Organic Alternatives to Quantum Dots for Intraoperative Near-Infrared Fluorescent Sentinel Lymph Node Mapping:

Intraoperative near-infrared (NIR) fluorescence imaging provides the surgeon with real-time image guidance during cancer and other surgeries. We have previously reported the use of NIR fluorescent quantum dots (QDs) for sentinel lymph node (SLN) mapping. However, because of concerns over potential toxicity, organic alternatives to QDs will be required for initial clinical studies. We describe a family of 800 nm organic heptamethine indocyanine-based contrast agents for SLN mapping spanning a spectrum from 775 Da small molecules to 7 MDa nanocolloids. We provide a detailed characterization of the optical and physical properties of these contrast agents and discuss the advantages and disadvantages of each. We present robust methods for the covalent conjugation, purification, and characterization of proteins with tetra-sulfonated heptamethine indocyanines, including mass spectroscopic site mapping of highly substituted molecules. One contrast agent, NIR fluorescent human serum albumin (HSA800), emerged as the molecule with the best overall performance with respect to entry to lymphatics, flow to the SLN, retention in the SLN, fluorescence yield and reproducibility. This preclinical study, performed on large animals approaching the size of humans, should serve as a foundation for future clinical studies. Mol Imaging (2005) 4, 172 – 181

Pollution, Public Health Care, and Life Expectancy When Inequality Matters

We analyze the link between economic inequality in terms of wealth, life expectancy, health care and pollution. The distribution of wealth is decisive for the number of households investing in human capital. Moreover, the willingness to invest in human capital depends on agents' life expectancy which determines the length of the amortization period of human capital investments. Life expectancy is positively affected by public health care expenditures but adversely affected by the pollution stock generated by aggregate production. Our model accounts for an endogenous take-off in terms of human capital investments. Higher initial inequality delays the take-off because a given set of policies (abatement measures and public health care) is less effective in improving agents' survival probabilities. We compare a change in taxes to a change in expenditure shares on health hand abatement given different amounts of (initial) inequality. The advantage of the latter as compared to the former is the achieved increase in the tax base which induces more expenditures on health care and abatement measures, such that an even higher economic activity is compatible with a similar level of long-run pollution

Human Glial Progenitor Cells Effectively Remyelinate the Demyelinated Adult Brain

Neonatally transplanted human glial progenitor cells (hGPCs) can myelinate the brains of myelin-deficient shiverer mice, rescuing their phenotype and survival. Yet, it has been unclear whether implanted hGPCs are similarly able to remyelinate the diffusely demyelinated adult CNS. We, therefore, ask if hGPCs could remyelinate both congenitally hypomyelinated adult shiverers and normal adult mice after cuprizone demyelination. In adult shiverers, hGPCs broadly disperse and differentiate as myelinating oligodendrocytes after subcortical injection, improving both host callosal conduction and ambulation. Implanted hGPCs similarly remyelinate denuded axons after cuprizone demyelination, whether delivered before or after demyelination. RNA sequencing (RNA-seq) of hGPCs back from cuprizone-demyelinated brains reveals their transcriptional activation of oligodendrocyte differentiation programs, while distinguishing them from hGPCs not previously exposed to demyelination. These data indicate the ability of transplanted hGPCs to disperse throughout the adult CNS, to broadly myelinate regions of dysmyelination, and also to be recruited as myelinogenic oligodendrocytes later in life, upon demyelination-associated demand

A MOOC in Nuclear- and Radio-Chemistry: from the design to the feedback

Recently, the loss of expertise in the nuclear- and radio-chemistry (NRC) is becoming an issue of concern, because of few engaging curricula and career prospects. To counteract this trend, the Massive Open Online Course “Essential Radiochemistry for Society” has been developed with the intent of letting young students in scientific matters discover all the benefits of NRC to society and improving their awareness of these disciplines. The MOOC development process as well as the feedbacks collected in the first MOOC editions are analyzed by highlighting strong points and weakness of the followed strategy

Frontal Polymerizations: From Chemical Perspectives to Macroscopic Properties and Applications

