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irispublisherspsychology · 4 years ago

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Iris publishers-Open Access Journal of Addiction and Psychology (OAJAP)

When the Brain Coordinates Life-Risk Behavior: A Rewarding Anorexia

Authored by Valérie Compan*

Abstract

Understanding how brain supports adapted (and adaptive) decisions when individuals deal with challenge of the environment (stressor) is critical as adapted decision-making (goal-directed behavior) is supposed to protect from disturbances including unexpected death. How does the brain can then trigger chronic consumption of drugs or persistent food restriction (anorexia) until the point of death? In the neurosciences field, most of studies report correlations between behavioral disturbances in the face of environmental challenges and deregulations of neural circuits. Even if causal relationship is less described, exploring these correlations in simpler animal models makes possible the study of molecular and behavioral phenotypes in isolation and has revealed the conservation of specific molecular mechanisms in humans. For instance, involvement of serotonergic system in eating behavior remains crucial as current investigations, consistent with several decades (79 years from 1940 to 2019) of studies, reveals the conservation of specific molecular underpinnings of eating behaviors in animals and humans with eating disorders, suggesting the robustness of identified effects. In this context, studies describe commonalities between restrictive food intake and addiction, as in the nucleus accumbens - a critical structure of the brain’s reward system - activation of addictive signaling under the control of serotonin (5-HT, 5-hydroxytryptamine) 4 receptors (5-Ht4Rs) mediates reduction in motivation for food in food-deprived mice, and ties anorexia and motor hyperactivity; Two hallmarks of anorexia nervosa. Accordingly, the brain prevents the transition from transient to persistent hypophagia (anorexia) with a network governing goal-directed behavior against depressive-like behavior, under the control of 5-Ht4Rs localized in the medial prefrontal cortex. Food restriction at the onset following stress appears as an adapted behavior for managing stressors (as mediated by specific molecular changes related to depression resistance), but in the face of chronic stress, loss-of-control of the mPFC could imbalance the activity of the NAc and triggers persistent anorexia.

Keywords:Addiction; Anorexia; Stress; Food intake; Locomotion; Animal models; Brain; Nucleus accumbens; Medial prefrontal cortex; Serotonin 4 receptors

Abbreviations: cAMP: Cyclic Adenosine Monophosphate; CART: Cocaine- and Amphetamine-Regulated Transcript; KO: Knockout; mPFC: Medial Prefrontal Cortex; Nac: Nucleus Accumbens; Pka: Protein Kinase A; 5-Ht: 5-Hydroxytryptamine Or Serotonin; 5-Htrs: Serotonin Receptors; 5-Ht4rs: Serotonin 4 Receptors; Sirna: Small Interference RNA

Introduction

All behavior appears to be the result of context-dependent brain functions; neuronal networks implement gradually over development during their interrelationships with environmental factors. This cerebral network likely becomes mature contextdependently and may gradually favor adapted (and adaptive) behavior as adapted decision-making (i.e., goal-directed behavior). Goal-directed behavior expresses motivation. For instance, when individuals feel hungry, they are motivated to obtain food under physiological circumstances. Feeling hungry then translates into the demand for energy. Hunger impels the organism to display goal-directed behavior to seek and consume foods and thus survive. However, individuals do not make the decision to feel hungry, but can decide to satisfy or not satisfy hunger. For some individuals, eating behavior can be chronically disordered and can include persistent food restriction and/or excessive intake despite negative consequences, suggesting disturbances of motivation, and of goal-directed behavior. Food is a basic primary reward, requiring motivation to obtain it (“wanting”) [1]. Some investigators assimilate excessive consumption of foods, regardless of whether it is associated with obesity (Corwin RL, et al.) [2], to addiction [3]. However, whether binge eating represents a kind of addiction remains unclear [2]. Here, we describe common molecular mechanisms between anorexia and addiction.

Towards Critical Implication of 5-HT Volume Transmission

The rewarding effect of anorexia has been described in humans at the onset of anorexia nervosa symptoms [4]. Is it the result of excess synapses (fixed brain) that maintain food restriction until the point of death? As the prospect of receipt of a positive reward is capable of inducing risky, and potentially lethal behavior, impairments in the neural underpinnings of persistent food restriction until lethal point could be included in those of dependence.

Some investigators examine the activity of neural centers involved in the recognition of rewards and the development of habits [5]. A report described goal-directed decision-making as a complicated process and argued that reward-based decisions depend on the habit and goal-oriented systems [6]. The habit system “stores” stimulus-response associations based on past rewards and the goal-oriented system selects one action by anticipating the positive and negative outcomes [6]. Indeed, “Addiction is a form of learning and relapse is a persistent memory of the drug experience” [7]. As neural bases of learning and memory appears the result of synapse function, as mainly demonstrated by studies of Eric Kandel; Does neural communication, as the serotonin (5-hydroxytryptamine, 5-HT) volume transmission, serve to avoid habits for conferring the most instant flexibility to better adapt to environmental changes [8]?

The existence of the synapse was critically challenged until 1954; and, in 1975, Descarries L, et al. [9], described that 5-HT binds receptors (5-HTRs), more often located at 100 μm than at 20 nm (synaptic transmission), introducing the volume transmission (Descarries L, et al.) [9], from the site of 5-HT release. The preponderant 5-HT volume transmission extends the ubiquitous distribution of the serotonergic system, supporting its multiple functions; all physiologically interrelated, from habituation, memory, moving etc., to protecting survival, likely in order to critically contribute to adaptive and adapted eating responses to stress. The phylogenetically old serotonergic system then appears as a continued red line underlying crucial functions, which appear sophisticated to the point of a 5-HT-independent action of some 5-HTRs to evoke constitutive activity, such as the 5-Ht4 receptors (5-Ht4Rs), in eating behavior [10].

Serotonergic System in Brain Serves to Reduce Food Intake and Motivation to eat

In mammals, the serotonergic neuronal cell bodies assemble in the raphe nuclei (reviewed in [11]). Among nine nuclei, the dorsal and median raphe nuclei (DR, MR) send axons to the whole forebrain [11]. In particular, the serotonergic axons in the cerebral cortex mainly arise from the DR (Figure 1a). 5-HT binds 18 G-protein coupled receptors (5-HTRs) and commonly mediates reduction in food intake [8].

Hoebel and Leibowitz’s groups in 1976 and 1986 reported, in series of studies, that 5-HT volume transmission commonly serves to reduce food intake, i.e. hypophagia, and to enhance satiety, in the hypothalamus. Following up these findings, decades of reports have described hypophagia following stimulation of 5-HT1B and 5-HT1C receptors (5-HT1BR, 5-HT1CR), whereas 5-HT1AR and 5-HT2BR can exceptionally serve to enhance feeding [12]. In 2004, 5-HT volume transmission has also been reported to favor less motivation for food in food-deprived mice, mediating anorexialike behavior through the activation of addictive signaling (cAMP: cyclic adenosine monophosphate / PKA: protein kinase A / CART: cocaine- and amphetamine-regulated transcript), in the NAc [13,14]. In 2017, causal relationships between the activity of the serotonergic system and hypophagia in response to external stress were identified in a network governing goal-directed behavior [15]. This network consists of the ascending serotonergic inputs from the DR to the mPFC and is controlled by 5-Ht4Rs [15].

