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Polymers are endocytosed and hydrolysed by lysosomal enzymes to generate transportable solutes. While the transport of diverse organic solutes across the plasma membrane is well studied, their necessary ongoing efflux from the endocytic fluid into the cytosol is poorly appreciated by comparison. Myeloid cells that employ specialized types of endocytosis, that is, phagocytosis and macropinocytosis, are highly dependent on such transport pathways to prevent the build-up of hydrostatic pressure that otherwise offsets lysosomal dynamics including vesiculation, tubulation and fission. Without undergoing rupture, we found that lysosomes incurring this pressure owing to defects in solute efflux, are unable to retain luminal Na+, which collapses its gradient with the cytosol. This cation ‘leak’ is mediated by pressure-sensitive channels resident to lysosomes and leads to the inhibition of mTORC1, which is normally activated by Na+-coupled amino acid transporters driven by the Na+gradient. As a consequence, the transcription factors TFEB/TFE3 are made active in macrophages with distended lysosomes. In addition to their role in lysosomal biogenesis, TFEB/TFE3 activation causes the release of MCP-1/CCL2. In catabolically stressed tissues, defects in efflux of solutes from the endocytic pathway leads to increased monocyte recruitment. Here we propose that macrophages respond to a pressure-sensing pathway on lysosomes to orchestrate lysosomal biogenesis as well as myeloid cell recruitment.

We find sufficient conditions for a self-map of the unit ball to converge uniformly under iteration to a fixed point or idempotent on the entire ball. Using these tools, we establish spectral containments for weighted composition operators on Hardy and Bergman spaces of the ball. When the compositional symbol is in the Schur–Agler class, we establish the spectral radii of these weighted composition operators.

We propose an algorithm for decentralized optimization in wireless device-to-device (D2D) networks of pervasive devices such as sensors or 5G handsets, in which the signs of stochastic gradient are used for descent steps. Our algorithm has the convergence rate of ${O}$ (1/(nT)) in which ${n}$ is the number of devices and ${T}$ is the number of learning iterations, saving the communication efficiency by at least 64 times when compared with previous results, and being relatively robust to unexpected errors of adversarial scaling in communication. Theoretical claims are verified by numerical results on a standard benchmark dataset.

We study the structure of Np $\mathcal N_{p}$-spaces in the ball. In particular, we show that any such space is Moebius-invariant and for 0 < p≤ n, all Np $\mathcal N_{p}$-spaces are different. Our results will be used in the study of operator theory on Np $\mathcal N_{p}$-spaces.

Many identity-based encryption schemes under the k-LIN assumption contain 2k+ 1 group elements in the ciphertext overhead and private keys. In this paper, we push the limit further by constructing an IBE scheme under the k-LIN assumption with 2kgroup elements in the ciphertext overhead and private keys. The schemes have variants with shorter public parameters under the k-SCasc assumption, which is a close assumption to k-LIN. Furthermore, via additional refinements, we also put efforts in reducing the public parameter size of our schemes, under either k-LIN or k-SCasc. While we mainly consider securities in the standard model for our schemes, we also show how to make relatively more efficient schemes secure in the random oracle model. Our technique additionally expands to the scheme of Boneh et al. (CRYPTO 2013) to yield more efficient function-private IBE under the 2-LIN (aka, DLIN) assumption. Overall, the shortened size in ciphertexts and private keys inherently leads to fewer exponentiations and pairings in encryption and decryption, and hence yields schemes with better computational efficiency.

We obtain the estimate for the essential norms of weighted composition operators acting from 𝓝p $\mathcal {N}_{p}$-spaces into the Beurling/Bergman-type spaces A−qof holomorphic functions in the unit ball. Our results contain the results in the unit disk as particular cases.

Although skyline queries are very useful in such areas such as decision support, market analysis and personalized services, they have not been extensively studied in the context of uncertain data. The existing work on answering probabilistic skyline queries either requires a user to define a threshold (Pei et al., 2007), or return all probabilistic skyline objects (Atallah and Qi, 2009). However, it is difficult to set the threshold because if set too high, important results may be lost, but if set too low or if there is no threshold, a lot of low quality results may be returned (Hua et al., 2011; Le and Cao, 2012; Le et al., 2013) [17]. In this paper, we identify two main challenges in answering probabilistic skyline queries. The first is defining what are the interesting probabilistic skyline tuples to return to the users. The second is efficiently finding these tuples without enumerating all possible worlds. We overcome the first challenge by introducing the bestpro-skyline query, which extends the dominance principle to also include the skyline probability of the probabilistic skyline tuples. This approach results in pruning the result set to just a very small number of the most interesting probabilistic skyline tuples without the need to set any user-defined threshold. We overcome the second challenge by using formulas based on the probabilistic theory to directly calculate the skyline probabilities without considering any possible worlds and develop algorithms to prune the search space. Experiments show that our solution is able to find the 17 interesting probabilistic skyline tuples from 13,095 tuples within 19 s in a real data set. Our solution outperforms a Naïve solution by up to three orders of magnitude for computational time. [ABSTRACT FROM AUTHOR]