Palbociclib has no clinically relevant effect on the QTc interval in patients with advanced breast cancer.

The aim of this study was to assess the potential effects of palbociclib in combination with letrozole on QTc. PALOMA-2, a phase 3, randomized, double-blind, placebo-controlled trial, compared palbociclib plus letrozole with placebo plus letrozole in postmenopausal women with estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer. The study included a QTc evaluation substudy carried out as a definitive QT interval prolongation assessment for palbociclib. Time-matched triplicate ECGs were performed at 0, 2, 4, 6, and 8 h at baseline (Day 0) and on Cycle 1 Day 14. Additional ECGs were collected from all patients for safety monitoring. The QT interval was corrected for heart rate using Fridericia's correction (QTcF), Bazett's correction (QTcB), and a study-specific correction factor (QTcS). In total, 666 patients were randomized 2 : 1 to palbociclib plus letrozole or placebo plus letrozole. Of these, 125 patients were enrolled in the QTc evaluation substudy. No patients in the palbociclib plus letrozole arm of the substudy (N=77) had a maximum postbaseline QTcS or QTcF value of ≥ 480 ms, or a maximum increase from clock time-matched baseline for QTcS or QTcF values of ≥ 60 ms. The upper bounds of the one-sided 95% confidence interval for the mean change from time-matched baseline for QTcS, QTcF, and QTcB at all time points and at steady-state Cmax following repeated administration of 125 mg palbociclib were less than 10 ms. Palbociclib, when administered with letrozole at the recommended therapeutic dosing regimen, did not prolong the QT interval to a clinically relevant extent

Novel components of the Toxoplasma inner membrane complex revealed by BioID.

UNLABELLED:The inner membrane complex (IMC) of Toxoplasma gondii is a peripheral membrane system that is composed of flattened alveolar sacs that underlie the plasma membrane, coupled to a supporting cytoskeletal network. The IMC plays important roles in parasite replication, motility, and host cell invasion. Despite these central roles in the biology of the parasite, the proteins that constitute the IMC are largely unknown. In this study, we have adapted a technique named proximity-dependent biotin identification (BioID) for use in T. gondii to identify novel components of the IMC. Using IMC proteins in both the alveoli and the cytoskeletal network as bait, we have uncovered a total of 19 new IMC proteins in both of these suborganellar compartments, two of which we functionally evaluate by gene knockout. Importantly, labeling of IMC proteins using this approach has revealed a group of proteins that localize to the sutures of the alveolar sacs that have been seen in their entirety in Toxoplasma species only by freeze fracture electron microscopy. Collectively, our study greatly expands the repertoire of known proteins in the IMC and experimentally validates BioID as a strategy for discovering novel constituents of specific cellular compartments of T. gondii. IMPORTANCE:The identification of binding partners is critical for determining protein function within cellular compartments. However, discovery of protein-protein interactions within membrane or cytoskeletal compartments is challenging, particularly for transient or unstable interactions that are often disrupted by experimental manipulation of these compartments. To circumvent these problems, we adapted an in vivo biotinylation technique called BioID for Toxoplasma species to identify binding partners and proximal proteins within native cellular environments. We used BioID to identify 19 novel proteins in the parasite IMC, an organelle consisting of fused membrane sacs and an underlying cytoskeleton, whose protein composition is largely unknown. We also demonstrate the power of BioID for targeted discovery of proteins within specific compartments, such as the IMC cytoskeleton. In addition, we uncovered a new group of proteins localizing to the alveolar sutures of the IMC. BioID promises to reveal new insights on protein constituents and interactions within cellular compartments of Toxoplasma

Guided Neuronal Growth on Arrays of Biofunctionalized GaAs/InGaAs Semiconductor Microtubes

We demonstrate embedded growth of cortical mouse neurons in dense arrays of semiconductor microtubes. The microtubes, fabricated from a strained GaAs/InGaAs heterostructure, guide axon growth through them and enable electrical and optical probing of propagating action potentials. The coaxial nature of the microtubes -- similar to myelin -- is expected to enhance the signal transduction along the axon. We present a technique of suppressing arsenic toxicity and prove the success of this technique by overgrowing neuronal mouse cells.Comment: 3 pages, 4 figure

Evaluation of Cancer Stem Cell Migration Using Compartmentalizing Microfluidic Devices and Live Cell Imaging

In the last 40 years, the United States invested over 200 billion dollars on cancer research, resulting in only a 5% decrease in death rate. A major obstacle for improving patient outcomes is the poor understanding of mechanisms underlying cellular migration associated with aggressive cancer cell invasion, metastasis and therapeutic resistance1. Glioblastoma Multiforme (GBM), the most prevalent primary malignant adult brain tumor2, exemplifies this difficulty. Despite standard surgery, radiation and chemotherapies, patient median survival is only fifteen months, due to aggressive GBM infiltration into adjacent brain and rapid cancer recurrence2. The interactions of aberrant cell migratory mechanisms and the tumor microenvironment likely differentiate cancer from normal cells3. Therefore, improving therapeutic approaches for GBM require a better understanding of cancer cell migration mechanisms. Recent work suggests that a small subpopulation of cells within GBM, the brain tumor stem cell (BTSC), may be responsible for therapeutic resistance and recurrence. Mechanisms underlying BTSC migratory capacity are only starting to be characterized1,4

Thermodynamics of a trapped interacting Bose gas and the renormalization group

We apply perturbative renormalization group theory to the symmetric phase of a dilute interacting Bose gas which is trapped in a three-dimensional harmonic potential. Using Wilsonian energy-shell renormalization and the epsilon-expansion, we derive the flow equations for the system. We relate these equations to the flow for the homogeneous Bose gas. In the thermodynamic limit, we apply our results to study the transition temperature as a function of the scattering length. Our results compare well to previous studies of the problem.Comment: 14 pages, 5 figure

Finite-size scaling in thin Fe/Ir(100) layers

The critical temperature of thin Fe layers on Ir(100) is measured through M\"o{\ss}bauer spectroscopy as a function of the layer thickness. From a phenomenological finite-size scaling analysis, we find an effective shift exponent lambda = 3.15 +/- 0.15, which is twice as large as the value expected from the conventional finite-size scaling prediction lambda=1/nu, where nu is the correlation length critical exponent. Taking corrections to finite-size scaling into account, we derive the effective shift exponent lambda=(1+2\Delta_1)/nu, where Delta_1 describes the leading corrections to scaling. For the 3D Heisenberg universality class, this leads to lambda = 3.0 +/- 0.1, in agreement with the experimental data. Earlier data by Ambrose and Chien on the effective shift exponent in CoO films are also explained.Comment: Latex, 4 pages, with 2 figures, to appear in Phys. Rev. Lett

Energy representation for out-of-equilibrium Brownian-like systems: steady states and fluctuation relations

Stochastic dynamics in the energy representation is employed as a method to study non-equilibrium Brownian-like systems. It is shown that the equation of motion for the energy of such systems can be taken in the form of the Langevin equation with multiplicative noise. Properties of the steady states are examined by solving the Fokker-Planck equation for the energy distribution functions. The generalized integral fluctuation theorem is deduced for the systems characterized by the shifted probability flux operator. There are a number of entropy and fluctuation relations such as the Hatano-Sasa identity and the Jarzynski's equality that follow from this theorem.Comment: revtex4-1, 18 pages, extended discussion, references adde

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These 'somatic eQTLs' (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of 13 electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out-of-sample errors as a function of training set size with up to ∼118k distinct molecules. Molecular structures and properties at the hybrid density functional theory (DFT) level of theory come from the QM9 database [Ramakrishnan et al. Sci. Data 2014, 1, 140022] and include enthalpies and free energies of atomization, HOMO/LUMO energies and gap, dipole moment, polarizability, zero point vibrational energy, heat capacity, and the highest fundamental vibrational frequency. Various molecular representations have been studied (Coulomb matrix, bag of bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed distribution based variants including histograms of distances (HD), angles (HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR), and two types of neural networks, graph convolutions (GC) and gated graph networks (GG). Out-of sample errors are strongly dependent on the choice of representation and regressor and molecular property. Electronic properties are typically best accounted for by MG and GC, while energetic properties are better described by HDAD and KRR. The specific combinations with the lowest out-of-sample errors in the ∼118k training set size limit are (free) energies and enthalpies of atomization (HDAD/KRR), HOMO/LUMO eigenvalue and gap (MG/GC), dipole moment (MG/GC), static polarizability (MG/GG), zero point vibrational energy (HDAD/KRR), heat capacity at room temperature (HDAD/KRR), and highest fundamental vibrational frequency (BAML/RF). We present numerical evidence that ML model predictions deviate from DFT (B3LYP) less than DFT (B3LYP) deviates from experiment for all properties. Furthermore, out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy. The results suggest that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data were available

A Coastal Cline in Sodium Accumulation in Arabidopsis thaliana Is Driven by Natural Variation of the Sodium Transporter AtHKT1;1

The genetic model plant Arabidopsis thaliana, like many plant species, experiences a range of edaphic conditions across its natural habitat. Such heterogeneity may drive local adaptation, though the molecular genetic basis remains elusive. Here, we describe a study in which we used genome-wide association mapping, genetic complementation, and gene expression studies to identify cis-regulatory expression level polymorphisms at the AtHKT1;1 locus, encoding a known sodium (Na+) transporter, as being a major factor controlling natural variation in leaf Na+ accumulation capacity across the global A. thaliana population. A weak allele of AtHKT1;1 that drives elevated leaf Na+ in this population has been previously linked to elevated salinity tolerance. Inspection of the geographical distribution of this allele revealed its significant enrichment in populations associated with the coast and saline soils in Europe. The fixation of this weak AtHKT1;1 allele in these populations is genetic evidence supporting local adaptation to these potentially saline impacted environments