Workmen’s Compensation—Assertion of Claims—Time Limitation

Workmen’s Compensation—Assertion of Claims—Time Limitation (Rutledge v. Sandlin

Procedures for Suppressing Illegally Seized Evidence: The Exclusionary Rule

Procedures for Suppressing Illegally Seized Evidence: The Exclusionary Rul

Feature Model Differences

International audienceFeature models are a widespread means to represent commonality and variability in software product lines. As is the case for other kinds of models, computing and managing feature model differences is useful in various real-world situations. In this paper, we propose a set of novel differencing techniques that combine syntactic and semantic mechanisms, and automatically produce meaningful differences. Practitioners can exploit our results in various ways: to understand, manipulate, visualize and reason about differences. They can also combine them with existing feature model composition and decomposition operators. The proposed automations rely on satisfiability algorithms. They come with a dedicated language and a comprehensive environment. We illustrate and evaluate the practical usage of our techniques through a case study dealing with a configurable component framework

Empirical assessment of generating adversarial configurations for software product lines

Software product line (SPL) engineering allows the derivation of products tailored to stakeholders’ needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-app configurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs

A Conserved Region of Human Vitamin K-dependent Carboxylase between Residues 393 and 404 Is Important for Its Interaction with the Glutamate Substrate

Certain individuals with combined deficiencies of vitamin K-dependent proteins have a mutation, L394R, in their gamma-glutamyl carboxylase causing impaired glutamate binding. The sequence surrounding Leu394 is similar in all known carboxylases, suggesting that the region is functionally important. To test this hypothesis we made the following mutant enzymes: W390A, Y395A, S398A, W399A, and H404A. We purified the enzymes and corrected the activity measurements for active enzyme concentration. Carboxylases W390A, S398A, and H404A had activities similar to that of wild type; however, Y395A and W399A had lower activities than did wild type. In the following descriptions we include our previously reported results for L394R. Kinetic studies with the substrate FLEEL, revealed Km values of 0.5 (wild type), 6.5 (L394R), 15 (Y395A), and 24 (W399A) mm. The kcat values relative to wild type were 51% (L394R), 1% (Y395A), and 2% (W399A). The kcat/Km values were 24-fold (L394R) and >2000-fold lower for Y395A and W399A than for wild-type carboxylase. Inhibition of FLEEL carboxylation by the competitive inhibitor, Boc-mEEV, gave Ki values of 0.013 (wild type), 1.4 (L394R), 2.1 (Y395A), and >5 (W399A) mm. The Y395A propeptide affinity was similar to that of wild type, but those of L394R and W399A were 16-22-fold less than that of wild type. Results of kinetic studies with a propeptide-containing substrate were consistent with results of propeptide binding and FLEEL kinetics. Although propeptide and vitamin K binding in some mutants were affected, our data provide compelling evidence that glutamate recognition is the primary function of the conserved region around Leu394

Nematic liquid crystal alignment on chemical patterns

Patterned Self-Assembled Monolayers (SAMs) promoting both homeotropic and planar degenerate alignment of 6CB and 9CB in their nematic phase, were created using microcontact printing of functionalised organothiols on gold films. The effects of a range of different pattern geometries and sizes were investigated, including stripes, circles and checkerboards. EvanescentWave Ellipsometry was used to study the orientation of the liquid crystal (LC) on these patterned surfaces during the isotropic-nematic phase transition. Pretransitional growth of a homeotropic layer was observed on 1 ¹m homeotropic aligning stripes, followed by a homeotropic mono-domain state prior to the bulk phase transition. Accompanying Monte-Carlo simulations of LCs aligned on nano-patterned surfaces were also performed. These simulations also showed the presence of the homeotropic mono-domain state prior to the transition.</p

The Effect of Interface Texture on Exchange Biasing in Ni80Fe20/Ir20Mn80System

Exchange-biasing phenomenon can induce an evident unidirectional hysteresis loop shift by spin coupling effect in the ferromagnetic (FM)/antiferromagnetic (AFM) interface which can be applied in magnetoresistance random access memory (MRAM) and recording-head applications. However, magnetic properties are the most important to AFM texturing. In this work, top-configuration exchange-biasing NiFe/IrMn(x Å) systems have been investigated with three different conditions. From the high-resolution cross-sectional transmission electron microscopy (HR X-TEM) and X-ray diffraction results, we conclude that the IrMn (111) texture plays an important role in exchange-biasing field (Hex) and interfacial exchange energy (Jk).HexandJktend to saturate when the IrMn thickness increases. Moreover, the coercivity (Hc) dependence on IrMn thickness is explained based on the coupling or decoupling effect between the spins of the NiFe and IrMn layers near the NiFe/IrMn interface. In this work, the optimal values forHexandJkare 115 Oe and 0.062 erg/cm2, respectively

