One of Android's main defense mechanisms against malicious apps is a risk communication mechanism which, before a user installs an app, warns the user about the permissions the app requires, trusting that the user will make the right decision. This approach has been shown to be ineffective as it presents the risk information of each app in a "stand-alone" fashion and in a way that requires too much technical knowledge and time to distill useful information.We introduce the notion of risk scoring and risk ranking for Android apps, to improve risk communication for Android apps, and identify three desiderata for an effective risk scoring scheme. We propose to use probabilistic generative models for risk scoring schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Experimental results conducted using real-world datasets show that probabilistic general models significantly outperform existing approaches, and that Naive Bayes models give a promising risk scoring approach.

Online learning has become increasingly popular on handling massive data. The sequential nature of online learning, however, requires a centralized learner to store data and update parameters. In this paper, we consider online learning with distributed data sources. The autonomous learners update local parameters based on local data sources and periodically exchange information with a small subset of neighbors in a communication network. We derive the regret bound for strongly convex functions that generalizes the work by Ram et al.[1] for convex functions. More importantly, we show that our algorithm has intrinsic privacypreserving properties, and we prove the sufficient and necessary conditions for privacy preservation in the network. These conditions imply that for networks with greater-than-one connectivity, a malicious learner cannot reconstruct the subgradients (and sensitive raw data) of other learners, which makes our algorithm appealing in privacy-sensitive applications.

SummaryThe type-1 ryanodine receptor (RyR1) is an intracellular calcium (Ca 2+ ) release channel required for skeletal muscle contraction. Here we present cryo-EM reconstructions of RyR1 in multiple functional states revealing the structural basis of channel gating and ligand-dependent activation. Binding sites for the channel activators Ca 2+ , ATP and caffeine were identified at interdomain interfaces of the C-terminal domain. Either ATP or Ca 2+ alone induce conformational changes in the cytoplasmic assembly ('priming'), without pore dilation. In contrast, in the presence of all three activating ligands, high-resolution reconstructions of open and closed states of RyR1 were obtained from the same sample, enabling analyses of conformational changes associated with gating. Gating involves global conformational changes in the cytosolic assembly accompanied by local changes in the transmembrane domain, which include bending of the S6 transmembrane segment and consequent pore dilation, displacement and deformation of the S4-S5 linker, and conformational changes in the pseudo-voltage-sensor domain.

A minority of the 1% to 9% IHC ER-positive tumors show molecular features similar to those of ER-positive, potentially endocrine-sensitive tumors, whereas most show ER-negative, basal-like molecular characteristics. The safest clinical approach may be to use both adjuvant endocrine therapy and chemotherapy in this rare subset of patients.

No abstract

Different biological processes were associated with prognosis and chemotherapy response in ER-positive and ER-negative breast cancers.

Purpose We examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FAC×6 preoperative chemotherapy. We also performed an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms. Experimental Design 273 patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n=138), or FAC × 6 (n=135) neoadjuvant chemotherapy. All patients underwent a pretreatment FNA biopsy of the tumor for gene expression profiling and treatment response prediction. Results The pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (p<0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% (95%CI:21–56%), the negative predictive value (NPV) 88% (CI:77–95%) and the AUC 0.711. In the FAC arm, the PPV was 9% (CI:1–29%) and the AUC 0.584. This suggests that the genomic predictor may have regimen-specificity. Its performance was similar to a clinical variable-based predictor nomogram. Conclusions Gene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next generation predictive markers will need to be developed separately for different molecular subsets of breast cancers.

Among ER-negative and ER-positive highly proliferative cancers, a subset of tumors with high expression of a B-cell/plasma cell metagene carries a favorable prognosis.