Perfluorochemical (PFC) liquid enhances recombinant adenovirus vector-mediated viral interleukin-10 (AdvIL-10) expression in rodent lung

Adenovirus and cationic liposome mediated transfer of Interleukin-10 (IL-10), a potent anti-inflammatory cytokine, has been shown to decrease pro-inflammatory cytokine levels and overall lung inflammation in models of lung transplantation and injury. Limitations to current approaches of IL-10 gene therapy include poor vector delivery methods and pro-inflammatory properties of human IL-10 under certain conditions. We hypothesize that using perfluorochemical (PFC) liquid to deliver the highly homologous viral IL-10 (vIL-10), which is predominantly anti-inflammatory with minimal pro-inflammatory activities, can potentially be a more effective strategy to combat inflammatory lung diseases. In this study, we compare the use of PFC liquid versus aerosolized method to deliver adenovirus encoding the vIL-10 gene (AdvIL-10) in C57Bl6 mice. Detectable vIL-10 levels were measured from bronchoalveolar lavage fluid and lung homogenates at one, four, ten and thirty days after AdvIL-10. Furthermore, we determined if use of PFC liquid could allow for the use of a lower dose of AdvIL-10 by comparing the levels of detectable vIL-10 at different doses of AdvIL-10 delivered +/- PFC liquid. Results showed that PFC liquid enhanced detectable vIL-10 by up to ten fold and that PFC liquid allowed the use of ten-fold less vector. PFC liquid increased detectable vIL-10 in lung homogenates at all time points; however, the increase in detectable vIL-10 in BAL fluid peaked at four days and was no longer evident by thirty days after intratracheal instillation. In summary, this is the first report utilizing PFC liquid to enhance the delivery of a potentially therapeutic molecule, vIL-10. We believe this strategy can be used to perform future studies on the use of the predominantly anti-inflammatory vIL-10 to treat inflammatory lung diseases

Multidimensional quantum entanglement with large-scale integrated optics

The ability to control multidimensional quantum systems is key for the investigation of fundamental science and for the development of advanced quantum technologies. Here we demonstrate a multidimensional integrated quantum photonic platform able to robustly generate, control and analyze high-dimensional entanglement. We realize a programmable bipartite entangled system with dimension up to $15 \times 15$ on a large-scale silicon-photonics quantum circuit. The device integrates more than 550 photonic components on a single chip, including 16 identical photon-pair sources. We verify the high precision, generality and controllability of our multidimensional technology, and further exploit these abilities to demonstrate key quantum applications experimentally unexplored before, such as quantum randomness expansion and self-testing on multidimensional states. Our work provides a prominent experimental platform for the development of multidimensional quantum technologies.Comment: Science, (2018

The Rule of Law is Dead! Long Live the Rule of Law!

Polls show that a significant proportion of the public considers judges to be political. This result holds whether Americans are asked about Supreme Court justices, federal judges, state judges, or judges in general. At the same time, a large majority of the public also believes that judges are fair and impartial arbiters, and this belief also applies across the board. In this paper, I consider what this half-law-half-politics understanding of the courts means for judicial legitimacy and the public confidence on which that legitimacy rests. Drawing on the Legal Realists, and particularly on the work of Thurman Arnold, I argue against the notion that the contradictory views must be resolved in order for judicial legitimacy to remain intact. A rule of law built on contending legal and political beliefs is not necessarily fair or just. But it can be stable. At least in the context of law and courts, a house divided may stand

DISC1 regulates N-methyl-D-aspartate receptor dynamics:abnormalities induced by a Disc1 mutation modelling a translocation linked to major mental illness

Abstract The neuromodulatory gene DISC1 is disrupted by a t(1;11) translocation that is highly penetrant for schizophrenia and affective disorders, but how this translocation affects DISC1 function is incompletely understood. N-methyl-D-aspartate receptors (NMDAR) play a central role in synaptic plasticity and cognition, and are implicated in the pathophysiology of schizophrenia through genetic and functional studies. We show that the NMDAR subunit GluN2B complexes with DISC1-associated trafficking factor TRAK1, while DISC1 interacts with the GluN1 subunit and regulates dendritic NMDAR motility in cultured mouse neurons. Moreover, in the first mutant mouse that models DISC1 disruption by the translocation, the pool of NMDAR transport vesicles and surface/synaptic NMDAR expression are increased. Since NMDAR cell surface/synaptic expression is tightly regulated to ensure correct function, these changes in the mutant mouse are likely to affect NMDAR signalling and synaptic plasticity. Consistent with these observations, RNASeq analysis of the translocation carrier-derived human neurons indicates abnormalities of excitatory synapses and vesicle dynamics. RNASeq analysis of the human neurons also identifies many differentially expressed genes previously highlighted as putative schizophrenia and/or depression risk factors through large-scale genome-wide association and copy number variant studies, indicating that the translocation triggers common disease pathways that are shared with unrelated psychiatric patients. Altogether, our findings suggest that translocation-induced disease mechanisms are likely to be relevant to mental illness in general, and that such disease mechanisms include altered NMDAR dynamics and excitatory synapse function. This could contribute to the cognitive disorders displayed by translocation carriers

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Combined fit to the spectrum and composition data measured by the Pierre Auger Observatory including magnetic horizon effects

The measurements by the Pierre Auger Observatory of the energy spectrum and mass composition of cosmic rays can be interpreted assuming the presence of two extragalactic source populations, one dominating the flux at energies above a few EeV and the other below. To fit the data ignoring magnetic field effects, the high-energy population needs to accelerate a mixture of nuclei with very hard spectra, at odds with the approximate E$^{-2}$ shape expected from diffusive shock acceleration. The presence of turbulent extragalactic magnetic fields in the region between the closest sources and the Earth can significantly modify the observed CR spectrum with respect to that emitted by the sources, reducing the flux of low-rigidity particles that reach the Earth. We here take into account this magnetic horizon effect in the combined fit of the spectrum and shower depth distributions, exploring the possibility that a spectrum for the high-energy population sources with a shape closer to E$^{-2}$ be able to explain the observations

A search for ultra-high-energy photons at the Pierre Auger Observatory exploiting air-shower universality

The Pierre Auger Observatory is the most sensitive detector to primary photons with energies above ∼0.2 EeV. It measures extensive air showers using a hybrid technique that combines a fluorescence detector (FD) with a ground array of particle detectors (SD). The signatures of a photon-induced air shower are a larger atmospheric depth at the shower maximum (X$_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced background. Using observables measured by the FD and SD, three photon searches in different energy bands are performed. In particular, between threshold energies of 1-10 EeV, a new analysis technique has been developed by combining the FD-based measurement of X$_{max}$ with the SD signal through a parameter related to its muon content, derived from the universality of the air showers. This technique has led to a better photon/hadron separation and, consequently, to a higher search sensitivity, resulting in a tighter upper limit than before. The outcome of this new analysis is presented here, along with previous results in the energy ranges below 1 EeV and above 10 EeV. From the data collected by the Pierre Auger Observatory in about 15 years of operation, the most stringent constraints on the fraction of photons in the cosmic flux are set over almost three decades in energy