Heat stress (HS) results in health problems in animals. This study was conducted to investigate the effect and the underlying mechanism of HS on the proliferation and differentiation process of 3T3-L1 preadipocytes. 3T3-L1 preadipocytes were treated at 37 °C or 41.5 °C. HS up-regulated the mRNA and protein expression level of heat shock protein 70 (HSP70). Furthermore, the proliferation of 3T3-L1 preadipocytes were significantly inhibited after HS treatment for 2 days. A large number of accumulated lipid droplets were observed under the microscope after HS treatment for 8 days. Notably, the result of oil red O staining showed that the number of lipid droplets increased significantly and the differentiation ability of the cells was enhanced after HS. Moreover, after 2 and 8 d of differentiation, HS increased the transcription levels of fat synthesis genes including peroxisome proliferators activated receptor γ (PPARγ), fatty acid binding protein 2 (AP2), fatty acid synthase (FAS) and CCAAT enhancer binding protein α (CEBPα) genes, while decreasing the transcription levels of lipid decomposition genes including ATGL and HSL genes. In addition, HS reduced the expression of AMPK and PGC-1α, as well as the dephosphorylation of AMPK. 5-Aminoimidazole-4-carboxamide ribonucleotide (AICAR) can eliminate HS induced lipogenesis by activating AMPK. These results indicated that HS inhibited the proliferation of 3T3-L1 preadipocytes and promoted lipid accumulation by inhibiting the AMPK-PGC-1α signaling pathway in 3T3-L1 preadipocytes. This work lays a theoretical foundation for improving the effect of HS on meat quality of livestock and provides a new direction for the prevention of obesity caused by HS.

Graph sparsification aims at compressing large graphs into smaller ones while preserving important characteristics of the input graph. In this work we study vertex sparsifiers, i.e., sparsifiers wh...

We study the problem of recognizing the cluster structure of a graph in the framework of property testing in the bounded degree model. Given a parameter ε, a d-bounded degree graph is defined to be (k, φ)-clusterable, if it can be partitioned into no more than k parts, such that the (inner) conductance of the induced subgraph on each part is at least φ and the (outer) conductance of each part is at most c_d,kε⁴φ², where c_d,k depends only on d,k. Our main result is a sublinear algorithm with the running time ~O(√n ⋅ poly(φ,k,1/ε)) that takes as input a graph with maximum degree bounded by d, parameters k, φ, ε, and with probability at least 2/3, accepts the graph if it is (k,φ)-clusterable and rejects the graph if it is ε-far from (k, φ*)-clusterable for φ* = c'_d,kφ² ε⁴}/log n, where c'_d,k depends only on d,k. By the lower bound of Ω(√n) on the number of queries needed for testing graph expansion, which corresponds to k=1 in our problem, our algorithm is asymptotically optimal up to polylogarithmic factors.

We investigate several geometric models of networks that simultaneously have some nice global properties, including the small-diameter property, the small-community phenomenon, which is defined to capture the common experience that (almost) everyone in society also belongs to some meaningful small communities, and the power law degree distribution, for which our result significantly strengthens those given in van den Esker (2008) and Jordan (2010). These results, together with our previous work in Li and Peng (2011), build a mathematical foundation for the study of both communities and the small-community phenomenon in various networks. In the proof of the power law degree distribution, we develop the method of alternating concentration analysis to build a concentration inequality by alternately and iteratively applying both the sub-and super-martingale inequalities, which seems to be a powerful technique with further potential applications.

By Pan Peng, Angsheng Li. Communities (or clusters) are ubiquitous in real-world networks. Researchers from different fields have proposed many...