-fidelity, and error-resilient quantum gates in superconducting quantum circuits