In sum (reviewed in Compan V. [8]), under basal conditions, specific 5-HTRs located in an automatic executive system (the hypothalamus) serve to stabilize usual food intake, whereas in response to external stressors, other 5-HTRs; 5-Ht4Rs located in a more adaptive-decisive system, including the mPFC and the NAc, favor rewarding effects of food restriction [14-19]. Such dual organization of 5-HTRs could promote decisional processing and dampen autonomic input, resulting in dysfunctional eating irrespective to requirements for energy. When food intake varies temporarily to the baseline, survival is not compromised, and restrictive food intake may well be adapted (and adaptive with beneficial effects on longevity), but when eating response to stress persists, as seen in anorexia nervosa, survival is compromised [20]. The predominance of cortical control could reflect adaptive processes to prevent “negative emotions” as neural commonalities exist between anorexia and antidepressant effect (Jean A, et al.) [15], (Figure 1); while, hypothalamic events could represent an executive system, which became autonomous as learned during long conserved evolutionary processes. It results the hypothesis formulated above: The low number of 5-HT cortical synapses (30%) could serve to prevent habits (do not have to be memorized) for conferring the most instant flexibility to better adapt to environmental changes [8].

Common Signaling Pathway Between Anorexia and Addiction under the Control of Serotonin 4 receptors

The cerebral distribution of 5-Ht4Rs is conserved from rodents to humans, with one of the highest levels in the NAc [21,22]. Four 5-Ht4Rs splice variants were described in mice (10 in humans) called 5-Ht4(a)R, 5-Ht4(b)R, 5-Ht4(e)R, 5-Ht4(f)R [23]. Stimulation of 5-Ht4Rs reduces deficits of associative learning in olfactory discrimination task (Bockaert J, et al. [24]), and, 5-Ht4Rs favor longterm (but not short-term) memory [25]. In humans, stimulation of 5-Ht4Rs also favors memory [26]. The 5-Ht4Rs may therefore have conserved functions such as feeding from mice to humans. Indeed, stimulation of 5-Ht4Rs reduces food intake in rodents [14-16,19]. And, the concentration of 5-Ht4Rs is low when patients with Alzheimer’s disease overeat, but not in individuals with Alzheimer who did not display hyperphagia [27]. Importantly, 5-Ht4Rs (and apparently not the other 5-HTRs) in the NAc serve to a rewarding effect of restrictive food intake, as stimulation of 5-Ht4Rs mediates anorexia-like behavior through activation of an addictive signaling pathway [cAMP/PKA/CART] [14,19], (Figure 1). Indeed, in neurons of the NAc, activation of a cAMP signaling is a means of transforming an immediate reduction of drugs’ rewarding effect into a durable dependence [28]. Cocaine triggers counteracted adaptive responses as an increased activity of cAMP/PKA signaling in the NAc [28]. The resultant phosphorylation of the cAMP-responsive element binding (pCREB) dampens rewarding effects [29]. The sensitivity to subsequent drug exposures then decreases (tolerance) with increased activity of reward pathways (dependence) to the point that drugs removal triggers declines in motivation, mimicking depression, leading to maladaptive decision [28]. Considering the involvement of CART in motivational properties of cocaine (Rogge G, et al.) [30], these findings evidence commonalities between addiction and anorexia, consistent with the rewarding effect of anorexia seen at the onset of symptoms. Food restriction is initially highly rewarding because the individual feels to cope with difficultto- manage stressors during adolescence and adulthood [31]. Indeed, the brain can implement food restriction until death, as the result of maladaptive decision-making. As deep brain stimulation in the NAc or the anterior cingulate cortex (that is homologous to the rodent mPFC) in patients with anorexia nervosa led to an overall improvement, our studies conducted in animal models may have critical clinical significance [32]. Accordingly, mPFC-5-Ht4Rs serve to prevent persistent food restriction by controlling the ascending 5-HT inputs from the DR to the mPFC under stressful conditions [15], (Figure 1). We predict that deregulation of 5-Ht4Rs could play a vital role in pathological appetitive decision as NAc-5-Ht4R levels are abnormal in overweight humans, which could be related to the capacity of 5-Ht4Rs to reshapes excitatory synaptic connections (Evgeni Ponimaskin, submitted), consistent with less dendritic spines in the NAc in 5-Ht4R knockout (KO) mice [8].

Conclusion

The neuronal network underlying eating behaviors is part of a larger network implicating reward and decision-making systems that react to environmental cues. Accordingly, environmental changes (i.e., stressors) associated with biological predisposition could alter motivation and adaptive decision-making, including persistent food restriction. Adaptive responses to stress depend on the serotonergic system – and, here, adaptive feeding response to stress depends on 5-Ht4Rs - eating disorders could emerge when serotonergic neurons reach the limit of their adaptive capacities. We suggest that a predominance of a cortical control reflects an adaptive process to prevent depressive-like behavior when facing an acute stress at the onset of anorexia nervosa (Figure 1). Numerous studies have to be conducted to test whether in the face of chronic stress, limits of this adaptive process could “submerge” cortical control and “release the influence of the subcortical areas” such as the NAc (autonomous control without adaptive decisional control), in which uncontrolled oscillating changes in common molecule levels (cAMP, CREB: all controlled by G-protein coupled receptors, here by 5-Ht4Rs) could lead to an anarchic consumption of foods (from anorexia to bulimia and/or binge eating).

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davidraudalesuk · 4 years ago

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Bidenomics: the good the bad and the unknown

The two presidential contenders squared up this week in the first debate before America votes on November 3rd. President Donald Trump set out to make it a brawl, even throwing a punch at the validity of the electoral process itself (see article). Joe Biden spent the evening jabbing at Mr Trump for bringing the country to its knees. And the president went for what he hoped would be a knockout blow, accusing his opponent of being a weak man who would succumb to the left’s plans to dramatically expand government and cripple business.

Fear of just such a leftward lurch under Mr Biden is circulating among some American business leaders. However, as we explain (see article), the charge is wide of the mark. Mr Biden has rejected the Utopian ideas of the left. His tax and spending proposals are reasonable. They imply only a modestly bigger state and attempt to deal with genuine problems facing America, including shoddy infrastructure, climate change and the travails of small business. In fact, the flaw in Mr Biden’s plans is that in some areas they are not far-reaching enough.

When Mr Trump took power in 2017 he hoped to unleash the animal spirits of business by offering bosses a hotline to the Oval Office and slashing red tape and taxes. Before covid-19, bits of this plan were working, helped by loose policy at the Federal Reserve. Small-business confidence was near a 30-year high; stocks were on a tear and the wages of the poorest quartile of workers were growing by 4.7% a year, the fastest since 2008. Voters rank the economy as a priority and, were it not for the virus that record may have been enough to re-elect him.

