Behaviour of the germ cell specific lamin through mammalian spermatogenesis as probed with monoclonal antibodies.

We had earlier identified a 60 kDa nuclear lamin protein (lamin(g)) unique to the germ cells of rat testis which was subsequently shown to be antigenically conserved in germ cells of grasshopper, rooster, frog and plants. We have now obtained eight monoclonal antibodies in mouse against this lamin(g) antigen. While all the eight Mabs reacted with lamin(g) antigen in an immunoblot analysis, only three Mabs (A(11)C(7), A(11)D(4), C1F7) showed strong reactivity in the immunofluorescence analysis of the germ cells. The Mabs A(11)C(7) and A(11)D(4) showed a slight cross-reactivity with rat liver lamin B. Indirect immunofluorescence analysis of pre-meiotic, meiotic and post-meiotic germ cells with Mabs have shown that while the lamin(g) is localized in the lamina structures of spermatogonia and round spermatids, it is localized to the phase dense regions of pachytene spermatocytes which is in conformity with our previous observations using rabbit polyclonal antibodies. The localization of the antigen in the germ cells was also confirmed by immunohistochemical staining of the thin sections of seminiferous tubules. By immunostaining the surface spread pachytene spermatocytes, the antigen was further localized to the telomeric ends of the paired homologous chromosomes. Using anti-somatic lamin B antibodies, we have also demonstrated the absence of somatic lamins in meiotic and post-meiotic germ cells. The lamina structure of pre-meiotic spermatogonial nucleus contains both somatic lamin B and lamin(g) as evidenced by immunofluorescence studies with two differently fluorochrome labelled anti-lamin B and anti-lamin(g) antibodies. The selective retention of lamin(g) in the pachytene spermatocytes is probably essential for anchoring the telomeric ends of the paired chromosomes to the inner nuclear membrane.

Diversity in ranking via resistive graph centers

Users can rarely reveal their information need in full detail to a search engine within 1--2 words, so search engines need to "hedge their bets" and present diverse results within the precious 10 response slots. Diversity in ranking is of much recent interest. Most existing solutions estimate the marginal utility of an item given a set of items already in the response, and then use variants of greedy set cover. Others design graphs with the items as nodes and choose diverse items based on visit rates (PageRank). Here we introduce a radically new and natural formulation of diversity as finding centers in resistive graphs. Unlike in PageRank, we do not specify the edge resistances (equivalently, conductances) and ask for node visit rates. Instead, we look for a sparse set of center nodes so that the effective conductance from the center to the rest of the graph has maximum entropy. We give a cogent semantic justification for turning PageRank thus on its head. In marked deviation from prior work, our edge resistances are learnt from training data. Inference and learning are NP-hard, but we give practical solutions. In extensive experiments with subtopic retrieval, social network search, and document summarization, our approach convincingly surpasses recently-published diversity algorithms like subtopic cover, max-marginal relevance (MMR), Grasshopper, DivRank, and SVMdiv.