Spit-systems – an overlooked target in hydrocarbon exploration: the Holocene to Recent Skagen Odde, Denmark

Well-constrained depositional models are essential for successful exploration and field development. The Skagen spitsystem offers a unique possibility for the establishment of a depositional model constrained by excellent outcrops, well-defined palaeogeography, good age control and detailed observations on hydrodynamics and morphology of the prograding part of the spit-system. The model offers a supplementary interpretation of shallow marine sandstones to the existing delta and linear shoreface models. The sand-dominated Skagen spit-system is c. 22 km long, 4 km wide and up to 35 m thick, with a sand volume of c. 2.2 km3. If filled with oil, this system would contain 0.6 km3 corresponding to 3.8 x 109 barrels assuming a porosity of 30% and an oil saturation of 90%. This is comparable in size with the largest Danish oil field (the Dan field), in the North Sea. Reservoir models for isolated linear ‘offshore’ sandstone bodies have been controversial for many years. Their size and internal indications of palaeocurrent directions are similar to those of the spit-system model, and this model may therefore be applicable for some of these bodies

Bridging the translational gap:adenosine as a modulator of neuropathic pain in preclinical models and humans

Objectives: This review aims to analyse the published data on preclinical and human experimental and clinical adenosine modulation for pain management. We summarise the translatability of the adenosine pathway for further drug development and aim to reveal subgroups of pain patients that could benefit from targeting the pathway. Content: Chronic pain patients suffer from inadequate treatment options and drug development is generally impaired by the low translatability of preclinical pain models. Therefore, validating the predictability of drug targets is of high importance. Modulation of the endogenous neurotransmitter adenosine gained significant traction in the early 2000s but the drug development efforts were later abandoned. With the emergence of new drug modalities, there is a renewed interest in adenosine modulation in pain management. In both preclinical, human experimental and clinical research, enhancing adenosine signalling through the adenosine receptors, has shown therapeutic promise. A special focus has been on the A 1 and A 3 receptors both of which have shown great promise and predictive validity in neuropathic pain conditions. Summary: Adenosine modulation shows predictive validity across preclinical, human experimental and clinical investigations. The most compelling evidence is in the field of neuropathic pain, where adenosine has been found to alleviate hyperexcitability and has the potential to be disease-modifying. Outlook: Adenosine modulation show therapeutic potential in neuropathic pain if selective and safe drugs can be developed. New drug modalities such as RNA therapeutics and cell therapies may provide new options.</p

Testing for difference between two groups of functional neuroimaging experiments

We describe a meta-analytic method that tests for the di#erence between two groups of functional neuroimaging experiments. We use kernel density estimation in three-dimensional brain space to convert points representing focal brain activations into a voxel-based representation. We find the maximum in the subtraction between two probability densities and compare its value against a resampling distribution obtained by permuting the labels of the two groups. The method is applied on data from thermal pain studies where &quot;hot pain&quot; and &quot;cold pain&quot; form the two groups

Mechanistic Basis for High Reactivity of (salen)Co–OTs in the Hydrolytic Kinetic Resolution of Terminal Epoxides

The (salen)Co(III)-catalyzed hydrolytic kinetic resolution (HKR) of terminal epoxides is a bimetallic process with a rate controlled by partitioning between a nucleophilic (salen)Co–OH catalyst and a Lewis acidic (salen)Co–X catalyst. The commonly used (salen)Co–OAc and (salen)Co–Cl precatalysts undergo complete and irreversible counterion addition to epoxide during the course of the epoxide hydrolysis reaction, resulting in quantitative formation of weakly Lewis acidic (salen)Co–OH, and severely diminished reaction rates in the late stages of HKR reactions. In contrast, (salen)Co–OTs maintains high reactivity over the entire course of HKR reactions. We describe here an investigation of catalyst partitioning with different (salen)Co–X precatalysts, and demonstrate that counterion addition to epoxide is reversible in the case of the (salen)Co–OTs. This reversible counterion addition results in stable partitioning between nucleophilic and Lewis acidic catalyst species, allowing highly efficient catalysis throughout the course of the HKR reaction.Chemistry and Chemical Biolog

The Apolipoprotein M/S1P Axis Controls Triglyceride Metabolism and Brown Fat Activity

Summary: Apolipoprotein M (apoM) is the carrier of sphingosine-1-phosphate (S1P) in plasma high-density lipoproteins. S1P is a bioactive lipid interacting with five receptors (S1P1–5). We show that lack of apoM in mice increases the amount of brown adipose tissue (BAT), accelerates the clearance of postprandial triglycerides, and protects against diet-induced obesity (i.e., a phenotype similar to that induced by cold exposure or β3-adrenergic stimulation). Moreover, the data suggest that the phenotype of apoM-deficient mice is S1P dependent and reflects diminished S1P1 stimulation. The results reveal a link between the apoM/S1P axis and energy metabolism. : Apolipoprotein M (apoM) is the carrier of sphingosine-1-phosphate (S1P) in lipoproteins. Christoffersen et al. show that lack of apoM in mice increases the amount of brown adipose tissue, accelerates the turnover of fat, and protects against obesity. The results reveal a link between the apoM/S1P axis and energy metabolism. Keywords: apolipoproteins, sphingolipids, sphingosine-1-phosphate, lipoproteins, lipid metabolism, triglyceride, brown adipose tissue, apo

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

AbstractDeficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.</jats:p

Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer

Abstract Fluorescence spectroscopy Excitation Emission Matrix (EEM) measurements were applied on human blood plasma samples from a case control study on colorectal cancer. Samples were collected before large bowel endoscopy and included patients with colorectal cancer or with adenomas, and from individuals with other non malignant findings or no findings (N = 308). The objective of the study was to explore the possibilities for applying fluorescence spectroscopy as a tool for detection of colorectal cancer. Parallel Factor Analysis (PARAFAC) was applied to decompose the fluorescence EEMs into estimates of the underlying fluorophores in the sample. Both the pooled score matrix from PARAFAC, holding the relative concentrations of the derived components, and the raw unfolded spectra were used as basis for discrimination models between cancer and the various controls. Both methods gave test set validated sensitivity and specificity values around 0.75 between cancer and controls, and poor discriminations between the various controls. The PARA-FAC solution gave better options for analyzing the chemical mechanisms behind the discrimination, and revealed a blue shift in tryptophan emission in the cancer patients, a result that supports previous findings. The present findings show how fluorescence spectroscopy and chemometrics can help in cancer diagnostics, and with PARAFAC fluorescence spectroscopy can be a potential metabonomic tool