Yet, partly owing to the pandemic, Mr Trump’s shortcomings have also become clear. Long-term problems have festered, including crumbling infrastructure and a patchy social safety-net. The underlying dynamism of business remains weak. Investment is muted and fewer firms have been created even as big ones gain clout. Mr Trump’s chaotic style, involving the public shaming of firms and attacks on the rule of law, is a tax on growth. Deregulation has turned into a careless bonfire of rules. The confrontation with China has yielded few concessions, while destabilising the global trading system.

As the 46th president, Mr Biden would alleviate some of these problems simply by being a competent administrator who believes in institutions, heeds advice and cares about outcomes. Those qualities will be needed in 2021, as perhaps 5m face long-term unemployment and many small firms confront bankruptcy. Mr Biden’s economic priority would be to pass a huge “recovery” bill, worth perhaps $2trn-3trn, depending on whether a stimulus plan passes Congress before the election. This would include short-term money, boosting unemployment insurance and help for state and local governments, which face a budget hole. Mr Biden would also extend grants or loans to small businesses which have not received as much aid as big firms. He would ease tensions with China, soothing the markets. And if a vaccine arrives, his co-operative rather than transactional approach to foreign relations would make its global distribution easier and allow borders to reopen and trade to recover faster.

The recovery bill would also aim to “build back better” by focusing on some long-term problems for America that have also been Biden priorities for many years. He is keen on a giant, climate-friendly infrastructure boom to correct decades of underinvestment: the average American bridge is 43 years old. Government research and development (r&d) has dropped from over 1.5% of gdp in 1960 to 0.7% today, just as China is mounting a serious challenge to American science. Mr Biden would reverse that, too, with more r&d in tech and renewable energy. He would scrap Mr Trump’s harsh restrictions on immigration, which are a threat to American competitiveness. And he wants to raise middle-class living standards and social mobility. That means more spending on education, health care and housing and a $15 minimum wage, helping 17m workers who earn less than that today.

This is hardly the agenda of a socialist. Mr Biden has ignored the Panglossian fantasies of the left, including Medicare For All, a ban on nuclear energy and guaranteed jobs. His plans are moderate in size as well as scope, adding up to an annual increase in public spending of 3% of gdp, assuming they could all pass the Senate. That compares with 16-23% for those of Elizabeth Warren and Bernie Sanders. He would raise taxes to pay for about half of the spending that is approved, with higher levies on firms and the rich. Even if all of his tax plan were enacted, which is highly unlikely, studies suggest corporate profits after tax might drop by up to 12% and the income of the top 1% of earners by up to 14%. If you are rich that would be an irritant, but not a catastrophe.

The real risk of Bidenomics is that his pragmatism will lead him to be insufficiently bold. Sometimes he fails to resolve competing objectives. For example, he rightly supports ladders for social mobility as well as a better safety-net for workers who lose their jobs; his plans range from more affordable housing to free public universities. But equipped with these safety buffers, he should be willing to welcome more creative destruction so as to raise long-run living standards. Instead Mr Biden’s instinct is to protect firms, and he has too little to say on boosting competition, including prising open tech monopolies. Incumbent firms and insiders often exploit complex regulations as a barrier to entry. His plans are wrapped in red tape.

Making trade alliances great again

Mr Biden’s climate policy represents real progress. Building green-power grids and charging networks makes sense because the private sector may hold back. But, again, its effect will be blunted by the rule that 40% of spending must favour disadvantaged communities and by perks for domestic suppliers: a recipe for inefficiency. His plan to cut emissions involves targets, but shies away from a carbon tax which would harness the power of capital markets to reallocate resources. That is a missed opportunity. Just last month the Business Roundtable, representing corporate America, said it supported carbon pricing.

This lack of boldness also reflects the lack of a fully developed strategy. Mr Biden has a record as a free trader, but he will not remove tariffs quickly and his plan indulges in petty protectionism by, say, insisting that goods are shipped on American vessels. That would complicate the daunting task ahead of him: to create a new framework to govern the economic relationship with China, which involves persuading America’s allies to sign up even as they flirt with protectionism, too (see article).

It is the same with fiscal policy. To his credit, Mr Biden wants to pay for some of his spending—a novelty these days. Nonetheless, by 2050 public debt is on track to hit almost 200% of gdp. There is little reason to fret now, when interest rates are near zero and the Fed is buying up government debt. But America would benefit if the next president faced up to this long-term challenge. That would mean beginning to build a harder-nosed consensus on entitlement spending and a sustainable tax base.

Mr Biden still has to win in November, so his ambiguity is understandable. But there is a risk he assumes that victory and a return to growth and competence will be sufficient to set America on the right track. If he wants to renew America’s economy and ensure it leads the rich world for decades to come, he will have to be bolder than that. On the threshold of power, he must be more ruthless about his priorities and far-reaching in his vision.■

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healthtimetaylor · 5 years ago

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Xanthohumol inhibits colorectal cancer cells via downregulation of Hexokinases II-mediated glycolysis.

PMID: Int J Biol Sci. 2019 ;15(11):2497-2508. Epub 2019 Sep 7. PMID: 31595166 Abstract Title: Xanthohumol inhibits colorectal cancer cells via downregulation of Hexokinases II-mediated glycolysis. Abstract: Deregulation of glycolysis is a common phenomenon in human colorectal cancer (CRC). In the present study, we reported that Hexokinase 2 (HK2) is overexpressed in human CRC tissues and cell lines, knockout of HK2 inhibited cell proliferation, colony formation, and xenograft tumor growth. We demonstrated that the natural compound, xanthohumol, has a profound anti-tumor effect on CRC via down-regulation of HK2 and glycolysis. Xanthohumol suppressed CRC cell growth bothand. Treatment with xanthohumol promoted the release of cytochrome C and activated the intrinsic apoptosis pathway. Moreover, our results revealed that xanthohumol down-regulated the EGFR-Akt signaling, exogenous overexpression of constitutively activated Akt1 significantly impaired xanthohumol-induced glycolysis suppression and apoptosis induction. Our results suggest that targeting HK2 appears to be a new approach for clinical CRC prevention or treatment.

