Search

The coming decade promises exceptional experimental progress in searches for charged lepton flavor-violating (CLFV) $\mu\rightarrow e$ conversion due to efforts at Fermilab (Mu2e) and J-PARC (COMET). Branching ratio sensitivities for this process are expected to advance by four orders of magnitude, potentially extending the reach of these probes up to energies of $10^4$ TeV. A pressing challenge for theorists is to extract the maximum amount of information about possible sources of CLFV from these measurements, whether or not a signal of new physics is detected. Efforts to observe $\mu\rightarrow e$ conversion share many similarities with other experimental programs where the nucleus is treated as a laboratory in which to search for beyond-standard-model (BSM) physics. These approaches are utilized because they present certain practical advantages: In searches for CLFV, the act of trapping a muon into the Coulomb field of a nucleus allows one to control the energy of the final state electron, ensuring that it is maximal compared to the energy of background electrons originating in standard-model free muon decays. The downside of employing the nucleus as part of the apparatus is that a host of complex nuclear physics consequently intervenes between the experimentalist and the desired observable. To extract meaningful constraints, one must have a method for disentangling the nuclear physics from the underlying BSM physics.Another experimental setting in which the nucleus is treated as a laboratory is in direct detection searches for weakly-interacting massive particle (WIMP) dark matter, where one aims to discern the mass, spin, and fundamental interactions of WIMP dark matter through scattering off of atomic nuclei. Again, to access the sought-after information about BSM physics, one must be able to separate it cleanly from the nuclear physics. In the case of dark matter direct detection, this separation has been achieved through the development of an effective theory formulated at the nuclear scale, which factorizes the nuclear physics from the BSM dark matter physics, sequestering the latter quantity into unknown low-energy constants (LECs) that are probed directly by experiment. As the effective theory describes the most general coupling between the WIMPs and the nucleus, the LECs that specify the effective theory represent the maximum information about the nature of dark matter that can be obtained from scattering off of nuclei. In this thesis, we introduce an analogous effective theory for the problem of $\mu\rightarrow e$ conversion. In order to adapt the existing framework to the problem at hand, several significant modifications are required, primarily stemming from the differing nature of the particles that couple to the nucleus in each scenario: non-relativistic plane-wave dark matter must be replaced by a bound muon in the initial state and an ultra-relativistic electron in the final state. We focus primarily on the case of elastic $\mu\rightarrow e$ conversion, wherein the nucleus remains in its ground state (as this ensures that the energy of the outgoing electron is maximal). The three-momentum transferred from the leptons to the nucleus $q\approx m_{\mu}$ is comparable to the inverse nuclear size, allowing significant angular momentum to be transferred between the leptons and the nucleus. As a result, the nuclear multipole expansion cannot be truncated at any order. This decomposition is complicated by the fact that the outgoing electron interacts with the nuclear charge through the Coulomb potential. Nonetheless, the nuclear multipole expansion can be performed in a straightforward manner by replacing the Coulomb-distorted electron wave function with a plane-wave form parameterized by a suitable local momentum.The effective theory is then specified by a controlled expansion in terms of the relevant velocity operators for the nucleons $\vec{v}_N$ and the bound muon $\vec{v}_{\mu}$. (The electron velocity is, in essence, ``integrated out'' of the theory by the assumption that it is ultra-relativistic.) The construction of the nucleon-scale effective theory proceeds in two steps: First, we specify a complete set (through a given order in power-counting) of CLFV operators that couple the leptons to single-nucleon charges and currents. Next, after performing the nuclear multipole decomposition, the resulting nucleon-level theory is embedded into the target nucleus, where the approximate parity and time-reversal symmetries of the nuclear ground state restrict the operators that can contribute to elastic $\mu\rightarrow e$ conversion. A valid effective theory can be constructed at three distinct degrees of complexity: The most basic theory is generated by including neither $\vec{v}_N$ nor $\vec{v}_{\mu}$. Relativistic corrections to the muon wave function and effects stemming from nuclear compositeness are completed ignored, and the CLFV amplitude depends on just three nuclear response functions, those of a point-like nucleus. Next, we extend the theory by considering $\vec{v}_N$ to first order, and consequently the set of nuclear responses is enlarged by the addition of three velocity-dependent response functions. Finally, we formulate the most complete effective theory, including both velocity operators, $\vec{v}_N$ and $\vec{v}_{\mu}$, to first order. This corresponds to the inclusion of relativistic muon effects, in the form of the muon's lower Dirac component, and introduces six additional nuclear responses. The muon's lower component always appears as a correction to the upper-component contribution, and therefore we consider the second of these constructions---containing $\vec{v}_N$ but not $\vec{v}_\mu$---to be the prototypical effective theory, complete through leading order in the nuclear response. The various nuclear responses can be understood as the ``nuclear dials'' that an experimentalist can tune through nuclear target selection in order to access different regions of CLFV parameter space. The nucleus $^{27}$Al, the target of the Mu2e and COMET experiments, has ground-state angular momentum $J=5/2$ and provides good sensitivity across a range of responses that are spin- and velocity-dependent/independent. On the other hand, a target such as Ca, whose natural abundance consists (almost) entirely of isotopes with ground-state angular momentum $J=0$, will not couple to non-scalar operators. A detailed understanding of the interplay between the various nuclear responses is prerequisite to carrying out an experimental program---across a multitude of targets---in order to fully constrain the unknown CLFV parameters of the nuclear-scale effective theory.Much of the previous literature has focused on a narrow special case in which the leading operator that mediates $\mu\rightarrow e$ conversion couples equally to protons and neutrons and is spin- and velocity-independent. Such an operator sums coherently in the conversion amplitude and receives an enhancement by the atomic mass number $A$ relative to incoherent operators, thereby dominating the CLFV response in cases where it is present. The primary advantage of working in this limited case is that the nuclear physics, which is a source of significant complication in general, becomes exceedingly simple. In fact, the coherent nuclear response is governed entirely by the scalar nucleon density, a quantity that is accurately determined by experiments. When considering specific extensions of the standard model that yield a leading coherent response, the $\mu\rightarrow e$ branching ratio can be predicted with a well-understood uncertainty. However, in the initial discovery phase of CLFV searches, one should not assume anything about the underlying nature of flavor-violating operators. The proper approach, which we pursue in this thesis, is to constrain the most general interaction as specified by the effective theory.

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco-and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ∼65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyperkeratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome towards PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage.AUTHOR SUMMARYPatients harboring oral premalignant lesions (PMLs) have an increased risk of developing oral squamous cell carcinoma (OSCC), but the underlying mechanisms driving transformation of PMLs to OSCC remain poorly understood. In this study, Khan et al., analyzed a newly generated dataset of gene expression and microbial profiles of oral tissues from patients diagnosed with PMLs from differing histopathological groups, including hyperkeratosis not reactive (HkNR) and dysplasia, comparing these profiles with OSCC and normal oral mucosa. Significant similarities between PMLs and OSCC were observed, with PMLs manifesting several cancer hallmarks, including oncogenic and immune pathways. The study also demonstrates associations between the abundance of multiple microbial species and PML groups, suggesting a potential contribution of the oral microbiome to the early stages of OSCC development. The study offers insights into the nature of the molecular, cellular and microbial heterogeneity of oral PMLs and suggests that molecular and clinical refinement of PMLs may provide opportunities for early disease detection and interception.

Background Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. Methods Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor’s evolution at subclonal resolution. Results The most unique, previously unreported aspect of the tumor’s evolution that we observed in this patient was the presence of “subclone incubators,” defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient’s metastases for effective clinical intervention. Conclusions Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

Background Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. Results To address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, the toolkit features traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis, combined with new methods for biomarker identification are. In addition, animalcules provides interactive and dynamic figures that enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. Conclusions We present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html.

Background . Periodontitis and rheumatoid arthritis have similar epidemiology and pathophysiology. Understanding the interaction between these two diseases is vital in our settings. We set out to assess the effect of oral hygiene interventions on disease activity of rheumatoid arthritis patients with periodontitis in Kampala, Uganda. Methods. Fifty-eight patients attending an arthritis clinic with rheumatoid arthritis and periodontitis were randomly assigned to either an intervention group or a control group. Patients diagnosed with rheumatoid arthritis at least two years before, who were on the same medication, dose, or formulation for RA treatment during the preceding three months, were included. The patients were >18 years of age, would be available for all the study visits in the next six months, had at least six natural teeth, had periodontal disease classified as Dutch Periodontal Index (DPSI) >3 and provided written informed consent. Those who had a chronic disorder requiring chronic or intermittent use of antibiotics, were pregnant, were lactating, or had intent to become pregnant were excluded. The primary outcome measure was a change in Disease Activity Score of 28 Joints (DAS28 score) in two 3-month follow-up periods after the intervention. The secondary outcome measure was a change in periodontal status. Results. There was a statistically significant improvement in the DAS-28 score in both the intervention and control arms during the follow-up period (PConclusion. Oral hygiene interventions given to RA patients could drastically improve their RA treatment outcomes, especially in resource-limited settings.

Ecologists have put forward many explanations for coexistence, but these are only partial explanations; nature is complex, so it is reasonable to assume that in any given ecological community, multiple mechanisms of coexistence are operating at the same time. Here, we present a methodology for quantifying the relative importance of different explanations for coexistence, based on an extension of Modern Coexistence Theory. Current versions of Modern Coexistence Theory only allow for the analysis of communities that are affected by spatial or temporal environmental variation, but not both. We show how to analyze communities with spatiotemporal fluctuations, how to parse the importance of spatial variation and temporal variation, and how to measure everything with either mathematical expressions or simulation experiments. Our extension of Modern Coexistence Theory allows empiricists to use realistic models and more data to better infer the mechanisms of coexistence in real communities., 40 pages, 1 figure, 2 tables

Software sandboxing or software-based fault isolation (SFI) is a lightweight approach to building secure systems out of untrusted components. Mozilla, for example, uses SFI to harden the Firefox browser by sandboxing third-party libraries, and companies like Fastly and Cloudflare use SFI to safely co-locate untrusted tenants on their edge clouds. While there have been significant efforts to optimize and verify SFI enforcement, context switching in SFI systems remains largely unexplored: almost all SFI systems use heavyweight transitions that are not only error-prone but incur significant performance overhead from saving, clearing, and restoring registers when context switching. We identify a set of zero-cost conditions that characterize when sandboxed code has sufficient structured to guarantee security via lightweight zero-cost transitions (simple function calls). We modify the Lucet Wasm compiler and its runtime to use zero-cost transitions, eliminating the undue performance tax on systems that rely on Lucet for sandboxing (e.g., we speed up image and font rendering in Firefox by up to 29.7% and 10% respectively). To remove the Lucet compiler and its correct implementation of the Wasm specification from the trusted computing base, we (1) develop a static binary verifier , VeriZero, which (in seconds) checks that binaries produced by Lucet satisfy our zero-cost conditions, and (2) prove the soundness of VeriZero by developing a logical relation that captures when a compiled Wasm function is semantically well-behaved with respect to our zero-cost conditions. Finally, we show that our model is useful beyond Wasm by describing a new, purpose-built SFI system, SegmentZero32, that uses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our zero-cost conditions; our prototype performs on-par with the state-of-the-art Native Client SFI system.

Binge eating is a heritable symptom of eating disorders with an unknown genetic etiology. Rodent models for binge-like eating (BLE) of palatable food permit the study of genetic and biological mechanisms. We previously genetically mapped a coding mutation in Cyfip2 associated with increased BLE of sweetened palatable food in the C57BL/6NJ versus C57BL/6J substrain. The increase in BLE in C57BL/6NJ mice was associated with a decrease in transcription of genes enriched for myelination in the striatum. Here, we tested the hypothesis that decreasing myelin levels with the demyelinating agent cuprizone would enhance BLE. Mice were treated with a 0.3% cuprizone home cage diet for two weeks. Cuprizone induced similar weight loss in both substrains and sexes that recovered within 48 h after removal of cuprizone. Following a three-week recovery period, mice were trained for BLE in an intermittent, limited access procedure. Surprisingly, cuprizone significantly reduced BLE in male but not female C57BL/6NJ mice while having no effect in C57BL/6J mice. Cuprizone also reduced myelin basic protein (MBP) at seven weeks post-cuprizone removal while having no effect on myelin-associated glycoprotein at this time point. C57BL/6NJ mice also showed less MBP than C57BL/6J mice. There were no statistical interactions of Treatment with Sex on MBP levels, indicating that differences in MBP reduction are unlikely to account for sex differences in BLE. To summarize, cuprizone induced an unexpected, significant reduction in BLE in C57BL/6NJ males, which could indicate genotype-dependent sex differences in the biological mechanisms of BLE.

The collection of microbes that live in and on the human body – the human microbiome – can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell–cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology – a systems biology approach. Published version

Maternal immune activation (MIA) disrupts the central innate immune system during a critical neurodevelopmental period. Microglia are primary innate immune cells in the brain although their direct influence on the MIA phenotype is largely unknown. Here we show that MIA alters microglial gene expression with upregulation of cellular protrusion/neuritogenic pathways, concurrently causing repetitive behavior, social deficits, and synaptic dysfunction to layer V intrinsically bursting pyramidal neurons in the prefrontal cortex of mice. MIA increases plastic dendritic spines of the intrinsically bursting neurons and their interaction with hyper-ramified microglia. Treating MIA offspring by colony stimulating factor 1 receptor inhibitors induces depletion and repopulation of microglia, and corrects protein expression of the newly identified MIA-associated neuritogenic molecules in microglia, which coalesces with correction of MIA-associated synaptic, neurophysiological, and behavioral abnormalities. Our study demonstrates that maternal immune insults perturb microglial phenotypes and influence neuronal functions throughout adulthood, and reveals a potent effect of colony stimulating factor 1 receptor inhibitors on the correction of MIA-associated microglial, synaptic, and neurobehavioral dysfunctions.

Purpose: To report the approach, technical success, clinical outcomes, and complications of interventional radiology-operated endoscopy using the LithoVue disposable endoscope. Materials and Methods: 12 patients, 6 (50%) males and 6 (50%) females, underwent interventional radiology-operated endoscopy using the LithoVue disposable endoscope between April 2016 and August 2017. Presenting complaint, reason for endoscopic evaluation, technical success, clinical success, procedure time, fluoroscopy time, hospital length of stay, and complications were recorded. Results: Interventional radiology-operated endoscopy using the LithoVue disposable endoscope was used in 8 (67%) biliary, 2 (16.5%) urologic, and 2 (16.5%) gastrointestinal procedures. Five (42%) patients required endoscopy to locate an ostium that could not be cannulated under fluoroscopy. Five (42%) patients had a biliary-enteric anastomosis with postoperative anatomy of the bowel which precluded endoscopic retrograde cholangiopancreatography and underwent endoscopy for removal of calculi. One (8%) patient underwent treatment of a stricture and one (8%) underwent biopsy. Technical success was 92% (11/12). Clinical success was 83% (10/12). Two complications, a perinephric hematoma and sepsis, occurred. Conclusion: Disposable endoscopes, including the LithoVue, may be used in a variety of procedures to improve patient care and limit fluoroscopy.

Background The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns. Methods Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines. Results Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways (“the survival phenotype”) or the EGFR, KRAS (G12V), RAF1, and BAD pathways (“the growth phenotype”). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies. Conclusions Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0429-x) contains supplementary material, which is available to authorized users.

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cell’s native mRNA. This results in cross-contamination of transcripts between different cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNA-seq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

Nervous systems have evolved to combine environmental information with internal state to select and generate adaptive behavioral sequences. To better understand these computations and their implementation in neural circuits, natural behavior must be carefully measured and quantified. Here, we collect high spatial resolution video of single zebrafish larvae swimming in a naturalistic environment and develop models of their action selection across exploration and hunting. Zebrafish larvae swim in punctuated bouts separated by longer periods of rest called interbout intervals. We take advantage of this structure by categorizing bouts into discrete types and representing their behavior as labeled sequences of bout-types emitted over time. We then construct probabilistic models – specifically, marked renewal processes – to evaluate how bout-types and interbout intervals are selected by the fish as a function of its internal hunger state, behavioral history, and the locations and properties of nearby prey. Finally, we evaluate the models by their predictive likelihood and their ability to generate realistic trajectories of virtual fish swimming through simulated environments. Our simulations capture multiple timescales of structure in larval zebrafish behavior and expose many ways in which hunger state influences their action selection to promote food seeking during hunger and safety during satiety.

