Decaying Dark Matter in the Supersymmetric Standard Model with Freeze-in and Seesaw mechanims

Inspired by the decaying dark matter (DM) which can explain cosmic ray anomalies naturally, we consider the supersymmetric Standard Model with three right-handed neutrinos (RHNs) and R-parity, and introduce a TeV-scale DM sector with two fields \phi_{1,2} and a $Z_3$ discrete symmetry. The DM sector only interacts with the RHNs via a very heavy field exchange and then we can explain the cosmic ray anomalies. With the second right-handed neutrino N_2 dominant seesaw mechanism at the low scale around 10^4 GeV, we show that \phi_{1,2} can obtain the vacuum expectation values around the TeV scale, and then the lightest state from \phi_{1,2} is the decay DM with lifetime around \sim 10^{26}s. In particular, the DM very long lifetime is related to the tiny neutrino masses, and the dominant DM decay channels to \mu and \tau are related to the approximate \mu-\tau symmetry. Furthermore, the correct DM relic density can be obtained via the freeze-in mechanism, the small-scale problem for power spectrum can be solved due to the decays of the R-parity odd meta-stable states in the DM sector, and the baryon asymmetry can be generated via the soft leptogensis.Comment: 24 pages,3 figure

Dietary and Physical Activity Interventions for Colorectal Cancer Survivors: A Randomized Controlled Trial

Abstract There has been evidence on the protective effects of diets high in fiber and low in red and processed meat (RPM), and physical activity (PA) against colorectal cancer (CRC) development, but that against CRC recurrence has been limited. This study evaluated the efficacy of a behavioral program comprising dietary and PA interventions in improving Chinese CRC survivors’ lifestyle. A 2 × 2 factorial randomized controlled trial of 223 CRC patients (82 females, mean age 65), randomly assigned to receive dietary, PA or both interventions, or usual care for 12 months, and assessed every 6 months for 24 months. Primary outcomes included two dietary and two PA targets. Secondary outcomes included changes in dietary consumptions and PA levels. Dietary interventions significantly increased the odds of achieving the targets of consuming less RPM at all time-points (OR 3.22–4.57, all p < 0.01) and refined grain (RG) at months 6 (OR 3.13, p = 0.002) and 24 (OR 2.19, p = 0.039), and reduced RPM (2.49–3.48 servings/week, all p < 0.01) and RG (0.31–0.5 servings/day, all p < 0.01) consumptions. Patients receiving PA interventions potentially spent more time on moderate-to-vigorous PA. This study demonstrated the efficacy of a behavioral program in improving dietary habits of Chinese CRC survivors

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools)

The kinome of Phytophthora infestans reveals oomycete-specific innovations and links to other taxonomic groups

<p>Abstract</p> <p>Background</p> <p>Oomycetes are a large group of economically and ecologically important species. Its most notorious member is <it>Phytophthora infestans</it>, the cause of the devastating potato late blight disease. The life cycle of <it>P. infestans </it>involves hyphae which differentiate into spores used for dispersal and host infection. Protein phosphorylation likely plays crucial roles in these stages, and to help understand this we present here a genome-wide analysis of the protein kinases of <it>P. infestans </it>and several relatives. The study also provides new insight into kinase evolution since oomycetes are taxonomically distant from organisms with well-characterized kinomes.</p> <p>Results</p> <p>Bioinformatic searches of the genomes of <it>P. infestans</it>, <it>P. ramorum</it>, and <it>P. sojae </it>reveal they have similar kinomes, which for <it>P. infestans </it>contains 354 eukaryotic protein kinases (ePKs) and 18 atypical kinases (aPKs), equaling 2% of total genes. After refining gene models, most were classifiable into families seen in other eukaryotes. Some ePK families are nevertheless unusual, especially the tyrosine kinase-like (TKL) group which includes large oomycete-specific subfamilies. Also identified were two tyrosine kinases, which are rare in non-metazoans. Several ePKs bear accessory domains not identified previously on kinases, such as cyclin-dependent kinases with integral cyclin domains. Most ePKs lack accessory domains, implying that many are regulated transcriptionally. This was confirmed by mRNA expression-profiling studies that showed that two-thirds vary significantly between hyphae, sporangia, and zoospores. Comparisons to neighboring taxa (apicomplexans, ciliates, diatoms) revealed both clade-specific and conserved features, and multiple connections to plant kinases were observed. The kinome of <it>Hyaloperonospora arabidopsidis</it>, an oomycete with a simpler life cycle than <it>P. infestans</it>, was found to be one-third smaller. Some differences may be attributable to gene clustering, which facilitates subfamily expansion (or loss) through unequal crossing-over.</p> <p>Conclusion</p> <p>The large sizes of the <it>Phytophthora </it>kinomes imply that phosphorylation plays major roles in their life cycles. Their kinomes also include many novel ePKs, some specific to oomycetes or shared with neighboring groups. Little experimentation to date has addressed the biological functions of oomycete kinases, but this should be stimulated by the structural, evolutionary, and expression data presented here. This may lead to targets for disease control.</p