Metabotropic glutamate receptors in GtoPdb v.2023.1

Metabotropic glutamate (mGlu) receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Metabotropic Glutamate Receptors [351]) are a family of G protein-coupled receptors activated by the neurotransmitter glutamate [140]. The mGlu family is composed of eight members (named mGlu1 to mGlu8) which are divided in three groups based on similarities of agonist pharmacology, primary sequence and G protein coupling to effector: Group-I (mGlu1 and mGlu5), Group-II (mGlu2 and mGlu3) and Group-III (mGlu4, mGlu6, mGlu7 and mGlu8) (see Further reading).Structurally, mGlu are composed of three juxtaposed domains: a core G protein-activating seven-transmembrane domain (TM), common to all GPCRs, is linked via a rigid cysteine-rich domain (CRD) to the Venus Flytrap domain (VFTD), a large bi-lobed extracellular domain where glutamate binds. mGlu form constitutive dimers, cross-linked by a disulfide bridge. The structures of the VFTD of mGlu1, mGlu2, mGlu3, mGlu5 and mGlu7 have been solved [200, 275, 268, 403]. The structure of the 7 transmembrane (TM) domains of both mGlu1 and mGlu5 have been solved, and confirm a general helical organisation similar to that of other GPCRs, although the helices appear more compacted [88, 433, 62]. Recent advances in cryo-electron microscopy have provided structures of full-length mGlu receptor homodimers [217, 191] and heterodimers [91]. Studies have revealed the possible formation of heterodimers between either group-I receptors, or within and between group-II and -III receptors [89]. First characterised in transfected cells, co-localisation and specific pharmacological properties suggest the existence of such heterodimers in the brain [270, 440, 145, 283, 259, 218]. Beyond heteromerisation with other mGlu receptor subtypes, increasing evidence suggests mGlu receptors form heteromers and larger order complexes with class A GPCRs (reviewed in [140]). The endogenous ligands of mGlu are L-glutamic acid, L-serine-O-phosphate, N-acetylaspartylglutamate (NAAG) and L-cysteine sulphinic acid. Group-I mGlu receptors may be activated by 3,5-DHPG and (S)-3HPG [30] and antagonised by (S)-hexylhomoibotenic acid [235]. Group-II mGlu receptors may be activated by LY389795 [269], LY379268 [269], eglumegad [354, 434], DCG-IV and (2R,3R)-APDC [355], and antagonised by eGlu [170] and LY307452 [425, 105]. Group-III mGlu receptors may be activated by L-AP4 and (R,S)-4-PPG [130]. An example of an antagonist selective for mGlu receptors is LY341495, which blocks mGlu2 and mGlu3 at low nanomolar concentrations, mGlu8 at high nanomolar concentrations, and mGlu4, mGlu5, and mGlu7 in the micromolar range [185]. In addition to orthosteric ligands that directly interact with the glutamate recognition site, allosteric modulators that bind within the TM domain have been described. Negative allosteric modulators are listed separately. The positive allosteric modulators most often act as &#8216;potentiators&#8217; of an orthosteric agonist response, without significantly activating the receptor in the absence of agonist

Metabotropic glutamate receptors in GtoPdb v.2021.3

Metabotropic glutamate (mGlu) receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Metabotropic Glutamate Receptors [347]) are a family of G protein-coupled receptors activated by the neurotransmitter glutamate [138]. The mGlu family is composed of eight members (named mGlu1 to mGlu8) which are divided in three groups based on similarities of agonist pharmacology, primary sequence and G protein coupling to effector: Group-I (mGlu1 and mGlu5), Group-II (mGlu2 and mGlu3) and Group-III (mGlu4, mGlu6, mGlu7 and mGlu8) (see Further reading).Structurally, mGlu are composed of three juxtaposed domains: a core G protein-activating seven-transmembrane domain (TM), common to all GPCRs, is linked via a rigid cysteine-rich domain (CRD) to the Venus Flytrap domain (VFTD), a large bi-lobed extracellular domain where glutamate binds. mGlu form constitutive dimers, cross-linked by a disulfide bridge. The structures of the VFTD of mGlu1, mGlu2, mGlu3, mGlu5 and mGlu7 have been solved [198, 271, 264, 399]. The structure of the 7 transmembrane (TM) domains of both mGlu1 and mGlu5 have been solved, and confirm a general helical organization similar to that of other GPCRs, although the helices appear more compacted [87, 429, 61]. Recent advances in cryo-electron microscopy have provided structures of full-length mGlu receptor dimers [189]. Studies have revealed the possible formation of heterodimers between either group-I receptors, or within and between group-II and -III receptors [88]. First well characterized in transfected cells, co-localization and specific pharmacological properties also suggest the existence of such heterodimers in the brain [266].[436, 143, 279]. Beyond heteromerization with other mGlu receptor subtypes, increasing evidence suggests mGlu receptors form heteromers and larger order complexes with class A GPCRs (reviewed in [138]). The endogenous ligands of mGlu are L-glutamic acid, L-serine-O-phosphate, N-acetylaspartylglutamate (NAAG) and L-cysteine sulphinic acid. Group-I mGlu receptors may be activated by 3,5-DHPG and (S)-3HPG [30] and antagonized by (S)-hexylhomoibotenic acid [232]. Group-II mGlu receptors may be activated by LY389795 [265], LY379268 [265], eglumegad [350, 430], DCG-IV and (2R,3R)-APDC [351], and antagonised by eGlu [168] and LY307452 [421, 103]. Group-III mGlu receptors may be activated by L-AP4 and (R,S)-4-PPG [128]. An example of an antagonist selective for mGlu receptors is LY341495, which blocks mGlu2 and mGlu3 at low nanomolar concentrations, mGlu8 at high nanomolar concentrations, and mGlu4, mGlu5, and mGlu7 in the micromolar range [183]. In addition to orthosteric ligands that directly interact with the glutamate recognition site, allosteric modulators that bind within the TM domain have been described. Negative allosteric modulators are listed separately. The positive allosteric modulators most often act as &#8216;potentiators&#8217; of an orthosteric agonist response, without significantly activating the receptor in the absence of agonist