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cancersfakianakis1 · 6 years ago

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Cancers, Vol. 11, Pages 251: Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer

Cancers doi: 10.3390/cancers11020251

Authors: Carter J Barger Connor Branick Linda Chee Adam R. Karpf

FOXM1 is frequently overexpressed in cancer, but this has not been studied in a comprehensive manner. We utilized genotype-tissue expression (GTEx) normal and The Cancer Genome Atlas (TCGA) tumor data to define FOXM1 expression, including its isoforms, and to determine the genetic alterations that promote FOXM1 expression in cancer. Additionally, we used human fallopian tube epithelial (FTE) cells to dissect the role of Retinoblastoma (Rb)-E2F and Cyclin E1 in FOXM1 regulation, and a novel human embryonic kidney cell (HEK293T) CRISPR FOXM1 knockout model to define isoform-specific transcriptional programs. FOXM1 expression, at the mRNA and protein level, was significantly elevated in tumors with FOXM1 amplification, p53 inactivation, and Rb-E2F deregulation. FOXM1 expression was remarkably high in testicular germ cell tumors (TGCT), high-grade serous ovarian cancer (HGSC), and basal breast cancer (BBC). FOXM1 expression in cancer was associated with genomic instability, as measured using aneuploidy signatures. FTE models confirmed a role for Rb-E2F signaling in FOXM1 regulation and in particular identified Cyclin E1 as a novel inducer of FOXM1 expression. Among the three FOXM1 isoforms, FOXM1c showed the highest expression in normal and tumor tissues and cancer cell lines. The CRISPR knockout model demonstrated that FOXM1b and FOXM1c are transcriptionally active, while FOXM1a is not. Finally, we were unable to confirm the existence of a FOXM1 auto-regulatory loop. This study provides significant and novel information regarding the frequency, causes, and consequences of elevated FOXM1 expression in human cancer.

https://ift.tt/2ItlVPK

#Oncology-Sfakianakis

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naivelocus · 6 years ago

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microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions

Dataset collection

6724 high confidence MREs were retrieved from direct experiments, including reporter gene assay techniques indexed in DIANA-TarBase repository15,16, miRNA-chimeras from CLASH (crosslinking, ligation, and sequencing of hybrids)13 and CLEAR-CLIP (covalent ligation of endogenous Argonaute-bound RNAs)14 experiments, as well as additional miRNA-target chimeric fragments derived from a previous meta-analysis of published AGO-CLIP datasets12. In order to quantify miRNA-induced mRNA expression changes and to identify functional binding sites, 101 miRNA perturbation experiments were analyzed (89 microarray and 12 RNA-Seq experiments, Supplementary Tables 4–6). This process enabled the formation of ~3900 and 4000 positive and negative miRNA-target pairs, respectively. A set of five ribosome profiling sequencing (RPF-Seq) libraries after miRNA overexpression, capturing differentially ribosome-bound transcripts, and six pSILAC (quantitative proteomics) experiments were an additional source for detecting more than 5900 miRNA effects at protein expression level (Supplementary Tables 7–8). The inclusion of AGO-IP and biotin pull-down high-throughput experiments upon specific miRNA perturbation yielded ~2600 miRNA-binding events (Supplementary Table 9). The aforementioned miRNA perturbation experiments enabled the detection of deregulated targets without specifying the exact binding sites15. miRNA-targeted regions were extracted from AGO-bound enriched regions present in at least 1 of 24 AGO-PAR-CLIP-sequencing libraries (Supplementary Table 1). Published background PAR-CLIP libraries32, stably expressing a commonly utilized non-RBP control (FLAG-GFP), were incorporated in our pipeline to identify non-specific AGO-bound transcripts and deduce more than 24,000 negative miRNA-binding sites. A compendium of 96 AGO-CLIP-Seq experiments was derived from DIANA-TarBase and used to further select background-derived MREs displaying no overlap with AGO-enriched regions (Fig. 2a).

Analysis of high-throughput miRNA perturbation experiments

High-throughput experiments were collected to measure gene expression alterations after specific miRNA transfection, silencing, or knockout. Log2 fold-change values as calculated from differential expression analyses of control versus post-treatment state enabled the formation of miRNA–mRNA positive and negative interactions.

A total of 44 microarray studies of distinct experimental conditions (Supplementary Table 4, 6) covering 43 human cell lines and 49 miRNAs were examined to deduce positive and negative miRNA-target interactions. In-house analysis was initiated from microarray raw data (Affymetrix.CEL files). Probe set summarization was implemented using Robust Multi-Array Average (RMA) with R packages affy33 or oligo34. Annotation of probe sets to Ensembl Gene IDs was accomplished using the chip-specific annotation R packages hgu133a2.db, hgu133plus2.db or hugene10sttranscriptcluster.db. miRNA-treated and control samples in each experiment were analyzed independently of other cell lines or miRNA treatments. Log2 fold-change ratios and p values were calculated with limma package35, following package instructions on Single-Channel Designs. Probe sets mapping to the same gene were averaged to calculate its fold-change. A log2 fold-change cutoff of ±1 ( > 1 or < −1, respectively), depending on the type of regulation, was applied to determine negative and positive interaction subsets. For GSE8501 experiment conducted in Rosetta–Merck microarrays, error-weighted log10 intensity ratios were retrieved and transformed to log2-scale.

Ribosome profiling sequencing (RPF-Seq) and RNA-Seq libraries treated with specific miRNA overexpression, 12 experimental conditions in total (Supplementary Tables 5–7), were retrieved from Eichhorn et al.36, Nam et al.11, Pellegrino et al.37, Zhang et al.38. To identify positive/negative miRNA interactions, a ±0.5 log2 fold-change threshold was applied to genes presenting > 10 RPKM expression.

Quantitative proteomics datasets (pSILAC) in HeLa cells following the individual overexpression of five human miRNAs (let-7b, miR-1, miR-16, miR-30a, and miR-155) or knockdown of let-7b (Supplementary Table 8) were derived from Selbach et al.20. Positive/negative miRNA interactions were deduced using a ±1 log2 fold-change threshold, respectively.

Analysis of AGO-PAR-CLIP and (s)RNA-Seq expression datasets

AGO-PAR-CLIP datasets from nine studies, corresponding to 13 cell lines in human species, were derived from GEO7,39 and DDBJ40 repositories (Supplementary Table 1). Fifteen small RNA-Seq and 9 RNA-Seq experiments of similar cell types with PAR-CLIP libraries were analyzed following methodologies as described by Vlachos et al.41 to infer expressed miRNAs and transcripts. (s)RNA-Seq datasets were derived from the ENCODE repository and from a series of studies (Supplementary Tables 10, 11). Whole transcriptome depleted from ribosomal RNAs and poly-A selected RNA-Seq libraries were analyzed.

Pre-processing and alignment of PAR-CLIP datasets was realized as described by Vlachos et al.15. Initially, libraries were quality checked using FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapter sequences were retrieved from the original publication or GEO/SRA entries, when available. Contaminants were detected utilizing in-house developed algorithms and the Kraken suite42. Pre-processing was performed utilizing Cutadapt43. PAR-CLIP reads were aligned against human reference genome (GRCh37/hg19) with GMAP/GSNAP44 spliced aligner, appropriately parameterized to identify known and novel splice junctions. microRNA expression was quantified using miRDeep245. Ensembl v7546 and miRBase v1847 were used as annotation for genes and microRNAs, respectively. Top expressed miRNAs and AGO-PAR-CLIP data in each cell type, were jointly utilized as input to microCLIP in silico framework for miRNA-target identification. Specifications on the processed 36 datasets are provided in Supplementary Tables 1, 10, 11.

For the analyses presented in Figs. 3–5 and Supplementary Figs. 4, 7, enriched AGO-CLIP peaks covered with reads having at least 20% cross-linked sites in the same position are defined as T-to-C targeted regions.