Etoricoxib - preemptive and postoperative analgesia (EPPA) in patients with laparotomy or thoracotomy - design and protocols

<p>Abstract</p> <p>Background and Objective</p> <p>Our objective was to report on the design and essentials of the <it>Etoricoxib </it>protocol<it>- Preemptive and Postoperative Analgesia (EPPA) </it>Trial, investigating whether preemptive analgesia with cox-2 inhibitors is more efficacious than placebo in patients who receive either laparotomy or thoracotomy.</p> <p>Design and Methods</p> <p>The study is a 2 × 2 factorial armed, double blinded, bicentric, randomised placebo-controlled trial comparing (a) etoricoxib and (b) placebo in a pre- and postoperative setting. The total observation period is 6 months. According to a power analysis, 120 patients scheduled for abdominal or thoracic surgery will randomly be allocated to either the preemptive or the postoperative treatment group. These two groups are each divided into two arms. Preemptive group patients receive etoricoxib prior to surgery and either etoricoxib again or placebo postoperatively. Postoperative group patients receive placebo prior to surgery and either placebo again or etoricoxib after surgery (2 × 2 factorial study design). The Main Outcome Measure is the cumulative use of morphine within the first 48 hours after surgery (measured by patient controlled analgesia PCA). Secondary outcome parameters include a broad range of tests including sensoric perception and genetic polymorphisms.</p> <p>Discussion</p> <p>The results of this study will provide information on the analgesic effectiveness of etoricoxib in preemptive analgesia and will give hints on possible preventive effects of persistent pain.</p> <p>Trial registration</p> <p>NCT00716833</p

Do parents of children with congenital malformations have a higher cancer risk? A nationwide study in Denmark

To investigate whether parents of children with congenital malformations more often developed cancer after birth of the child, a population-based case-control study in Denmark was undertaken. By linking the Cancer Registry with the Central Population Registry, we identified 8783 cancer patients having their first child born between 1977 and 1995 before the cancer was diagnosed. Parents of 41 206 firstborn children of a 10% random sample of newborns from the Birth Registry between 1980 and 1995 were identified as controls. We obtained malformation diagnoses of children of cases and controls by linking to the Hospital Discharge Registry. We estimated the association between malformation and cancer by using logistic regression, adjusting for maternal age at birth and sex of child. We found no increased risk of cancer in parents having children with malformations in general, but a higher cancer risk in parents of children born with cleft lip/palate, odds ratio (OR) for all cancer=1.8 (95% confidence interval 1.0–3.2), OR for lymphomas=4.2 (1.3–13.5) and OR for leukaemia=8.1 (2.0–33.7). This association was not restricted to cancer cases diagnosed shortly after birth of the child. Our results suggest a common genetic association between these diseases, but further studies are needed

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Combined mRNA expression levels of members of the urokinase plasminogen activator (uPA) system correlate with disease-associated survival of soft-tissue sarcoma patients

<p>Abstract</p> <p>Background</p> <p>Members of the urokinase-type plasminogen activator (uPA) system are up-regulated in various solid malignant tumors. High antigen levels of uPA, its inhibitor PAI-1 and its receptor uPAR have recently been shown to be associated with poor prognosis in soft-tissue sarcoma (STS) patients. However, the mRNA expression of uPA system components has not yet been comprehensively investigated in STS patients.</p> <p>Methods</p> <p>The mRNA expression level of uPA, PAI-1, uPAR and an uPAR splice variant, uPAR-del4/5, was analyzed in tumor tissue from 78 STS patients by quantitative PCR.</p> <p>Results</p> <p>Elevated mRNA expression levels of PAI-1 and uPAR-del4/5 were significantly associated with clinical parameters such as histological subtype (<it>P </it>= 0.037 and <it>P </it>< 0.001, respectively) and higher tumor grade (<it>P </it>= 0.017 and <it>P </it>= 0.003, respectively). In addition, high uPAR-del4/5 mRNA values were significantly related to higher tumor stage of STS patients (<it>P </it>= 0.031). On the other hand, mRNA expression of uPA system components was not significantly associated with patients' survival. However, in STS patients with complete tumor resection (R0), high PAI-1 and uPAR-del4/5 mRNA levels were associated with a distinctly increased risk of tumor-related death (RR = 6.55, <it>P </it>= 0.054 and RR = 6.00, <it>P </it>= 0.088, respectively). Strikingly, R0 patients with both high PAI-1 and uPAR-del4/5 mRNA expression levels showed a significant, 19-fold increased risk of tumor-related death (<it>P </it>= 0.044) compared to the low expression group.</p> <p>Conclusion</p> <p>Our results suggest that PAI-1 and uPAR-del4/5 mRNA levels may add prognostic information in STS patients with R0 status and distinguish a subgroup of R0 patients with low PAI-1 and/or low uPAR-del4/5 values who have a better outcome compared to patients with high marker levels.</p

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis

Genome-Scale Reconstruction of Escherichia coli's Transcriptional and Translational Machinery: A Knowledge Base, Its Mathematical Formulation, and Its Functional Characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship