Mixed Wino Dark Matter: Consequences for Direct, Indirect and Collider Detection

In supersymmetric models with gravity-mediated SUSY breaking and gaugino mass unification, the predicted relic abundance of neutralinos usually exceeds the strict limits imposed by the WMAP collaboration. One way to obtain the correct relic abundance is to abandon gaugino mass universality and allow a mixed wino-bino lightest SUSY particle (LSP). The enhanced annihilation and scattering cross sections of mixed wino dark matter (MWDM) compared to bino dark matter lead to enhanced rates for direct dark matter detection, as well as for indirect detection at neutrino telescopes and for detection of dark matter annihilation products in the galactic halo. For collider experiments, MWDM leads to a reduced but significant mass gap between the lightest neutralinos so that chi_2^0 two-body decay modes are usually closed. This means that dilepton mass edges-- the starting point for cascade decay reconstruction at the CERN LHC-- should be accessible over almost all of parameter space. Measurement of the m_{\tz_2}-m_{\tz_1} mass gap at LHC plus various sparticle masses and cross sections as a function of beam polarization at the International Linear Collider (ILC) would pinpoint MWDM as the dominant component of dark matter in the universe.Comment: 29 pages including 19 eps figure

Exploring the BWCA (Bino-Wino Co-Annihilation) Scenario for Neutralino Dark Matter

In supersymmetric models with non-universal gaugino masses, it is possible to have opposite-sign SU(2) and U(1) gaugino mass terms. In these models, the gaugino eigenstates experience little mixing so that the lightest SUSY particle remains either pure bino or pure wino. The neutralino relic density can only be brought into accord with the WMAP measured value when bino-wino co-annihilation (BWCA) acts to enhance the dark matter annihilation rate. We map out parameter space regions and mass spectra which are characteristic of the BWCA scenario. Direct and indirect dark matter detection rates are shown to be typically very low. At collider experiments, the BWCA scenario is typified by a small mass gap m_{\tilde Z_2}-m_{\tilde Z_1} ~ 20-80 GeV, so that tree level two body decays of \tilde Z_2 are not allowed. However, in this case the second lightest neutralino has an enhanced loop decay branching fraction to photons. While the photonic neutralino decay signature looks difficult to extract at the Fermilab Tevatron, it should lead to distinctive events at the CERN LHC and at a linear e^+e^- collider.Comment: 44 pages, 21 figure

Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism.

The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes' degrees remains constant

Mixed Higgsino Dark Matter from a Reduced SU(3) Gaugino Mass: Consequences for Dark Matter and Collider Searches

In gravity-mediated SUSY breaking models with non-universal gaugino masses, lowering the SU(3) gaugino mass |M_3| leads to a reduction in the squark and gluino masses. Lower third generation squark masses, in turn, diminish the effect of a large top quark Yukawa coupling in the running of the higgs mass parameter m_{H_u}^2, leading to a reduction in the magnitude of the superpotential mu parameter (relative to M_1 and M_2). A low | mu | parameter gives rise to mixed higgsino dark matter (MHDM), which can efficiently annihilate in the early universe to give a dark matter relic density in accord with WMAP measurements. We explore the phenomenology of the low |M_3| scenario, and find for the case of MHDM increased rates for direct and indirect detection of neutralino dark matter relative to the mSUGRA model. The sparticle mass spectrum is characterized by relatively light gluinos, frequently with m(gl)<<m(sq). If scalar masses are large, then gluinos can be very light, with gl->Z_i+g loop decays dominating the gluino branching fraction. Top squarks can be much lighter than sbottom and first/second generation squarks. The presence of low mass higgsino-like charginos and neutralinos is expected at the CERN LHC. The small m(Z2)-m(Z1) mass gap should give rise to a visible opposite-sign/same flavor dilepton mass edge. At a TeV scale linear e^+e^- collider, the region of MHDM will mean that the entire spectrum of charginos and neutralinos are amongst the lightest sparticles, and are most likely to be produced at observable rates, allowing for a complete reconstruction of the gaugino-higgsino sector.Comment: 35 pages, including 26 EPS figure

A correlative and quantitative imaging approach enabling characterization of primary cell-cell communication: Case of human CD4+ T cell-macrophage immunological synapses

Cell-to-cell communication engages signaling and spatiotemporal reorganization events driven by highly context-dependent and dynamic intercellular interactions, which are difficult to capture within heterogeneous primary cell cultures. Here, we present a straightforward correlative imaging approach utilizing commonly available instrumentation to sample large numbers of cell-cell interaction events, allowing qualitative and quantitative characterization of rare functioning cell-conjugates based on calcium signals. We applied this approach to examine a previously uncharacterized immunological synapse, investigating autologous human blood CD4+ T cells and monocyte-derived macrophages (MDMs) forming functional conjugates in vitro. Populations of signaling conjugates were visualized, tracked and analyzed by combining live imaging, calcium recording and multivariate statistical analysis. Correlative immunofluorescence was added to quantify endogenous molecular recruitments at the cell-cell junction. By analyzing a large number of rare conjugates, we were able to define calcium signatures associated with different states of CD4+ T cell-MDM interactions. Quantitative image analysis of immunostained conjugates detected the propensity of endogenous T cell surface markers and intracellular organelles to polarize towards cell-cell junctions with high and sustained calcium signaling profiles, hence defining immunological synapses. Overall, we developed a broadly applicable approach enabling detailed single cell- and population-based investigations of rare cell-cell communication events with primary cells

The efficacy of various machine learning models for multi-class classification of RNA-seq expression data

Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present a barrier to the adoption of their routine use. There is a pressing need for accurate methods that enable early diagnosis and cover a broad range of cancers. The use of machine learning and RNA-seq expression analysis has shown promise in the classification of cancer type. However, research is inconclusive about which type of machine learning models are optimal. The suitability of five algorithms were assessed for the classification of 17 different cancer types. Each algorithm was fine-tuned and trained on the full array of 18,015 genes per sample, for 4,221 samples (75 % of the dataset). They were then tested with 1,408 samples (25 % of the dataset) for which cancer types were withheld to determine the accuracy of prediction. The results show that ensemble algorithms achieve 100% accuracy in the classification of 14 out of 17 types of cancer. The clustering and classification models, while faster than the ensembles, performed poorly due to the high level of noise in the dataset. When the features were reduced to a list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as opposed to the clustering and classification models.Comment: 12 pages, 4 figures, 3 tables, conference paper: Computing Conference 2019, published at https://link.springer.com/chapter/10.1007/978-3-030-22871-2_6

Monthly intravenous methylprednisolone in relapsing-remitting multiple sclerosis - reduction of enhancing lesions, T2 lesion volume and plasma prolactin concentrations

BACKGROUND: Intravenous methylprednisolone (IV-MP) is an established treatment for multiple sclerosis (MS) relapses, accompanied by rapid, though transient reduction of gadolinium enhancing (Gd+) lesions on brain MRI. Intermittent IV-MP, alone or with immunomodulators, has been suggested but insufficiently studied as a strategy to prevent relapses. METHODS: In an open, single-cross-over study, nine patients with relapsing-remitting MS (RR-MS) underwent cranial Gd-MRI once monthly for twelve months. From month six on, they received a single i.v.-infusion of 500 mg methylprednisolone (and oral tapering for three days) after the MRI. Primary outcome measure was the mean number of Gd+ lesions during treatment vs. baseline periods; T2 lesion volume and monthly plasma concentrations of cortisol, ACTH and prolactin were secondary outcome measures. Safety was assessed clinically, by routine laboratory and bone mineral density measurements. Soluble immune parameters (sTNF-RI, sTNF-RII, IL1-ra and sVCAM-1) and neuroendocrine tests (ACTH test, combined dexamethasone/CRH test) were additionally analyzed. RESULTS: Comparing treatment to baseline periods, the number of Gd+ lesions/scan was reduced in eight of the nine patients, by a median of 43.8% (p = 0.013, Wilcoxon). In comparison, a pooled dataset of 83 untreated RR-MS patients from several studies, selected by the same clinical and MRI criteria, showed a non-significant decrease by a median of 14% (p = 0.32). T2 lesion volume decreased by 21% during treatment (p = 0.001). Monthly plasma prolactin showed a parallel decline (p = 0.027), with significant cross-correlation with the number of Gd+ lesions. Other hormones and immune system variables were unchanged, as were ACTH test and dexamethasone-CRH test. Treatment was well tolerated; routine laboratory and bone mineral density were unchanged. CONCLUSION: Monthly IV-MP reduces inflammatory activity and T2 lesion volume in RR-MS

Retention on Buprenorphine Is Associated with High Levels of Maximal Viral Suppression among HIV-Infected Opioid Dependent Released Prisoners

HIV-infected prisoners lose viral suppression within the 12 weeks after release to the community. This prospective study evaluates the use of buprenorphine/naloxone (BPN/NLX) as a method to reduce relapse to opioid use and sustain viral suppression among released HIV-infected prisoners meeting criteria for opioid dependence (OD).From 2005-2010, 94 subjects meeting DSM-IV criteria for OD were recruited from a 24-week prospective trial of directly administered antiretroviral therapy (DAART) for released HIV-infected prisoners; 50 (53%) selected BPN/NLX and were eligible to receive it for 6 months; the remaining 44 (47%) selected no BPN/NLX therapy. Maximum viral suppression (MVS), defined as HIV-1 RNA<50 copies/mL, was compared for the BPN/NLX and non-BPN/NLX (N = 44) groups.The two groups were similar, except the BPN/NLX group was significantly more likely to be Hispanic (56.0% v 20.4%), from Hartford (74.4% v 47.7%) and have higher mean global health quality of life indicator scores (54.18 v 51.40). MVS after 24 weeks of being released was statistically correlated with 24-week retention on BPN/NLX [AOR = 5.37 (1.15, 25.1)], having MVS at the time of prison-release [AOR = 10.5 (3.21, 34.1)] and negatively with being Black [AOR = 0.13 (0.03, 0.68)]. Receiving DAART or methadone did not correlate with MVS.In recognition that OD is a chronic relapsing disease, strategies that initiate and retain HIV-infected prisoners with OD on BPN/NLX is an important strategy for improving HIV treatment outcomes as a community transition strategy

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data