Analysis of PARS experimental data

PARS sequencing data on total RNA isolated from lymphoblastoid cells were obtained from Wan et al. study18 (GEO accessions GSM1226157, GSM1226158). The identification of single or double stranded regions, across the human transcriptome, was derived from deeply sequenced RNA fragments generated from RNase S1 or V1 nuclease treatment of GM12878 cells, respectively.

Raw reads of 51nt length, accordingly pre-processed for quality control and contaminant removal, were aligned against human reference genome (GRCh37/hg19) with GSNAP spliced aligner. This analysis resulted in ~130 M uniquely mapped PE-sequenced fragments per sample. In order to derive structural signals in RNase S1 or V1 nuclease experiments at single base resolution, we calculated single(S1) and double(V1) stranded raw reads initiating on each nucleotide. The number of PARS tags per sample starting at each base were normalized by sequencing library depth. These base intensities were subsequently combined into the formula described by Wan et al. to compute PARS scores.

RSS were defined by estimated PARS scores in the vicinity of PAR-CLIP-derived miRNA-binding sites in four lymphoblastoid cell lines from the study of Skalsky et al.2. miRNA-mRNA interactions were identified in both T-to-C and non-T-to-C PAR-CLIP clusters, corresponding to transcripts with > 1 TPM expression in GM12878 cells. For expressed miRNAs ( ≥ 50 aligned reads per miRNA) in respective EFD3-AGO2, LCL-BAC, LCL-BAC-D1, and LCL-BAC-D3 EBV infected lymphoblastoid cells, we included collapsed miRNA-binding sites residing within the PAR-CLIP clusters. For the performed comparisons, we incorporated negative MREs extracted from different high-throughput miRNA perturbation experiments (more detailed description in Methods “Analysis of miRNA transfection/knockdown high-throughput experiments”). MREs utilized for the assessment of RSS signatures on AGO-bound clusters and the derivation of (non-)functional conformations of miRNA-target base pairings, were localized on coding and 3′UTR regions. The examined sites had to present S1 and V1 signals in at least half of their occupied bases.

sRNA-Seq and RNA-Seq datasets were retrieved from ENCODE consortium (GEO accession numbers GSM605625, GSM1020026, GSM1020027, GSM1020028, GSM1020029, and GSM1020030).

microCLIP in silico framework

Feature set description: A set of 131 descriptors (Supplementary Data 1) with non-zero variance was included in microCLIP. The extracted features were retrieved from positive/negative miRNA interactions, identified on AGO-bound locations in different PAR-CLIP datasets. They comprised PAR-CLIP-specific descriptors, such as substitution ratios and distance of conversions from the MRE start, as well as coverage metrics. Aggregate substitution ratios, positions, and distances independent of the transition type were also included. In order to estimate MRE and AGO-peak respective sequencing coverage, we calculated normalized RPKM values for miRNA-target sites and clusters.

Moreover, single base and dinucleotide contents for miRNA-binding and respective flanking regions, complexity features for the MRE and proximal upstream/downstream sequences were introduced to microCLIP model. BLAST’s DUST score48 and Shannon-Wiener Index49 constituted measurements for masking sequence complexity. Other descriptors were formed to represent energy-related variables for the duplex structure, while metrics capturing sequence content skewness/asymmetry (GC-skew, AT-skew, purine-skew, Ks-skew) and biases of codon usage were added. Entropy, enthalpy, free energy, and melting temperature (Tm) thermodynamic properties were calculated for MRE sequences in R.

miRNA-target hybrids were associated with different descriptors such as the binding type, duplex structure energy calculated with the Vienna package50, positions and nucleotide composition of (un)paired nucleotides. Distinct features have been established to model (mis)matches, bulges, loops, and wobble pairs for miRNA-MRE hybrid structure and sub-domains encountered in the duplex. The distinct domains for miRNA sequences, as defined by microCLIP, are: (i) seed region (2–8 positions), (ii) central region (9–12 positions), (iii) 3′ supplementary/compensatory region (13–16 positions), (iv) tail region (17-3′ miRNA end). Similar regions were designated on the MREs based on the miRNA-binding anchors upon duplex formation.

Our approach incorporates conservation of the MRE and upflank/downflank-MRE regions. phastCons pre-computed scores from genome-wide multiple alignments were downloaded from the UCSC repository51 in bigwig format and were utilized to deduce respective evolutionary rates. Conservation signals were computed as mean intensities of the phastCons base-wise scores on miRNA targeted regions, as well as their flanking regions. The conservation of the 5′ MRE binding nucleotides was independently modeled. microCLIP integrates additional features corresponding to the location of the MRE within the AGO-enriched cluster and binding length ratios of miRNA and/or target regions.

The applied super learning scheme benefits from the incorporation of the complete array of features, maximizing their contribution through their parallel use in different classification models in every node. The impact of weaker features and classifiers in optimal super learner design and behavior is shown in Supplementary Fig. 9, where microCLIP performance was compared to three different classification schemes using the independent validation set available in Supplementary Data 3.

Description of the algorithm: microCLIP operates on AGO-PAR-CLIP-sequencing reads, requiring a SAM/BAM alignment file and a list of miRNAs as minimum input. It initially seeks for AGO-enriched regions and resolves coverage and observed transitions. A sensitive pipeline is adopted to scan read clusters for putative targeted sites including a wide range of binding types. The algorithm supports an extended set of (non-)canonical matches including 6mer to 9mer, offset 6mer, 3′supplementary and compensatory sites as well as (im)perfect centered bindings. microCLIP extracts features for each candidate MRE and subsequently scores sites through a super learning scheme.

The adopted framework incorporates two distinct levels of classification. The first layer comprises a group of 9 different nodes (base classifiers), which are aggregated in the meta-classifier of the second layer. The learning procedure is decentralized through the distinct nodes and relevant base classifiers that specialize in different subsets of features (Fig. 2b). “Region Features” node comprises CLIP-Seq-derived features, such as RPKM coverage, substitution frequencies, and region-related descriptors, including nucleotide composition, conservation, sequence energy, complexity, content asymmetry, and biases of codon usage. A set of five additional base classifiers were designed for MRE-specific features. Binary binding vectors of miRNA/MRE hybrid were separately incorporated in a base classifier (“Binding Vectors”). Each vector element corresponds to one (un)paired position in the duplex. Matches per miRNA/MRE sub-domain were added to a distinct base classifier introducing a group of 13 features regarding total and consecutive matches in the miRNA-target structure as well as in MRE and miRNA relevant sub-domains. Another base model consists of miRNA-target duplex descriptors (“Duplex Features”) including miRNA-target duplex structure energy, bulges, internal loops, GU wobbles, and AU base pairing features for the specified miRNA and/or target and relevant sub-domains. The “Base pairing” node encompasses composition descriptors (A, T, G, C) of the (un)paired nucleotides. An extra base learner incorporates MRE general descriptors such as the degree of overlap with the respective cluster, conservation of MRE bound nucleotides, MRE location within the cluster, MRE binding type as well as metrics for duplex paired nucleotides content asymmetry/skewness. The latter five base models are dedicated to the determination of genuine miRNA-binding sites. Non-overlapping feature sets from the aforementioned base nodes are combined into three supplementary classifiers also incorporated into microCLIP framework.

Eight of the nine base nodes adopt a super learning scheme that assembles the output of seven individual Random Forest (RF), Generalized Linear Model (GLM), Gradient Boosting Model (GBM), Deep Learning (DL) classifiers (2 RF, 2 GBM, 2 DL, 1 GLM models). The “Region features” is analyzed by an RF classification scheme. The retrieved scores from each node are aggregated in a final GBM meta-classifier.

Model training: The DL models developed for the microCLIP framework adopt a feed-forward multi-layer architecture. The input layers match the respective feature space and values are subsequently propagated within three hidden layers. We utilized a rectifier activation function to retrieve weighted combinations of the inputs transmitted to interconnected neuron units. Dropout regularization was added to achieve model optimization and avoid overfitting. A cross entropy cost-function was selected to adapt weights during the learning process by minimizing the loss. Bernoulli distribution function was used along with cross entropy (log-loss) to model the response variables. The output layer at the end of the network applies a Softmax activation function so that each neuron (predicted class) results in a probabilistic interpretation. The DL network depth, width, and topology, as well as activation functions and learning parameters were modeled with a tuning-in grid search algorithm using H2O52 R package. The RF, GBM, GLM learning models were developed, parameterized, and tuned with the caret53 and H2O52 R packages.

Base classifiers were trained against a collection of 8693 positive and 21,789 negative miRNA interactions (Supplementary Table 3). The final GBM meta-learner that aggregates the base classifier outcomes was trained against an independent dataset comprising 3276 and 6702 positive and negative instances, respectively. Ten-fold cross-validation was performed on the training data to estimate each model’s accuracy and finalize the algorithm’s learning architecture. Distribution of base model scores on positive and negative instances and their respective performance, in terms of sensitivity and specificity in an independent test set of ~4000 instances (Supplementary Table 3), are depicted in Supplementary Fig. 10. The individual performance of internal classifiers (DL, RF, GBM, GLM) in microCLIP base models adopting a super learner approach is shown using the same set in Supplementary Figs. 11, 12. Additionally, the performance of the super learner scheme against Random Forest models was tested (Supplementary Fig. 9). microCLIP training and required computations for model optimizations were multi-threaded.

A microCLIP model adopting the same super learning scheme, including information only from T-to-C enriched sites, microCLIP T-to-C, was also deployed. Clusters from the training set incorporating adequate T-to-C transition sites were selected as input to re-train the super learning classifier. Additional support for the robustness of CLIP-guided super learner classification irrespective of non-T-to-C site inclusion is provided through evaluations of microCLIP T-to-C algorithm performance (Supplementary Fig. 8), as well as of its prediction efficacy (Supplementary Fig. 13) using the same test and datasets as in Fig. 6.

Expression correlation analysis on (s)RNA-Seq TCGA samples

271 TCGA ductal breast cancer RNA-Seq and sRNA-Seq samples were obtained from Firehose (http://gdac.broadinstitute.org/runs/stddata__2016_01_28). mRNA and miRNA pre-computed expression values were RPM and RPKM units, respectively. In downstream analyses, miRNAs/mRNAs with zero expression value in at least 70% of the samples were filtered out. miRNAs presenting > 10 RPM in > 10% samples were included, based on miRBase criteria for defining a high confidence set47. The mRNA set was specified by applying a threshold of more than 1 RPKM in at least 10% of the processed samples. Zero mRNA expression values were replaced by the lowest non-zero RPKM value per sample. This pipeline resulted in a set of 13,346 mRNAs and 322 expressed miRNAs. Pearson correlation coefficient was computed for each miRNA-target pair across samples.

Functional analysis of AGO-PAR-CLIP-derived miRNA targets

miRNA-target pairs were retrieved from the analysis of MCF7 PAR-CLIP library (Farazi et al.3) with microCLIP in silico framework. The 100 most highly expressed miRNAs and their targets in 3′ UTR regions were retained. Gene set enrichment analysis of AGO-PAR-CLIP-detected miRNA targets was performed for KEGG pathways54 using the R package limma35. Enrichment P values were corrected for multiple comparisons using Benjamini–Hochberg false discovery rate and a 0.01 p-value threshold was applied. R package Pathview55 was used to visualize targeted pathway members in KEGG pathways.

miRNA interactions from in silico implementations

In order to form a complete list of interactions for MIRZA4, microMUMMIE6, and PARma5 computational approaches, each algorithm was evaluated on a set of 7 PAR-CLIP HEK293 libraries obtained from Kishore et al.21 and Memczak et al.31 studies (GEO accessions GSM714644, GSM714645, GSM714646, GSM714647, GSM1065667, GSM1065668, GSM1065669, and GSM1065670). The proposed settings for each implementation were retrieved from the relevant publications.

The MIRZA biophysical model was executed in the “noupdate” mode. The algorithm provides an optional parameterization to introduce miRNA expression profiles. We realized two different runs of MIRZA, with and without cell type-specific miRNA expression values that were extracted from the CLIPZ web server (http://www.clipz.unibas.ch). MIRZA input data were 51nt AGO-bound sequences centered on T-to-C sites and mature miRNA sequences of 21nt length as reported in the model’s restrictions. The “target frequency” score was utilized to evaluate the quality of MIRZA-detected sites.

microMUMMIE algorithm was tested in both Viterbi and posterior decoding modes. Following microMUMMIE’s constraints, we utilized PARalyzer v1.57 to define the set of T-to-C AGO-enriched peaks. An extra pre-requisite annotation step to complement PARalyzer detected clusters was implemented with the PARpipe tool (https://github.com/ohlerlab/PARpipe). Derived files, comprising annotated AGO clusters with positions of T-to-C transitions, constituted the input of the microMUMMIE core algorithm. Predictions with signal-to-noise ratio (SNR, generally correlated with sensitivity) equal to 9.95 were retained, while posterior probabilities were utilized for the evaluation of microMUMMIE’s performance.

PARma was applied on AGO-PAR-CLIP aligned data that were prepared following the algorithm’s described format. The required input files contained genomic locations of aligned CLIP reads along with positions of observed conversion sites. PARma predictions are coupled with Cscore and MAscore scores for the cluster and miRNA seed family activity, respectively. The latter score was utilized for PARma-detected miRNA-target sites evaluation.

Precompiled (non)conserved miRNA site context++ scores for representative transcripts were downloaded from the Targetscan v7.2 site (http://www.targetscan.org/cgi-bin/targetscan/data_download.vert72.cgi). Targetscan v7 algorithm was additionally executed following the proposed settings in order to cover a greater transcript collection, as well as the whole spectrum of Targetscan-detected interactions including 6mer sites. Gene annotation files were retrieved from the Targetscan v7.2 official download page, and the miRNA seed sequence file that is a pre-requisite for the execution of the model was provided by Targetscan developers. The local Targetscan run complements the precompiled data with miRNA-target interactions on transcripts presenting the longest 3′UTR, in cases they are not deposited on the online repository.

