Learning to detect spyware using end user license agreements

The amount of software that hosts spyware has increased dramatically. To avoid legal repercussions, the vendors need to inform users about inclusion of spyware via end user license agreements (EULAs) during the installation of an application. However, this information is intentionally written in a way that is hard for users to comprehend. We investigate how to automatically discriminate between legitimate software and spyware associated software by mining EULAs. For this purpose, we compile a data set consisting of 996 EULAs out of which 9.6% are associated to spyware. We compare the performance of 17 learning algorithms with that of a baseline algorithm on two data sets based on a bag-of-words and a meta data model. The majority of learning algorithms significantly outperform the baseline regardless of which data representation is used. However, a non-parametric test indicates that bag-of-words is more suitable than the meta model. Our conclusion is that automatic EULA classification can be applied to assist users in making informed decisions about whether to install an application without having read the EULA. We therefore outline the design of a spyware prevention tool and suggest how to select suitable learning algorithms for the tool by using a multi-criteria evaluation approach.     

Untargeted In Silico Compound Classification—A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes

In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies.     

Technological Applications of Zeolites in Catalysis

Abstract

Zeolites have been used as catalysts in industry since the early 1960s. The great majority of commercial applications employ one of three zeolite types: zeolite Y (faujasite); mordenite; ZSM-5. By far the largest use of zeolites is in catalytic cracking, and to a lesser extent in hydrocracking. Table 1 presents some data showing the commercial importance of this field [1]. The data are for United States refineries only and must be multiplied by a factor to arrive at worldwide use. Better than 90% of free-world cracking units now use zeolite catalysts. For many years it had been assumed that crystalline aluminosilicates with their uniform pore structure would make inferior catalysts to amorphous silica-slumina with a rather wide pore size distribution. The tremendous acid activity of hydrogen zeolites also was not recognized. Rabo and co-workers [2] showed at the 2nd International Congress on Catalysis that hydrogen exchanged faujasites possessed good isomerization ability, but commercial application in catalytic cracking became feasible only after Plank and Rosinsky at Socony-Mobil Oil Co. succeeded in stabilizing zeolite Y against steam and heat sintering by exchange with rare earth ions and by separating zeolite crystallites by incorporating them into a silica-alumina matrix which provided a heat reservoir along with some synergistic cracking effects. Modern cracking catalysts comprise 10–40% rare earth exchanged H-Y zeolite dispersed in a matrix of silica-alumina, semisynthetic clay, or natural clay.     

Discovery, structure-activity relationship studies, and crystal structure of nonpeptide inhibitors bound to the Shank3 PDZ domain

Shank is the central scaffolding protein of the postsynaptic density (PSD) protein complex found in cells of the central nervous system. Cellular studies indicate a prominent role of the protein in the organization of the PSD, in the development of neuronal morphology, in neuronal signaling, and in synaptic plasticity, thus linking Shank functions to the molecular basis of learning and memory. Mutations in the Shank gene have been found in several neuronal disorders including mental retardation, typical autism, and Asperger syndrome. Shank is linked to the PSD complex via its PDZ domain that binds to the C‐terminus of guanylate‐kinase‐associated protein (GKAP). Here, small‐molecule inhibitors of Shank3 PDZ domain are developed. A fluorescence polarization assay based on an identified high‐affinity peptide is established, and tetrahydroquinoline carboxylates are identified as inhibitors of this protein–protein interaction. Chemical synthesis via a hetero‐Diels–Alder strategy is employed for hit optimization, and structure–activity relationship studies are performed. Best hits possess K_i values in the 10 μM range, and binding to the PDZ domain is confirmed by ¹H,¹⁵N HSQC NMR experiments. One of the hits crystallizes with the Shank3 PDZ domain. The structure, analyzed at a resolution of 1.85 Å, reveals details of the binding mode. Finally, binding to PDZ domains of PSD‐95, syntrophin, and DVL3 was studied using ¹H,¹⁵N HSQC NMR spectroscopy.     

