β-Glycosphingolipids as Mediators of Both Inflammation and Immune Tolerance: A Manifestation of Randomness in Biological Systems

Plasticity in biological systems is attributed to the combination of multiple parameters which determine function. These include genotypic, phenotypic, and environmental factors. While biological processes can be viewed as ordered and sequential, biological randomness was suggested to underline part of them. The present review looks into the concept of randomness in biological systems by exploring the glycosphingolipids-NKT cells example. NKT cells are a unique subset of regulatory lymphocytes which play a role in both inflammation and tolerance. Glycosphingolipids promote an immune balance by changing different arms of the immune system in opposing environments. Traditional immunology looks at skewing the immune system into different directions by different types of activation of the same cell stimulation of different cells subsets, use of different ligands, or different the effect of different immune environments. While these may explain some of the effects, the lack of consistency and opposing results under similar settings may involve randomness which may also be part of real life effects of immunomodulatory agents. It means that several of the biological processes, cannot be explained by simple linear models, and may involve more complex concepts. The application for these concepts for improving therapies to patients with Gaucher disease are discussed.SUMMARY The use of different ligands that target a variety of cell subsets in different immune environments may underlie differences in the functionality of NKT cells and their variability in response to NKT-based therapies. The novel concept of randomness in biology means that several biological processes cannot be solely explained by simple linear models and may instead involve much more complicated schemes of random disorder. These may have implications on future design of therapeutic regimens for improving the response to current treatments

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

Aging is a complex biological process with multifactorial nature underlined by genetic, environmental, and social factors. In the present paper, we review several mechanisms of aging and the pre-clinically and clinically studied anti-aging therapies. Variability characterizes biological processes from the genome to cellular organelles, biochemical processes, and whole organs’ function. Aging is associated with alterations in the degrees of variability and complexity of systems. The constrained disorder principle defines living organisms based on their inherent disorder within arbitrary boundaries and defines aging as having a lower variability or moving outside the boundaries of variability. We focus on associations between variability and hallmarks of aging and discuss the roles of disorder and variability of systems in the pathogenesis of aging. The paper presents the concept of implementing the constrained disease principle-based second-generation artificial intelligence systems for improving anti-aging modalities. The platform uses constrained noise to enhance systems’ efficiency and slow the aging process. Described is the potential use of second-generation artificial intelligence systems in patients with chronic disease and its implications for the aged population

PTF11eon/SN2011dh: Discovery of a Type IIb Supernova From a Compact Progenitor in the Nearby Galaxy M51

On May 31, 2011 UT a supernova (SN) exploded in the nearby galaxy M51 (the Whirlpool Galaxy). We discovered this event using small telescopes equipped with CCD cameras, as well as by the Palomar Transient Factory (PTF) survey, and rapidly confirmed it to be a Type II supernova. Our early light curve and spectroscopy indicates that PTF11eon resulted from the explosion of a relatively compact progenitor star as evidenced by the rapid shock-breakout cooling seen in the light curve, the relatively low temperature in early-time spectra and the prompt appearance of low-ionization spectral features. The spectra of PTF11eon are dominated by H lines out to day 10 after explosion, but initial signs of He appear to be present. Assuming that He lines continue to develop in the near future, this SN is likely a member of the cIIb (compact IIb; Chevalier and Soderberg 2010) class, with progenitor radius larger than that of SN 2008ax and smaller than the eIIb (extended IIb) SN 1993J progenitor. Our data imply that the object identified in pre-explosion Hubble Space Telescope images at the SN location is possibly a companion to the progenitor or a blended source, and not the progenitor star itself, as its radius (~10^13 cm) would be highly inconsistent with constraints from our post-explosion photometric and spectroscopic data

Assessment of insulin resistance by a 13C glucose breath test: a new tool for early diagnosis and follow-up of high-risk patients

<p>Abstract</p> <p>Background/Aims</p> <p>Insulin resistance (IR) plays an important role in the pathogenesis of diabetes and non-alcoholic fatty liver disease (NAFLD). Current methods for insulin resistance detection are cumbersome, or not sensitive enough for early detection and follow-up. The BreathID^®system can continuously analyse breath samples in real-time at the point-of-care. Here we determined the efficacy of the BreathID^®using the ¹³C-Glucose breath test (GBT) for evaluation of insulin resistance.</p> <p>Methods</p> <p>Twenty healthy volunteers were orally administered 75 mg of ¹³C-glucose 1-¹³C. An oral glucose tolerance test (OGTT) was performed immediately; followed by serum glucose and insulin level determinations using GBT. GBT and OGTT were repeated following exercise, which alters insulin resistance levels.</p> <p>Results</p> <p>Within-subject correlations of GBT parameters with serum glucose and serum insulin levels were high. Before and after exercise, between-subjects correlations were high between the relative insulin levels and the % dose recoveries at 90 min (PDR 90), and the cumulative PDRs at 60 min (CPDR 60). Pairwise correlations were identified between pre-exercise Homeostasis Model Assessment (HOMA) IR at 90 min and PDR 90; HOMA B (for beta cell function) 120 and CPDR 30; HOMA IR 60 and peak time post-exercise; and HOMA B 150 with PDR 150.</p> <p>Conclusions</p> <p>The non-invasive real-time BreathID^®GBT reliably assesses changes in liver glucose metabolism, and the degree of insulin resistance. It may serve as a non-invasive tool for early diagnosis and follow up of patients in high-risk groups.</p