The synthesis and processing of most thermoplastics and thermoset polymeric materials rely on energy-inefficient and environmentally burdensome manufacturing methods. Frontal polymerization is an attractive, scalable alternative due to its exploitation of polymerization heat that is generally wasted and unutilized. The only external energy needed for frontal polymerization is an initial thermal (or photo) stimulus that locally ignites the reaction. The subsequent reaction exothermicity provides local heating; the transport of this thermal energy to neighboring monomers in either a liquid or gel-like state results in a self-perpetuating reaction zone that provides fully cured thermosets and thermoplastics. Propagation of this polymerization front continues through the unreacted monomer media until either all reactants are consumed or sufficient heat loss stalls further reaction. Several different polymerization mechanisms support frontal processes, including free-radical, cat- or anionic, amine-cure epoxides, and ring-opening metathesis polymerization. The choice of monomer, initiator/catalyst, and additives dictates how fast the polymer front traverses the reactant medium, as well as the maximum temperature achievable. Numerous applications of frontally generated materials exist, ranging from porous substrate reinforcement to fabrication of patterned composites. In this review, we examine in detail the physical and chemical phenomena that govern frontal polymerization, as well as outline the existing applications

Identification of the skeletal progenitor cells forming osteophytes in osteoarthritis.

OBJECTIVES: Osteophytes are highly prevalent in osteoarthritis (OA) and are associated with pain and functional disability. These pathological outgrowths of cartilage and bone typically form at the junction of articular cartilage, periosteum and synovium. The aim of this study was to identify the cells forming osteophytes in OA. METHODS: Fluorescent genetic cell-labelling and tracing mouse models were induced with tamoxifen to switch on reporter expression, as appropriate, followed by surgery to induce destabilisation of the medial meniscus. Contributions of fluorescently labelled cells to osteophytes after 2 or 8 weeks, and their molecular identity, were analysed by histology, immunofluorescence staining and RNA in situ hybridisation. Pdgfrα-H2BGFP mice and Pdgfrα-CreER mice crossed with multicolour Confetti reporter mice were used for identification and clonal tracing of mesenchymal progenitors. Mice carrying Col2-CreER, Nes-CreER, LepR-Cre, Grem1-CreER, Gdf5-Cre, Sox9-CreER or Prg4-CreER were crossed with tdTomato reporter mice to lineage-trace chondrocytes and stem/progenitor cell subpopulations. RESULTS: Articular chondrocytes, or skeletal stem cells identified by Nes, LepR or Grem1 expression, did not give rise to osteophytes. Instead, osteophytes derived from Pdgfrα-expressing stem/progenitor cells in periosteum and synovium that are descendants from the Gdf5-expressing embryonic joint interzone. Further, we show that Sox9-expressing progenitors in periosteum supplied hybrid skeletal cells to the early osteophyte, while Prg4-expressing progenitors from synovial lining contributed to cartilage capping the osteophyte, but not to bone. CONCLUSION: Our findings reveal distinct periosteal and synovial skeletal progenitors that cooperate to form osteophytes in OA. These cell populations could be targeted in disease modification for treatment of OA

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering

We discuss a technique for measuring a charged particle's momentum by means of multiple Coulomb scattering (MCS) in the MicroBooNE liquid argon time projection chamber (LArTPC). This method does not require the full particle ionization track to be contained inside of the detector volume as other track momentum reconstruction methods do (range-based momentum reconstruction and calorimetric momentum reconstruction). We motivate use of this technique, describe a tuning of the underlying phenomenological formula, quantify its performance on fully contained beam-neutrino-induced muon tracks both in simulation and in data, and quantify its performance on exiting muon tracks in simulation. Using simulation, we have shown that the standard Highland formula should be re-tuned specifically for scattering in liquid argon, which significantly improves the bias and resolution of the momentum measurement. With the tuned formula, we find agreement between data and simulation for contained tracks, with a small bias in the momentum reconstruction and with resolutions that vary as a function of track length, improving from about 10% for the shortest (one meter long) tracks to 5% for longer (several meter) tracks. For simulated exiting muons with at least one meter of track contained, we find a similarly small bias, and a resolution which is less than 15% for muons with momentum below 2 GeV/c. Above 2 GeV/c, results are given as a first estimate of the MCS momentum measurement capabilities of MicroBooNE for high momentum exiting tracks

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal