Modelling the time-varying cell capacity in LTE networks

In wireless orthogonal frequency-division multiple access (OFDMA) based networks like Long Term Evolution (LTE) or Worldwide Interoperability for Microwave Access (WiMAX) a technique called adaptive modulation and coding (AMC) is applied. With AMC, different modulation and coding schemes (MCSs) are used to serve different users in order to maximise the throughput and range. The used MCS depends on the quality of the radio link between the base station and the user. Data is sent towards users with a good radio link with a high MCS in order to utilise the radio resources more efficiently while a low MCS is used for users with a bad radio link. Using AMC however has an impact on the cell capacity as the quality of a radio link varies when users move around; this can even lead to situations where the cell capacity drops to a point where there are too little radio resources to serve all users. AMC and the resulting varying cell capacity notably has an influence on admission control (AC). AC is the algorithm that decides whether new sessions are allowed to a cell or not and bases its decisions on, amongst others, the cell capacity. The analytical model that is developed in this paper models a cell with varying capacity caused by user mobility using a continuous -time Markov chain (CTMC). The cell is divided into multiple zones, each corresponding to the area in which data is sent towards users using a certain MCS and transitions of users between these zones are considered. The accuracy of the analytical model is verified by comparing the results obtained with it to results obtained from simulations that model the user mobility more realistically. This comparison shows that the analytical model models the varying cell capacity very accurately; only under extreme conditions differences between the results are noticed. The developed analytical and simulation models are then used to investigate the effects of a varying cell capacity on AC. Also, an optimisation algorithm that adapts the parameter of the AC algorithm which determines the amount of resources that are reserved in order to mitigate the effects of the varying cell capacity is studied using the models. Updating the parameter of the AC algorithm is done by reacting to certain triggers that indicate good or bad performance and adapt the parameters of the AC algorithm accordingly. Results show that using this optimisation algorithm improves the quality of service (QoS) that is experienced by the users.This work was partially supported by the Spanish Government through project TIN2010-21378-C02-02 and contract BES-2007-15030.Sas, B.; Bernal Mor, E.; Spaey, K.; Pla, V.; Blondia, C.; Martínez Bauset, J. (2014). Modelling the time-varying cell capacity in LTE networks. 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Cell Hierarchy and Lineage Commitment in the Bovine Mammary Gland

The bovine mammary gland is a favorable organ for studying mammary cell hierarchy due to its robust milk-production capabilities that reflect the adaptation of its cell populations to extensive expansion and differentiation. It also shares basic characteristics with the human breast, and identification of its cell composition may broaden our understanding of the diversity in cell hierarchy among mammals. Here, Lin− epithelial cells were sorted according to expression of CD24 and CD49f into four populations: CD24medCD49fpos (putative stem cells, puStm), CD24negCD49fpos (Basal), CD24highCD49fneg (putative progenitors, puPgt) and CD24medCD49fneg (luminal, Lum). These populations maintained differential gene expression of lineage markers and markers of stem cells and luminal progenitors. Of note was the high expression of Stat5a in the puPgt cells, and of Notch1, Delta1, Jagged1 and Hey1 in the puStm and Basal populations. Cultured puStm and Basal cells formed lineage-restricted basal or luminal clones and after re-sorting, colonies that preserved a duct-like alignment of epithelial layers. In contrast, puPgt and Lum cells generated only luminal clones and unorganized colonies. Under non-adherent culture conditions, the puPgt and puStm populations generated significantly more floating colonies. The increase in cell number during culture provides a measure of propagation potential, which was highest for the puStm cells. Taken together, these analyses position puStm cells at the top of the cell hierarchy and denote the presence of both bi-potent and luminally restricted progenitors. In addition, a population of differentiated luminal cells was marked. Finally, combining ALDH activity with cell-surface marker analyses defined a small subpopulation that is potentially stem cell- enriched

Pregnancy and Breast Cancer: when They Collide

Women of childbearing age experience an increased breast cancer risk associated with a completed pregnancy. For younger women, this increase in breast cancer risk is transient and within a decade after parturition a cross over effect results in an ultimate protective benefit. The post-partum peak of increased risk is greater in women with advanced maternal age. Further, their lifetime risk for developing breast cancer remains elevated for many years, with the cross over to protection occurring decades later or not at all. Breast cancers diagnosed during pregnancy and within a number of years post-partum are termed pregnancy-associated or PABC. Contrary to popular belief, PABC is not a rare disease and could affect up to 40,000 women in 2009. The collision between pregnancy and breast cancer puts women in a fear-invoking paradox of their own health, their pregnancy, and the outcomes for both. We propose two distinct subtypes of PABC: breast cancer diagnosed during pregnancy and breast cancer diagnosed post-partum. This distinction is important because emerging epidemiologic data highlights worsened outcomes specific to post-partum cases. We reported that post-partum breast involution may be responsible for the increased metastatic potential of post-partum PABC. Increased awareness and detection, rationally aggressive treatment, and enhanced understanding of the mechanisms are imperative steps toward improving the prognosis for PABC. If we determine the mechanisms by which involution promotes metastasis of PABC, the post-partum period can be a window of opportunity for intervention strategies