Strong Ultraviolet Pulse From a Newborn Type Ia Supernova

Type Ia supernovae are destructive explosions of carbon oxygen white dwarfs. Although they are used empirically to measure cosmological distances, the nature of their progenitors remains mysterious, One of the leading progenitor models, called the single degenerate channel, hypothesizes that a white dwarf accretes matter from a companion star and the resulting increase in its central pressure and temperature ignites thermonuclear explosion. Here we report observations of strong but declining ultraviolet emission from a Type Ia supernova within four days of its explosion. This emission is consistent with theoretical expectations of collision between material ejected by the supernova and a companion star, and therefore provides evidence that some Type Ia supernovae arise from the single degenerate channel.Comment: Accepted for publication on the 21 May 2015 issue of Natur

Type II Supernova Energetics and Comparison of Light Curves to Shock-cooling Models

During the first few days after explosion, Type II supernovae (SNe) are dominated by relatively simple physics. Theoretical predictions regarding early-time SN light curves in the ultraviolet (UV) and optical bands are thus quite robust. We present, for the first time, a sample of 57 R-band SN II light curves that are well-monitored during their rise, with \gt 5 detections during the first 10 days after discovery, and a well-constrained time of explosion to within 1-3 days. We show that the energy per unit mass (E/M) can be deduced to roughly a factor of five by comparing early-time optical data to the 2011 model of Rabinak & Waxman, while the progenitor radius cannot be determined based on R-band data alone. We find that SN II explosion energies span a range of E/M = (0.2-20) × 1051 erg/(10 {M}☉ ), and have a mean energy per unit mass of =0.85× {10}51 erg/(10 {M}☉ ), corrected for Malmquist bias. Assuming a small spread in progenitor masses, this indicates a large intrinsic diversity in explosion energy. Moreover, E/M is positively correlated with the amount of 56Ni produced in the explosion, as predicted by some recent models of core-collapse SNe. We further present several empirical correlations. The peak magnitude is correlated with the decline rate ({{∆ }}{m}15), the decline rate is weakly correlated with the rise time, and the rise time is not significantly correlated with the peak magnitude. Faster declining SNe are more luminous and have longer rise times. This limits the possible power sources for such events

The economics of software distribution over the Internet revisited

Research on the information economy has been based on the assumption that production of software involves low, or even zero, marginal costs. This paper examines this assumption. It argues that the act of driving Internet traffic to an Internet server is an act of distribution and that costs associated with it are actually software production and distribution costs. It suggests a generalized model, the Internet distribution chain, using which the variable and marginal cost of production and distribution can be revealed. Using this model the paper will show that marginal costs associated with the production and distribution of software actually resemble those of traditional products

Enhancing the plasticity, proper function and efficient use of energy of the Sun, genes and microtubules using variability

Backgournd Variability is essential for the efficient functioning of multiple systems in nature. Systems do not function according to rigid rules, easy to determine. The reasons for such behaviour directly affect the design and efficient energy utilization. Aim and methods In this paper, we review the relevant studies and present examples of variabilities in the functions of the Sun, genes and microtubules (MTs) and discuss the potential implications of these variabilities to their operations. Results Variability characterizes multiple systems in nature and is mandatory for their proper function. Variability can also be detercted in quantum effects in physics and several biological systems. Loss of the inherent variability of systems leads to less efficient energy usage and is associated with their malfunction. Variability is an energy‐dependent effect, and examples of the potential importance of electromagnetic energy in the behaviours of the Sun, genes, MTs and quantum effects are provided. Methods are presented for increasing the degrees of the variability of systems for improving their efficiency of energy usage, correcting malfunctioning systems that lost their variability, and improving their function. Summary The collaborative efforts of multiple disciplines in science, along with notions based on literature, philosophy, and industry‐evolving concepts, set up variability‐based signatures for progressing systems. Uses of these methods are demonstrated in therapeutic interventions and designing daily‐use devices

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases