A methodology for radiolabeling of the endocannabinoid 2-arachidonoylglycerol (2-AG)

The metabolic intermediate and endocannabinoid signaling lipid 2-arachidonoylglycerol (2-AG) has not been readily labeled, primarily because of its instability toward rearrangement. We now detail a synthetic method that easily gives tritiated 2-AG from [5,6,8,9,11,12,14,15-(3)H(N)]arachidonic acid in two steps. We utilized a short chain 1,3-diacylglycerol and proceeded through the "structured lipid" [5',6',8',9',11',12',14',15'-(3)H(N)]2-arachidonoyl-1,3-dibutyrylglycerol, a triacylglycerol that was conveniently deprotected in ethanol with acrylic beads containing Candida antarctica lipase B to give [5',6',8',9',11',12',14',15'-(3)H(N)]2-arachidonoylglycerol ([(3)H]2-AG). The flash chromatographic separation necessary to isolate the labeled 2-acylglycerol [(3)H]2-AG resulted in only 4% of the rearrangement byproducts that have been a particular problem with previous methodologies. This reliable "kit" method to prepare the radiolabeled endocannabinoid as needed gave tritiated 2-arachidonoylglycerol [(3)H]2-AG with a specific activity of 200 Ci/mmol for enzyme assays, metabolic studies, and tissue imaging. It has been run on unlabeled materials on over 10 mg scales and should be generally applicable to other 2-acylglycerols.     

Stochastic Description of Traffic Flow

We propose a traffic model based on microscopic stochastic dynamics. We built a Markov chain equipped with an Arrhenius interaction law. The resulting stochastic process is comprised of both spin-flip and spin-exchange dynamics which models vehicles exiting, entering and interacting in a two-dimensional lattice environment corresponding to a multi-lane highway. The process is further equipped with a novel look-ahead type, anisotropic interaction potential which allows drivers/vehicles to ascertain local fluctuations and advance to new cells forward or sideways. The resulting vehicular traffic model is simulated via kinetic Monte Carlo and examined under both, typical and extreme traffic flow scenarios. The model is shown to correctly predict both qualitative as well as quantitative traffic observables for any highway geometry. Furthermore it also captures interesting multi-scale phenomena in traffic flows after a simulated accident which lead to oscillatory, dissipating, traffic waves with different periods per lane.     

Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F₂-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.     

Total Synthesis of Dragocins A−C through Electrochemical Cyclization

The first total synthesis of dragocins A−C, remarkable natural products containing an unusual C4’ oxidized ribose architecture bridged by a polyhydroxylated pyrrolidine, is presented through a route featuring a number of uncommon maneuvers. Several generations towards the target molecules are presented, including the spectacular failure of a key C−H oxidation on a late-stage intermediate. The final route features rapid, stereocontrolled access to a densely functionalized pyrrolidine and an unprecedented diastereoselective oxidative electrochemical cyclization to forge the hallmark 9-membered ring. Preliminary studies suggest this electrochemical oxidation protocol is generally useful.     

A Mathematical Radiobiological Model (MRM) to Predict Complex DNA Damage and Cell Survival for Ionizing Particle Radiations of Varying Quality

Predicting radiobiological effects is important in different areas of basic or clinical applications using ionizing radiation (IR); for example, towards optimizing radiation protection or radiation therapy protocols. In this case, we utilized as a basis the ‘MultiScale Approach (MSA)’ model and developed an integrated mathematical radiobiological model (MRM) with several modifications and improvements. Based on this new adaptation of the MSA model, we have predicted cell-specific levels of initial complex DNA damage and cell survival for irradiation with ¹¹Β, ¹²C, ¹⁴Ν, ¹⁶Ο, ²⁰Νe, ⁴⁰Αr, ²⁸Si and ⁵⁶Fe ions by using only three input parameters (particle’s LET and two cell-specific parameters: the cross sectional area of each cell nucleus and its genome size). The model-predicted survival curves are in good agreement with the experimental ones. The particle Relative Biological Effectiveness (RBE) and Oxygen Enhancement Ratio (OER) are also calculated in a very satisfactory way. The proposed integrated MRM model (within current limitations) can be a useful tool for the assessment of radiation biological damage for ions used in hadron-beam radiation therapy or radiation protection purposes.     

Schlenk's Legacy—Methyllithium Put under Close Scrutiny

Commercially available stock solutions of organolithium reagents are well-implemented tools in organic and organometallic chemistry. However, such solutions are inherently contaminated with lithium halide salts, which can complicate certain synthesis protocols and purification processes. Here, we report the isolation of chloride-free methyllithium employing K[N(SiMe₃)₂] as a halide-trapping reagent. The influence of distinct LiCl contaminations on the ⁷Li-NMR chemical shift is examined and their quantification demonstrated. The structural parameters of new chloride-free monomeric methyllithium complex [(Me₃TACN)LiCH₃], ligated by an azacrown ether, are assessed by comparison with a halide-contaminated variant and monomeric lithium chloride [(Me₃TACN)LiCl], further emphasizing the effect of halide impurities.