Isotope-Guided Metabolomics Reveals Divergent Incorporation of Valine into Different Flavor Precursor Classes in Chives

Plants of the genus Allium such as chives, onions or garlic produce S-alk(en)yl cysteine sulfoxides as flavor precursors. Two major representatives are S-propenyl cysteine sulfoxide (isoalliin) and S-propyl cysteine sulfoxide (propiin), which only differ by a double bond in the C₃ side chain. The propenyl group of isoalliin is derived from the amino acid valine, but the source of the propyl group of propiin remains unclear. Here, we present an untargeted metabolomics approach in seedlings of chives (Allium schoenoprasum) to track mass features containing sulfur and/or ¹³C from labeling experiments with valine-¹³C₅ guided by their isotope signatures. Our data show that propiin and related propyl-bearing metabolites incorporate carbon derived from valine-¹³C₅, but to a much lesser extent than isoalliin and related propenyl compounds. Our findings provide new insights into the biosynthetic pathways of flavor precursors in Allium species and open new avenues for future untargeted labeling experiments.     

From Microbial Succinic Acid Production to Polybutylene Bio-Succinate Synthesis

A simplified and scalable one-pot process for the anaerobic production of succinic acid using a metabolically engineered Corynebacterium glutamicum strain is demonstrated. With targeted bioprocess optimization, succinic acid titer of 78 g L⁻¹ and yield of 1.41 mol_SAmol_GLC⁻¹ were achieved. Succinic acid was recovered from the neutral fermentation broth by electrochemically induced crystallization and applied for polybutylene bio-succinate synthesis using a biocompatible zinc catalyst. Except for a slight color change, the final biopolymer was comparable to the polymer from commercial precursors.     

Oxidative coupling of methane at 600 °C on CaNiK oxide catalysts

We are herewith reporting new data which modifies, explains and extends our earlier work on this subject. Previous observations of low CH₄ conversion and high selectivity to C₂ hydrocarbons were erroneous due to the formation of bulk CaCO₃ in the catalyst. The carbonate was detected by powder X-ray diffraction and was shown to accumulate during the reaction and decompose during regeneration. Catalytic runs which incorporated an internal standard revealed a deficit in the C balance consistent with carbonate formation. Actual CH₄ conversions were 20% with 15% selectivity to hydrocarbons. The effect of steam in promoting coupling over combustion was affirmed.     

低场MRI原位研究离子液体合成及相态变化

离子液体作为一个平台化合物已经在诸多领域得到广泛应用,但离子液体的生产成本仍然是限制其进一步工业化推广的瓶颈之一。深入研究反应动力学、反应传递耦合规律对优化离子液体合成工艺/关键设备,降低大规模离子液体制备成本至关重要。本研究用一个原位低场磁共振成像(MRI)设备实时监测ILs合成过程中的反应动力学和相态变化。通过建立双组分分析模型,即1H低场核磁共振(LF-NMR)弛豫法对反应体系中的各个组分进行定量分析,并采集MRI图像记录相位分离和倒置过程,系统考察了卤代烷烃的链长、卤素种类、搅拌速度、反应温度等对咪唑类离子液体反应动力学的影响规律。对溴丁烷、甲基咪唑合成溴代1-丁基-3-甲基咪唑类离子液体合成过程中相态变化进行了实时动态观测。本研究为检测离子液体反应过程及有机溶剂中离子液体的残余量提供一种快速、便利、无损的检测方法,有望利用低场MRI对离子液体系统中传质和反应动力学的结合研究做进一步探索。     

Tests against stationary and explosive alternatives in vector autoregressive models

Abstract. The article proposes new tests for the number of unit roots in vector autoregressive models based on the eigenvalues of the companion matrix. Both stationary and explosive alternatives are considered. The limiting distributions of test statistics depend only on the number of unit roots. Size and power are investigated, and it is found that the new test against some stationary alternatives compares favourably with the widely used likelihood ratio test for the cointegrating rank. The powers are prominently higher against explosive than against stationary alternatives. Some empirical examples are provided to show how to use the new tests with real data.