4-(4-Chlorophenyl)-4,5-dihydro-1H-1,2,4-triazole-5-thione

The title compound, 4-(4-chlorophenyl)-4,5-dihydro-1H-1,2,4-triazole-5-thione (1), was synthesized by a hetero-cyclization reaction of 4-chlorophenyl isothiocyanate and formic hydrazide. Compound 1 was characterized by a single-crystal X-ray structure determination as well as 1H and 13C{1H} NMR, IR, and UV spectroscopy, and microelemental analysis. X-ray crystallography on 1 confirms the molecule exists as the thione tautomer and shows the five-membered ring to be planar and to form a dihedral angle of 82.70(5)° with the appended chlorophenyl ring, indicating an almost orthogonal relationship. In the molecular packing, supramolecular dimers are formed via thioamide-N–H⋯S(thione) hydrogen bonds and these are connected by C=S⋯π(triazolyl) and C-Cl⋯π(triazolyl) interactions, leading to a three-dimensional architectur

Fisher profiles and perceptions of sea turtle-fishery interactions: case study of East Coast Peninsular Malaysia

The paper focuses on coastal fisheries, particularly examining sea turtle-fishery interactions and determining the socioeconomic profile and perception of local fishers about sea turtle issues along the East Coast of Peninsular Malaysia.Turtle fisheries, Nature conservation, Coastal fisheries, Man-induced effects, ISEW, Malaysia, Malaya, Pahang, Malaysia, Malaya, Kelantan, Malaysia, Malaya, Terengganu,

Unsupervised Fiber Bundles Registration using Weighted Measures Geometric Demons

International audienceBrain image registration aims at reducing anatomical variability across subjects to create a common space for group analysis. Multi-modal approaches intend to minimize cortex shape variations along with internal structures, such as fiber bundles. A di ficulty is that it requires a prior identi fication of these structures, which remains a challenging task in the absence of a complete reference atlas. We propose an extension of the log-Geometric Demons for jointly registering images and fi ber bundles without the need of point or ber correspondences. By representing fi ber bundles as Weighted Measures we can register subjects with di fferent numbers of fiber bundles. The ef ficacy of our algorithm is demonstrated by registering simultaneously T1 images and between 37 and 88 ber bundles depending on each of the ten subject used. We compare results with a multi-modal T1 + Fractional Anisotropy (FA) and a tensor-based registration algorithms and obtain superior performance with our approach

Memory consolidation in the cerebellar cortex

Several forms of learning, including classical conditioning of the eyeblink, depend upon the cerebellum. In examining mechanisms of eyeblink conditioning in rabbits, reversible inactivations of the control circuitry have begun to dissociate aspects of cerebellar cortical and nuclear function in memory consolidation. It was previously shown that post-training cerebellar cortical, but not nuclear, inactivations with the GABA(A) agonist muscimol prevented consolidation but these findings left open the question as to how final memory storage was partitioned across cortical and nuclear levels. Memory consolidation might be essentially cortical and directly disturbed by actions of the muscimol, or it might be nuclear, and sensitive to the raised excitability of the nuclear neurons following the loss of cortical inhibition. To resolve this question, we simultaneously inactivated cerebellar cortical lobule HVI and the anterior interpositus nucleus of rabbits during the post-training period, so protecting the nuclei from disinhibitory effects of cortical inactivation. Consolidation was impaired by these simultaneous inactivations. Because direct application of muscimol to the nuclei alone has no impact upon consolidation, we can conclude that post-training, consolidation processes and memory storage for eyeblink conditioning have critical cerebellar cortical components. The findings are consistent with a recent model that suggests the distribution of learning-related plasticity across cortical and nuclear levels is task-dependent. There can be transfer to nuclear or brainstem levels for control of high-frequency responses but learning with lower frequency response components, such as in eyeblink conditioning, remains mainly dependent upon cortical memory storage

EIC Climate Change Technology Conference 2013 1 STATISTICAL MODELING OF EXTREME RAINFALL PROCESSES IN THE CONTEXT OF CLIMATE CHANGE CCTC 2013 Paper Number 1569696427

Abstract This paper proposes a Statistical Downscaling (SD) approach to link climate predictors provided by General Circulation Models (GCMs) to extreme rainfalls at a local site. More specifically, the suggested approach is based on the combination of a spatial downscaling method for linking the large-scale predictors to extreme rainfall and a temporal downscaling method for describing the relationships between daily and sub-daily annual maximum precipitations (AMPs). Results of the numerical application to observed AMPs (Quebec, Canada) and climate change scenarios have indicated that it is feasible to link large-scale predictors given by GCMsimulation outputs with daily and sub-daily AMPs at a local site. EIC Climate Change Technology Conference 2013

Liquid-to-liquid phase transition in pancake vortex systems

We study the thermodynamics of a model of pancake vortices in layered superconductors. The model is based on the effective pair potential for the pancake vortices derived from the London approximation of a version of the Lawrence-Doniach model which is valid for extreme type-II superconductors. Using the hypernetted-chain (HNC) approximation, we find that there is a temperature below which multiple solutions to the HNC equations exist. By explicitly evaluating the free energy for each solution we find that the system undergoes a first-order transition between two vortex liquid phases. The low-temperature phase has larger correlations along the field direction than the high-temperature phase. We discuss the possible relation of this phase transition to the liquid-to-liquid phase transition recently observed in Y-Ba-Cu-O superconductors in high magnetic fields in the presence of disorder.Comment: 7 pages, 6 figure

Parquet Graph Resummation Method for Vortex Liquids

We present in detail a nonperturbative method for vortex liquid systems. This method is based on the resummation of an infinite subset of Feynman diagrams, the so-called parquet graphs, contributing to the four-point vertex function of the Ginzburg-Landau model for a superconductor in a magnetic field. We derive a set of coupled integral equations, the parquet equations, governing the structure factor of the two-dimensional vortex liquid system with and without random impurities and the three-dimensional system in the absence of disorder. For the pure two-dimensional system, we simplify the parquet equations considerably and obtain one simple equation for the structure factor. In two dimensions, we solve the parquet equations numerically and find growing translational order characterized by a length scale $R_c$ as the temperature is lowered. The temperature dependence of $R_c$ is obtained in both pure and weakly disordered cases. The effect of disorder appears as a smooth decrease of $R_c$ as the strength of disorder increases.Comment: 15 pages, 12 PostScript figures, uses multicols.sty and epsf.st

Joint T1 and Brain Fiber Log-Demons Registration Using Currents to Model Geometry

International audienceWe present an extension of the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. Our algorithm works in the log-domain space, so that one can efficiently compute the deformation field of the geometry. We represent the shape of objects of interest in the space of currents which is sensitive to both location and geometric structure of objects. Currents provides a distance between geometric structures that can be defined without specifying explicit point-to-point correspondences. We demonstrate this framework by registering simultaneously T1 images and 65 fiber bundles consistently extracted in 12 subjects and compare it against non-linear T1, tensor, and multi-modal T1+ Fractional Anisotropy (FA) registration algorithms. Results show the superiority of the Log-domain Geometric Demons over their purely iconic counterparts