Median fold changes

The comparison between microCLIP and existing implementations was performed using six gene expression profiling datasets following individual transfection of highly expressed miRNAs into HEK293 cells (GEO accessions GSE60426, GSE52531, GSE21901, GSE14537, GSE35621, Supplementary Table 6). Genes with unchanged expression levels (zero log2 fold change) following miRNA transfection and/or knockdown have been filtered out. Subsequent measurements were realized at the gene level. miRNA-gene interactions retrieved from each implementation were sorted according to their prediction scores. Each miRNA-target pair was characterized by the highest scored miRNA-binding site overlapping coding or 3′UTR exons, since utilized algorithms provided MRE-oriented prediction scores. In cases of multiple transcript-gene associations, the transcript with the longest 3′UTR was selected. Median expression log2 fold changes were estimated in consistence with the number of top predicted targets. Aggregated expression changes of genes were calculated by applying stepwise score thresholds. Paired comparisons required tested programs to have targets at every computational cutoff. Lower mean log2 fold changes correspond to stronger downregulation of the detected targets upon miRNA transfection. The statistical differences in the mean log2 fold-change values obtained by each implementation were assessed using two-tailed Wilcoxon signed-rank test. Identified targets by each algorithm were also juxtaposed against averaged log fold changes of 1000 randomly selected genes (without replacement). The mean log2 fold-change values of the randomly selected genes in different stepwise thresholds were taken and the median curve derived from these values was calculated. Genes with zero fold-change indication were filtered out from the random selection process.

Statistics

Enrichment analyses were performed using one-sided Fisher’s exact test. Correlations between quantitative parameters were assessed by calculating Pearson’s correlation coefficient. Comparisons between two or more groups were conducted using Wilcoxon’s rank-sum test and Kruskal-Wallis’ test, respectively. In the latter, Wilcoxon’s rank-sum test was performed as a post hoc test in order to assess between-group differences. The one-sided Kolmogorov–Smirnov test was used to test for greater functional efficacy. In cases of multiple hypothesis testing, Benjamini–Hochberg’s false discovery rate was applied to control family-wise error rate. P values < 0.05 were considered as statistically significant.

— Nature Communications

#Nature Communications #microCLIP super learning framework uncovers functional transcriptome-wide miR

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brianfrench1995 · 4 years ago

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Air California hybrid livery. Plane was leased from United airlines. Vintage postcard @postcardtimemachine

#travel #airplanes #airplane #plane #planes #aviation #flying #ephemera #advertisement #postcard #postcards #united airlines #Air California #AirCal #deregulation knockouts #hybrid

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ekambi · 8 years ago

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If Miss Le Pen needed to land a knockout punch to win the election, she failed, but the differences between the two candidates could not have been clearer. They spent a lot of time on the economy, unemployment, social security, public debt, etc., with Miss Le Pen taking the populist, almost paternalist position, while Mr. Macron stood for deregulation and free markets.

Macron and Le Pen Square Off - American Renaissance

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takebackthedream · 8 years ago

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Trump Offers “A Nation of Miracles.” Your Move, Democrats by Richard Eskow

The bad news for Democrats in President Trump’s first speech to a joint session of Congress is that he exceeded expectations. A Washington Post headline called the speech “surprisingly presidential.” it’s likely to solidify Trump’s hold on his base, and will probably gain him some additional ground.

Expectations weren’t very high, especially after the apocalyptic tone of his inaugural address. The fact that he didn’t announce the End of Days and call down hellfire on four-fifths of the globe probably caused sighs of relief all over the country.

Still, Trump’s detractors forget that he has a gift for aspirational rhetoric that plays well among many Americans. It’s a gift many Democrats seem to have lost.

A Nation of Miracles

Trump was clearly chastened by recent criticism over his seeming indifference to racism. He began by noting Black History Month, a wave of anti-Semitic threats and vandalism, and the shooting of two Indian-American men in Kansas City.

Trump’s remarks were uplifting. We know, because he told us so.

“I am here tonight to deliver a message of unity and strength,” he said, “and it is a message deeply delivered from my heart.” He spoke of “American greatness,” “a new national pride,” “a new surge of optimism,” and “the renewal of the American spirit.”

“Our children will grow up in a nation of miracles,” Trump said as he promised jobs, medical breakthroughs, and the rebuilding of America’s infrastructure.

The speech hewed to themes laid out by Trump political advisor Stephen Bannon at the Conservative Political Action Conference. “The center core of what we believe,” Bannon said, “(is) that we’re a nation with an economy… a culture and a reason for being.”

That belief is as exclusionary as it is visionary. Trump’s comments about economic greatness were matched, almost word for word, by fearmongering about immigrants. When it comes to that “nation of miracles,” it seems that only native-born Americans need apply.

The Fine Print

The president seemed to promise a major infrastructure plan in his speech, but it’s important to read the fine print. Trump said:

“I will be asking the Congress to approve legislation that produces a $1 trillion investment in the infrastructure of the United States – financed through both public and private capital – creating millions of new jobs.”

That connecting phrase, “financed through public and private capital,” is telling. It sounds like a plan to sell off some the resources Americans hold in common – from bridges and dams to the federal highway system – coupled with massive corporate tax breaks and a plethora of financial deals that will funnel billions in public funds to Wall Street’s already-overflowing coffers.

You didn’t think Goldman Sachs was staffing his administration without getting something out of the deal, did you?

Falling in Line

Trump’s speech made clear he has brought most recalcitrant Republicans firmly to heel. “I am sickened by what I heard today,” House Speaker Paul Ryan said last October about Trump’s videotaped sexual remarks. “Women are to be championed and revered, not objectified.”

“I have the high privilege and distinct honor of presenting to you the president of the United States,” Ryan said as he introduced the president to the gathered lawmakers. Ryan, Vice President Mike Pence, and other GOP leaders stood and applauded over and over as Trump lied about crime, terrorism, or immigration.

Weren’t they supposed to be the decent ones?

But the Republicans in Congress have tamed Trump, too. Tuesday’s speech largely toed the Republican party line.

Take infrastructure. On the campaign trail, Trump promised major government investment. On Tuesday, he promised a financial boon for corporations and bankers.

He promised no American would go without healthcare. But the ideas Trump floated on Tuesday could have been written by the insurance executives he hosted on Monday – and probably were. There, too, Trump’s political independence has been replaced by Republican orthodoxy.

He promised not to cut Social Security or Medicare, as Congressional Republicans are determined to do. But he pointedly refused to repeat that promise on Tuesday night.

Republicans applauded lustily for most of Trump’s speech. But the applause seemed to grow tepid on both sides of the aisle when Trump mentioned having killed the TPP trade agreement. The TPP, like NAFTA and other such pacts, was a bad deal for American workers. Nevertheless, a “bipartisan” consensus of Washington insiders – a group that is heavily funded by corporate contributions – has supported those agreements for decades.

