Is a Calorie Really a Calorie? Metabolic Advantage of Low-Carbohydrate Diets

The first law of thermodynamics dictates that body mass remains constant when caloric intake equals caloric expenditure. It should be noted, however, that different diets lead to different biochemical pathways that are not equivalent when correctly compared through the laws of thermodynamics. It is inappropriate to assume that the only thing that counts in terms of food consumption and energy balance is the intake of dietary calories and weight storage. Well-controlled studies suggest that calorie content may not be as predictive of fat loss as is reduced carbohydrate consumption. Biologically speaking, a calorie is certainly not a calorie. The ideal weight loss diet, if it even exists, remains to be determined, but a high-carbohydrate/low-protein diet may be unsatisfactory for many obese individuals

A Formalism for the Systematic Treatment of Rapidity Logarithms in Quantum Field Theory

Many observables in QCD rely upon the resummation of perturbation theory to retain predictive power. Resummation follows after one factorizes the cross section into the rele- vant modes. The class of observables which are sensitive to soft recoil effects are particularly challenging to factorize and resum since they involve rapidity logarithms. In this paper we will present a formalism which allows one to factorize and resum the perturbative series for such observables in a systematic fashion through the notion of a "rapidity renormalization group". That is, a Collin-Soper like equation is realized as a renormalization group equation, but has a more universal applicability to observables beyond the traditional transverse momentum dependent parton distribution functions (TMDPDFs) and the Sudakov form factor. This formalism has the feature that it allows one to track the (non-standard) scheme dependence which is inherent in any scenario where one performs a resummation of rapidity divergences. We present a pedagogical introduction to the formalism by applying it to the well-known massive Sudakov form factor. The formalism is then used to study observables of current interest. A factorization theorem for the transverse momentum distribution of Higgs production is presented along with the result for the resummed cross section at NLL. Our formalism allows one to define gauge invariant TMDPDFs which are independent of both the hard scattering amplitude and the soft function, i.e. they are uni- versal. We present details of the factorization and resummation of the jet broadening cross section including a renormalization in pT space. We furthermore show how to regulate and renormalize exclusive processes which are plagued by endpoint singularities in such a way as to allow for a consistent resummation.Comment: Typos in Appendix C corrected, as well as a typo in eq. 5.6

Constructing Biological Pathways by a Two-Step Counting Approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability

The impacts of Information and Communications Technology (ICT) and E-commerce on bilateral trade flows

This study examines the impact of Internet and e-commerce adoption on bilateral trade flows using a panel of 21 developing- and least-developed countries and 30 OECD countries. Given the commitment of East African Community (EAC) to become the frontrunner in export-led economy across the African continent, special attention is dedicated to analyse the role of ICT and e-commerce on EAC’s export performance. The empirical results indicate that better access to the modern ICT and adoption of e-commerce applications stimulate bilateral trade flows at various levels. The study notes that the efficient use of ICT equipped with high speed internet and secured servers is a crucial milestone for unlocking the e-trade potentials for developing- and least-developed counties

Lessons from bright-spots for advancing knowledge exchange at the interface of marine science and policy

Evidence-informed decision-making is in increasing demand given growing pressures on marine environments. A way to facilitate this is by knowledge exchange among marine scientists and decision-makers. While many barriers are reported in the literature, there are also examples whereby research has successfully informed marine decision-making (i.e., 'bright-spots'). Here, we identify and analyze 25 bright-spots from a wide range of marine fields, contexts, and locations to provide insights into how to improve knowledge exchange at the interface of marine science and policy. Through qualitative surveys we investigate what initiated the bright-spots, their goals, and approaches to knowledge exchange. We also seek to identify what outcomes/impacts have been achieved, the enablers of success, and what lessons can be learnt to guide future knowledge exchange efforts. Results show that a diversity of approaches were used for knowledge exchange, from consultative engagement to genuine knowledge co-production. We show that diverse successes at the interface of marine science and policy are achievable and include impacts on policy, people, and governance. Such successes were enabled by factors related to the actors, processes, support, context, and timing. For example, the importance of involving diverse actors and managing positive relationships is a key lesson for success. However, enabling routine success will require: 1) transforming the ways in which we train scientists to include a greater focus on interpersonal skills, 2) institutionalizing and supporting knowledge exchange activities in organizational agendas, 3) conceptualizing and implementing broader research impact metrics, and 4) transforming funding mechanisms to focus on need-based interventions, impact planning, and an acknowledgement of the required time and effort that underpin knowledge exchange activities

Prepatterning in the Stem Cell Compartment

The mechanism by which an apparently uniform population of cells can generate a heterogeneous population of differentiated derivatives is a fundamental aspect of pluripotent and multipotent stem cell behaviour. One possibility is that the environment and the differentiation cues to which the cells are exposed are not uniform. An alternative, but not mutually exclusive possibility is that the observed heterogeneity arises from the stem cells themselves through the existence of different interconvertible substates that pre-exist before the cells commit to differentiate. We have tested this hypothesis in the case of apparently homogeneous pluripotent human embryonal carcinoma (EC) stem cells, which do not follow a uniform pattern of differentiation when exposed to retinoic acid. Instead, they produce differentiated progeny that include both neuronal and non-neural phenotypes. Our results suggest that pluripotent NTERA2 stem cells oscillate between functionally distinct substates that are primed to select distinct lineages when differentiation is induced

Therapeutic efficacy in a hemophilia B model using a biosynthetic mRNA liver depot system

DNA-based gene therapy has considerable therapeutic potential, but the challenges associated with delivery continue to limit progress. Messenger RNA (mRNA) has the potential to provide for transient production of therapeutic proteins, without the need for nuclear delivery and without the risk of insertional mutagenesis. Here we describe the sustained delivery of therapeutic proteins in vivo in both rodents and non-human primates via nanoparticle-formulated mRNA. Nanoparticles formulated with lipids and lipid-like materials were developed for delivery of two separate mRNA transcripts encoding either human erythropoietin (hEPO) or factor IX (hFIX) protein. Dose-dependent protein production was observed for each mRNA construct. Upon delivery of hEPO mRNA in mice, serum EPO protein levels reached several orders of magnitude (>125 000-fold) over normal physiological values. Further, an increase in hematocrit (Hct) was established, demonstrating that the exogenous mRNA-derived protein maintained normal activity. The capacity of producing EPO in non-human primates via delivery of formulated mRNA was also demonstrated as elevated EPO protein levels were observed over a 72-h time course. Exemplifying the possible broad utility of mRNA drugs, therapeutically relevant amounts of human FIX (hFIX) protein were achieved upon a single intravenous dose of hFIX mRNA-loaded lipid nanoparticles in mice. In addition, therapeutic value was established within a hemophilia B (FIX knockout (KO)) mouse model by demonstrating a marked reduction in Hct loss following injury (incision) to FIX KO mice

Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data

Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomicbased approach to identify drug effects by monitoring drug-induced topology alterations. With the proposed method, drug effects are investigated under several conditions on a cell-type specific signaling network. First, starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental cases. Fitting is performed via a formulation as an Integer Linear Program (ILP) and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell topology, we monitor the same key phopshoprotein signals under the presence of drug and cytokines and we re-optimize the specific map to reveal the drug-induced topology alterations. To prove our case, we make a pathway map for the hepatocytic cell line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non selective drug. We confirm effects easily predictable from the drugs’ main target (i.e. EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell’s specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib is able to inhibit signaling downstream the Interleukin-1alpha (IL-1α) pathway; an effect that cannot be extracted from binding affinity based approaches. Our method represents an unbiased approach to identify drug effects on a small to medium size pathways and is scalable to larger topologies with any type of signaling perturbations (small molecules, 3 RNAi etc). The method is a step towards a better picture of drug effects in pathways, the cornerstone in identifying the mechanisms of drug efficacy and toxicity