Simulating a Maxwellian plasma using an electron beam ion trap

We describe a technique for producing a Maxwell–Boltzmann electron energy distribution using an electron beam ion trap (EBIT). The technique was implemented on the Lawrence Livermore EBIT to simulate Maxwellian plasmas. We discuss technical and experimental issues related to these simulations. To verify the fidelity of the quasi-Maxwellian, we have measured line emission due to dielectronic recombination (DR) and electron impact excitation (EIE) of heliumlike neon, magnesium, and argon for a range of simulated electron temperatures. The ratio of DR to EIE lines in heliumlike ions is a well understood electron temperature diagnostic. The spectroscopically inferred quasi-Maxwellian temperatures are in excellent agreement with the simulated temperatures

Measurement and interpretation of the polarization of the x-ray line emission of heliumlike Fe XXV excited by an electron beam

The linear polarization of the 1s2p 1P1→1s2 1S0 resonance line, the 1s2p 3P1,2→1s2 1S0 intercombination lines, and the 1s2s 3S1→1s2 1S0 forbidden line was measured in heliumlike Fe XXV excited near threshold by a monoenergetic electron beam. The measurement was carried out with a high-resolution x-ray spectrometer employing a set of two analyzing crystals that acted as polarizers by selectively reflecting the individual polarization components. A value of +0.56-0.08+0.17 was determined for the polarization of the 1P1 line, -0.53-0.02+0.05 for the 3P2 line, -0.22-0.02+0.05 for the 3P1 line, and -0.076-0.007+0.007 for the 3S1 line. The measurements were compared with results from a relativistic distorted-wave calculation, which was carried out for a number of mid-Z heliumlike ions (Mg10+–Kr34+), and good agreement was found. By contrast, disagreement was noted with predictions based on Coulomb-Born calculations, allowing us to distinguish between theoretical approaches

Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection

There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). The result is that the efficacy and gaps among the available approaches are opaque, inhibiting end users from making informed network security decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four market-leading malware detection tools to assist an organization with two primary questions: (Q1) To what extent do ML-based tools accurately classify never-before-seen files without sacrificing detection ability on known files? (Q2) Is it worth purchasing a network-level malware detector to complement host-based detection? We tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) including over 400 zero-day malware, and tested with a variety of file types and protocols for delivery. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of a recent cost-benefit evaluation procedure by Iannaconne & Bridges that incorporates all the above metrics into a single quantifiable cost. While the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool may still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files -- 37% of malware tested, including all polyglot files, were undetected.Comment: Includes Actionable Takeaways for SOC

Mono- and bi-functional arenethiols as surfactants for gold nanoparticles: synthesis and characterization

Stable gold nanoparticles stabilized by different mono and bi-functional arenethiols, namely, benzylthiol and 1,4-benzenedimethanethiol, have been prepared by using a modified Brust's two-phase synthesis. The size, shape, and crystalline structure of the gold nanoparticles have been determined by high-resolution electron microscopy and full-pattern X-ray powder diffraction analyses. Nanocrystals diameters have been tuned in the range 2 ÷ 9 nm by a proper variation of Au/S molar ratio. The chemical composition of gold nanoparticles and their interaction with thiols have been investigated by X-ray photoelectron spectroscopy. In particular, the formation of networks has been observed with interconnected gold nanoparticles containing 1,4-benzenedimethanethiol as ligand

Genome-wide association study identifies Sjögren’s risk loci with functional implications in immune and glandular cells

Sjögren’s disease is a complex autoimmune disease with twelve established susceptibility loci. This genome-wide association study (GWAS) identifies ten novel genome-wide significant (GWS) regions in Sjögren’s cases of European ancestry: CD247, NAB1, PTTG1-MIR146A, PRDM1-ATG5, TNFAIP3, XKR6, MAPT-CRHR1, RPTOR-CHMP6-BAIAP6, TYK2, SYNGR1. Polygenic risk scores yield predictability (AUROC = 0.71) and relative risk of 12.08. Interrogation of bioinformatics databases refine the associations, define local regulatory networks of GWS SNPs from the 95% credible set, and expand the implicated gene list to >40. Many GWS SNPs are eQTLs for genes within topologically associated domains in immune cells and/or eQTLs in the main target tissue, salivary glands.Research reported in this publication was supported by the National Institutes of Health (NIH): R01AR073855 (C.J.L.), R01AR065953 (C.J.L.), R01AR074310 (A.D.F.), P50AR060804 (K.L.S.), R01AR050782 (K.L.S), R01DE018209 (K.L.S.), R33AR076803 (I.A.), R21AR079089 (I.A.); NIDCR Sjögren’s Syndrome Clinic and Salivary Disorders Unit were supported by NIDCR Division of Intramural Research at the National Institutes of Health funds - Z01-DE000704 (B.W.); Birmingham NIHR Biomedical Research Centre (S.J.B.); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2155 – Projektnummer 390874280 (T.W.); Research Council of Norway (Oslo, Norway) – Grant 240421 (TR.R.), 316120 (M.W-H.); Western Norway Regional Health Authority (Helse Vest) – 911807, 912043 (R.O.); Swedish Research Council for Medicine and Health (L.R., G.N., M.W-H.); Swedish Rheumatism Association (L.R., G.N., M.W-H.); King Gustav V’s 80-year Foundation (G.N.); Swedish Society of Medicine (L.R., G.N., M.W-H.); Swedish Cancer Society (E.B.); Sjögren’s Syndrome Foundation (K.L.S.); Phileona Foundation (K.L.S.). The Stockholm County Council (M.W-H.); The Swedish Twin Registry is managed through the Swedish Research Council - Grant 2017-000641. The French ASSESS (Atteinte Systémique et Evolution des patients atteints de Syndrome de Sjögren primitive) was sponsored by Assistance Publique-Hôpitaux de Paris (Ministry of Health, PHRC 2006 P060228) and the French society of Rheumatology (X.M.).publishedVersio

Tempo and Pattern of Avian Brain Size Evolution

Relative brain sizes in birds can rival those of primates, but large-scale patterns and drivers of avian brain evolution remain elusive. Here, we explore the evolution of the fundamental brain-body scaling relationship across the origin and evolution of birds. Using a comprehensive dataset sampling> 2,000 modern birds, fossil birds, and theropod dinosaurs, we infer patterns of brain-body co-variation in deep time. Our study confirms that no significant increase in relative brain size accompanied the trend toward miniaturization or evolution of flight during the theropod-bird transition. Critically, however, theropods and basal birds show weaker integration between brain size and body size, allowing for rapid changes in the brain-body relationship that set the stage for dramatic shifts in early crown birds. We infer that major shifts occurred rapidly in the aftermath of the Cretaceous-Paleogene mass extinction within Neoaves, in which multiple clades achieved higher relative brain sizes because of a reduction in body size. Parrots and corvids achieved the largest brains observed in birds via markedly different patterns. Parrots primarily reduced their body size, whereas corvids increased body and brain size simultaneously (with rates of brain size evolution outpacing rates of body size evolution). Collectively, these patterns suggest that an early adaptive radiation in brain size laid the foundation for subsequent selection and stabilization