A Novel Form of Memory for Auditory Fear Conditioning at a Low-Intensity Unconditioned Stimulus

Fear is one of the most potent emotional experiences and is an adaptive component of response to potentially threatening stimuli. On the other hand, too much or inappropriate fear accounts for many common psychiatric problems. Cumulative evidence suggests that the amygdala plays a central role in the acquisition, storage and expression of fear memory. Here, we developed an inducible striatal neuron ablation system in transgenic mice. The ablation of striatal neurons in the adult brain hardly affected the auditory fear learning under the standard condition in agreement with previous studies. When conditioned with a low-intensity unconditioned stimulus, however, the formation of long-term fear memory but not short-tem memory was impaired in striatal neuron-ablated mice. Consistently, the ablation of striatal neurons 24 h after conditioning with the low-intensity unconditioned stimulus, when the long-term fear memory was formed, diminished the retention of the long-term memory. Our results reveal a novel form of the auditory fear memory depending on striatal neurons at the low-intensity unconditioned stimulus

Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data

<p>Abstract</p> <p>Background</p> <p>Researchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups differ from one another. Thus, <it>post hoc </it>tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template correlations work extremely well, especially when the researcher has <it>a priori </it>information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased by the researcher's assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally, different patterns may have different probabilities of occurring by chance, importantly influencing researchers' conclusions about a pattern and its constituent genes.</p> <p>Results</p> <p>We developed a four step, <it>post hoc </it>pattern matching (PPM) algorithm to automate single channel gene expression pattern identification/significance. First, 1-Way Analysis of Variance (ANOVA), coupled with <it>post hoc </it>'all pairwise' comparisons are calculated for all genes. Second, for each ANOVA-significant gene, all pairwise contrast results are encoded to create unique pattern ID numbers. The # genes found in each pattern in the data is identified as that pattern's 'actual' frequency. Third, using Monte Carlo simulations, those patterns' frequencies are estimated in random data ('random' gene pattern frequency). Fourth, a Z-score for overrepresentation of the pattern is calculated ('actual' against 'random' gene pattern frequencies). We wrote a Visual Basic program (StatiGen) that automates PPM procedure, constructs an Excel workbook with standardized graphs of overrepresented patterns, and lists of the genes comprising each pattern. The visual basic code, installation files for StatiGen, and sample data are available as supplementary material.</p> <p>Conclusion</p> <p>The PPM procedure is designed to augment current microarray analysis procedures by allowing researchers to incorporate all of the information from post hoc tests to establish unique, overarching gene expression patterns in which there is no overlap in gene membership. In our hands, PPM works well for studies using from three to six treatment groups in which the researcher is interested in treatment-related patterns of gene expression. Hardware/software limitations and extreme number of theoretical expression patterns limit utility for larger numbers of treatment groups. Applied to a published microarray experiment, the StatiGen program successfully flagged patterns that had been manually assigned in prior work, and further identified other gene expression patterns that may be of interest. Thus, over a moderate range of treatment groups, PPM appears to work well. It allows researchers to assign statistical probabilities to patterns of gene expression that fit <it>a priori </it>expectations/hypotheses, it preserves the data's ability to show the researcher interesting, yet unanticipated gene expression patterns, and assigns the majority of ANOVA-significant genes to non-overlapping patterns.</p

Prescribing indicators at primary health care centers within the WHO African region: a systematic analysis (1995-2015)

Abstract Background Rational medicine use is essential to optimize quality of healthcare delivery and resource utilization. We aim to conduct a systematic review of changes in prescribing patterns in the WHO African region and comparison with WHO indicators in two time periods 1995–2005 and 2006–2015. Methods Systematic searches were conducted in PubMed, Scopus, Web of science, Africa-Wide Nipad, Africa Journals Online (AJOL), Google scholar and International Network for Rational Use of Drugs (INRUD) Bibliography databases to identify primary studies reporting prescribing indicators at primary healthcare centres (PHCs) in Africa. This was supplemented by a manual search of retrieved references. We assessed the quality of studies using a 14-point scoring system modified from the Downs and Black checklist with inclusions of recommendations in the WHO guidelines. Results Forty-three studies conducted in 11 African countries were included in the overall analysis. These studies presented prescribing indicators based on a total 141,323 patient encounters across 572 primary care facilities. The results of prescribing indicators were determined as follows; average number of medicines prescribed per patient encounter = 3.1 (IQR 2.3–4.8), percentage of medicines prescribed by generic name =68.0 % (IQR 55.4–80.3), Percentage of encounters with antibiotic prescribed =46.8 % (IQR 33.7–62.8), percentage of encounters with injection prescribed =25.0 % (IQR 18.7–39.5) and the percentage of medicines prescribed from essential medicines list =88.0 % (IQR 76.3–94.1). Prescribing indicators were generally worse in private compared with public facilities. Analysis of prescribing across two time points 1995–2005 and 2006–2015 showed no consistent trends. Conclusions Prescribing indicators for the African region deviate significantly from the WHO reference targets. Increased collaborative efforts are urgently needed to improve medicine prescribing practices in Africa with the aim of enhancing the optimal utilization of scarce resources and averting negative health consequences

Signatures of Environmental Genetic Adaptation Pinpoint Pathogens as the Main Selective Pressure through Human Evolution

Previous genome-wide scans of positive natural selection in humans have identified a number of non-neutrally evolving genes that play important roles in skin pigmentation, metabolism, or immune function. Recent studies have also shown that a genome-wide pattern of local adaptation can be detected by identifying correlations between patterns of allele frequencies and environmental variables. Despite these observations, the degree to which natural selection is primarily driven by adaptation to local environments, and the role of pathogens or other ecological factors as selective agents, is still under debate. To address this issue, we correlated the spatial allele frequency distribution of a large sample of SNPs from 55 distinct human populations to a set of environmental factors that describe local geographical features such as climate, diet regimes, and pathogen loads. In concordance with previous studies, we detected a significant enrichment of genic SNPs, and particularly non-synonymous SNPs associated with local adaptation. Furthermore, we show that the diversity of the local pathogenic environment is the predominant driver of local adaptation, and that climate, at least as measured here, only plays a relatively minor role. While background demography by far makes the strongest contribution in explaining the genetic variance among populations, we detected about 100 genes which show an unexpectedly strong correlation between allele frequencies and pathogenic environment, after correcting for demography. Conversely, for diet regimes and climatic conditions, no genes show a similar correlation between the environmental factor and allele frequencies. This result is validated using low-coverage sequencing data for multiple populations. Among the loci targeted by pathogen-driven selection, we found an enrichment of genes associated to autoimmune diseases, such as celiac disease, type 1 diabetes, and multiples sclerosis, which lends credence to the hypothesis that some susceptibility alleles for autoimmune diseases may be maintained in human population due to past selective processes

Targeting histone deacetyalses in the treatment of B- and T-cell malignancies

HDAC inhibitors (HDACI) are now emerging as one of the most promising new classes of drugs for the treatment of select forms of non-Hodgkin’s lymphoma (NHL). They are particularly active in T-cell lymphomas, possibly hodgkin’s lymphoma and indolent B cell lymphomas. Presently, two of these agents, vorinostat and romidepsin, have been approved in the US for the treatment of relapsed and refractory cutaneous T cell lymphomas (CTCL). Initially, these agents were developed with the idea that they affected transcriptional activation and thus gene expression, by modulating chromatin condensation and decondensation. It is now clear that their effects go beyond chromatin and by affecting the acetylation status of histones and other intra-cellular proteins, they modify gene expression and cellular function via multiple pathways. Gene expression profiles and functional genetic analysis has led to further understanding of the various molecular pathways that are affected by these agents including cell cycle regulation, pathways of cellular proliferation, apoptosis and angiogenesis all important in lymphomagenesis. There is also increasing data to support the effects of these agents on T cell receptor and immune function which may explain the high level of activity of these agents in T cell lymphomas and hodgkin’s lymphoma. There is ample evidence of epigenetic dysregulation in lymphomas which may underlie the mechanisms of action of these agents but how these agents work is still not clear. Current HDAC inhibitors can be divided into at least four classes based on their chemical structure. At present several of these HDAC inhibitors are in clinical trials both as single agents and in combination with chemotherapy or other biological agents. They are easy to administer and are generally well tolerated with minimal side effects. Different dosing levels and schedules and the use of isospecific HDAC inhibitors are some of the strategies that are being employed to increase the therapeutic effect of these agents in the treatment of lymphomas. There may also be class differences that translate into specific activity against different lymphoma. HDAC inhibitors will likely be incorporated into combinations of targeted therapies both in the upfront and relapsed setting for lymphomas

The Emergence of Emotions

Emotion is conscious experience. It is the affective aspect of consciousness. Emotion arises from sensory stimulation and is typically accompanied by physiological and behavioral changes in the body. Hence an emotion is a complex reaction pattern consisting of three components: a physiological component, a behavioral component, and an experiential (conscious) component. The reactions making up an emotion determine what the emotion will be recognized as. Three processes are involved in generating an emotion: (1) identification of the emotional significance of a sensory stimulus, (2) production of an affective state (emotion), and (3) regulation of the affective state. Two opposing systems in the brain (the reward and punishment systems) establish an affective value or valence (stimulus-reinforcement association) for sensory stimulation. This is process (1), the first step in the generation of an emotion. Development of stimulus-reinforcement associations (affective valence) serves as the basis for emotion expression (process 2), conditioned emotion learning acquisition and expression, memory consolidation, reinforcement-expectations, decision-making, coping responses, and social behavior. The amygdala is critical for the representation of stimulus-reinforcement associations (both reward and punishment-based) for these functions. Three distinct and separate architectural and functional areas of the prefrontal cortex (dorsolateral prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex) are involved in the regulation of emotion (process 3). The regulation of emotion by the prefrontal cortex consists of a positive feedback interaction between the prefrontal cortex and the inferior parietal cortex resulting in the nonlinear emergence of emotion. This positive feedback and nonlinear emergence represents a type of working memory (focal attention) by which perception is reorganized and rerepresented, becoming explicit, functional, and conscious. The explicit emotion states arising may be involved in the production of voluntary new or novel intentional (adaptive) behavior, especially social behavior

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

The topographic evolution of the Tibetan Region as revealed by palaeontology

The Tibetan Plateau was built through a succession of Gondwanan terranes colliding with Asia during the Mesozoic. These accretions produced a complex Paleogene topography of several predominantly east–west trending mountain ranges separated by deep valleys. Despite this piecemeal assembly and resultant complex relief, Tibet has traditionally been thought of as a coherent entity rising as one unit. This has led to the widely used phrase ‘the uplift of the Tibetan Plateau’, which is a false concept borne of simplistic modelling and confounds understanding the complex interactions between topography climate and biodiversity. Here, using the rich palaeontological record of the Tibetan region, we review what is known about the past topography of the Tibetan region using a combination of quantitative isotope and fossil palaeoaltimetric proxies, and present a new synthesis of the orography of Tibet throughout the Paleogene. We show why ‘the uplift of the Tibetan Plateau’ never occurred, and quantify a new pattern of topographic and landscape evolution that contributed to the development of today’s extraordinary Asian biodiversity