Background Current cardiovascular risk reduction guidance focuses on shifts in dietary patterns, rather than single foods or nutrients. Experimental studies are needed to identify the mechanisms by which food-based diets affect the development and progression of atherosclerosis. Objectives The aim of this study was to investigate the effect of 2 food-based dietary patterns and statin therapy on the transcriptome of the left anterior descending coronary artery of the Ossabaw pig. Methods Pigs were randomly assigned to 1 of 4 groups and fed isocaloric diets for 6 mo; Heart Healthy–style diet (HHD) (high in unsaturated fat, unrefined grain, fruits/vegetables) or Western-style diet (WD) (high in saturated fat, cholesterol, refined grain), with or without atorvastatin. A 2-factor edge R analysis was used to determine differential gene expression in the left anterior descending coronary artery. Results Relative to the HHD, the WD resulted in the differential expression of 143 genes, of which 139 genes were upregulated and 4 genes were downregulated (all log fold change ≥0.6, false discovery rate

Epicardial adipose tissue (EAT) inflammation is thought to potentiate the development of coronary artery disease (CAD). Overall diet quality and statin therapy are important modulators of inflammation and CAD progression. Our objective was to examine the effects and interaction of dietary patterns and statin therapy on EAT gene expression in the Ossabaw pig. Pigs were randomized to 1 of 4 groups; Heart Healthy diet (high in unsaturated fat, unrefined grain, fruits/vegetables [HHD]) or Western diet (high in saturated fat, cholesterol, refined grain [WD]), with or without atorvastatin. Diets were fed in isocaloric amounts for 6 months. A two-factor edge R analysis identified the differential expression of 21 genes. Relative to the HHD, the WD resulted in a significant 12-fold increase of radical s-adenosyl methionine domain containing 2 (RSAD2), a gene induced by interferon signaling. Atorvastatin led to the significant differential expression of 17 genes predominately involved in interferon signaling. Results were similar using the Porcine Translational Research Database. Pathway analysis confirmed the up-regulation of interferon signaling in response to the WD and atorvastatin independently. An expression signature of the largely interferon related differentially expressed genes had no predictive capability on a histological assessment of atherosclerosis in the underlying coronary artery. These results suggest that a WD and atorvastatin evoke an interferon mediated immune response in EAT of the Ossabaw pig, which is not associated with the presence of atherosclerosis.

The most dramatic "Sommerfeld enhancements" of neutral-wino-pair annihilation occur when the wino mass is tuned to near critical values where there is a zero-energy S-wave resonance at the neutral-wino-pair threshold. If the wino mass is larger than the critical value, the resonance is a wino-pair bound state. If the wino mass is near a critical value, low-energy winos can be described by a zero-range effective field theory in which the winos interact nonperturbatively through a contact interaction. The parameters of the zero-range effective field theory can be determined by matching wino scattering amplitudes calculated by solving the Schr\"odinger equation for a nonrelativistic effective field theory in which the winos interact through a potential due to the exchange of electroweak gauge bosons. The utility of the zero-range effective field theory is illustrated by calculating the rate for formation of an S-wave bound state in the collision of two neutral winos through the emission of two soft photons., Comment: 6 pages, 5 figures. Contribution to proceedings of ICHEP 2016

Diagnoses that are both timely and accurate are critically important for patients with life-threatening or drug resistant infections. Technological improvements in High-Throughput Sequencing (HTS) have led to its use in pathogen detection and its application in clinical diagnoses of infectious diseases. The present study compares two HTS methods, 16S rRNA marker gene sequencing (metataxonomics) and whole metagenomic shotgun sequencing (metagenomics), in their respective abilities to match the same diagnosis as traditional culture methods (culture inference) for patients with ventilator associated pneumonia (VAP). The metagenomic analysis was able to produce the same diagnosis as culture methods at the species-level for five of the six samples, while the metataxonomic analysis was only able to produce results with the same species-level identification as culture for two of the six samples. These results indicate that metagenomic analyses have the accuracy needed for a clinical diagnostic tool, but full integration in diagnostic protocols is contingent on technological improvements to decrease turnaround time and lower costs.