Small Dreams

But it is Trump’s optimism in the speech, not the specifics, that many people will remember. And with Trump’s approval rating at historic lows, he has no place to go but up.

That’s why it’s so frustrating to see so many Democrats continue to flounder in the face of a political phenomenon they don’t seem to understand. Americans in the Republican base feel hopeless, so they vote for politicians like Trump. Americans in the Democratic base feel hopeless, too, so many of them don’t vote at all.

Trump offered a bold-sounding vision of trillion-dollar spending and major policy reform. But the policy portion of former Kentucky governor Steve Beshear’s Democratic response began with Beshear boasting that he had been “fiscally responsible… balanced our budget and turned deficits into surpluses without raising taxes.”

Too many Democrats remain obsessed with irrelevant political processes and outdated fiscal notions, even as their nemesis weaves a vision of a better life for millions. These self-appointed arbiters of expectation behave as if idealism itself was irresponsible. They seem to pride themselves on the smallness of their dreams.

Beshear led off by explaining that while he is a Democrat, he is also “a proud Republican and Democrat and mostly American.” That may have just been a gaffe, but it reflects a long-standing Democratic reluctance to associate themselves with their own party.

For what it’s worth, Beshear improved considerably after that.

Restarting the Engine

If Trump scored points with his proposal for a “deregulation task force,” was he also scoring a subliminal association with his “deportation force”? That’s partly because Democrats have failed to make the case for government’s vital role in keeping Americans safe and secure.

If his childcare plan sounded generous when it’s actually a giveaway to the rich, that’s partly because Dems haven’t agreed on one of their own.

And if his underhanded corporate giveaway on infrastructure raised some people’s hopes, that’s partly because so many Democrats haven’t dared to think big for a long time.

Trump claimed that tax breaks for corporations would “restart the engine of the American economy.” But workers, not corporations, drive the economy. This country needs a political party that will make that case, boldly and directly.

Democrats who were hoping Trump would inflict a knockout punch on himself Tuesday night undoubtedly walked away disappointed. It looks like they’re going to have to learn to fight for themselves.

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consciousowl · 8 years ago

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Is There an Upside To Setbacks?

Hello darkness, my old friend,

I've come to talk with you again,

Because a vision softly creeping,

Left its seeds while I was sleeping,

And the vision that was planted in my brain

Still remains

Within the sound of silence.

Paul Simon, “The Sound of Silence”

Have you ever flunked a major exam and failed the class? Have you ever had advanced placement, only to find that you weren’t up to the challenge, and better go back to the next class of freshmen coming in?

Maybe you have even been suspended from school, and had your relatives and friends lecture you and tell you that you were up to no good.

Either you felt like a dunce forced to sit at the back of the class and face continual ridicule, or a juvenile hood or punk, like one of the famed motorcyclists, “Born to Raise Hell.”

Is a Setback Really a Setback?

We all assume that a setback is either all our fault, or someone out there is to blame for messing us up. We often start out with a tight agenda in life, and it better be filled. If we have aspirations, we want an ivy league education, professional credentials and early membership in a rising star startup that all but guarantees we turn millionaires by our early 30’s.

We rarely consider that we primarily learn through challenges, obstacles and setbacks, like a flabby prize fighter who has never been thrown into the ring or done advanced training.

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One knockout punch, and it would be all over. You don’t get to take on the World Heavy-Weight Champion until you are more than ready… until you’ve gotten enough beating to get there.

Life doesn’t usually throw us the most severe challenges until we are ready.

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Character-building is no joke. Most millionaires, even billionaires, have declared bankruptcy several times in their careers. Just look at Donald Trump! You aren’t a seasoned pro until you’ve been around the block.

Welcome to the club!

Revisiting the Past Can Be Enlightening

I find Amazon’s The Man in the High Castle the most intriguing TV / Movie series I have ever watched. It was introduced before Donald Trump ran for President, and President Obama was in his heyday. It is based on the premise that President Roosevelt was assassinated early on, and America waddled through the Great Depression much longer and sat through World War II until the fascists proved invincible.

By the second season, American boys in school are saluting the Nazi flag and chanting “Heil, Hitler!” An authoritarian society has become the standard, and a liberal society is but a distant dream, with a handful of Resistance operatives risking their lives daily.

There is only one problem with this whole scenario. Yes, Donald Trump has become our President, and yes, he has German ancestry. However, we can never fully return to the past, even though we see his Presidential orders reversing many of President Obama’s key initiatives. We are now in a global society, and too many people in too many places know too much.

History spirals forward, constantly moving in circles, but always inching upward.

We don’t have expansion without recession, booms without bust, regulation without deregulation. Everything moves in cycles, much like the celestial bodies. Reversing direction is nature’s way of driving us to the next level.

Setbacks: The Macro Perspective

While it is true that global warming and climate change are a direct threat to our collective survival, it is also true that other priorities need to be addressed. Recent Democratic Presidents, such as Bill Clinton and Barack Obama, have done a lot to bring us together on a planetary scale. But they have also made miscalculations on the domestic and international scale that severely set us back.

Anyone who has lived through the Cold War era has no interest in seeing relations with Russia deteriorate. A massive amount of psychic energy was wrapped up in the Eastern and Western blocks. We couldn’t collaborate in any meaningful way, such as conducting joint missions to other planets. The collective firepower of America and Russia was such as to annihilate humanity 50 times over!

Yet the Democratic Party has been relatively clueless about challenges within Russian society, or their struggle to transition out of a planned socialist economy into a thriving capitalist economy. Had America been more helpful in the 1990’s, the 2000’s might have gone a whole lot smoother.

Donald Trump revealed that economic politics in the United States have replaced identity politics.

Just think of the “Occupy Wall Street” movement. While affirmative action and other programs have done much for minority groups throughout the country, youths of European descent are challenged to start a new life and buy a house. They must stay with their parents throughout their 20’s. Manufacturing has all but evaporated here.

There are always two sides to any issue.

Setbacks: The Micro Perspective

Several years back, I was hospitalized for a relatively minor surgery. Due to complications, I had to stay two full weeks, with unpleasant financial repercussions. From there, I had two months of convalescence where I was challenged to walk normally.

During the first week in the hospital, I have one of the most moving spiritual experiences in my life, where I was never closer to God. I felt thankful to everyone and everything. I enjoyed a beautiful suite with a view, and constant attendance by the nurses and specialists. Those people who were really there for me came several times. I, myself, felt like an honored guest.

After the hospital stay, I had a chance to get much closer to my best friend, getting much more in synch with her. We developed a deeper understanding and appreciation of one another than might ever have been possible. What was initially a major upset (initially my very life was in danger), turned out to be a great blessing, and I am healthier than before.

Always Have a Plan B

It is always a good idea to have a Plan B to back up your Plan A. Reversals have a way of occurring when you least expect them. You needn’t change your objective, just the way that you fulfill it. My partners and I planned to enlighten the world with a book revealing our new planetary age. When the book didn’t realize the traction in the market we had hoped, we took a major step backward.

Later on, it became most apparent that our grand vision was too big for most to grasp and we needed to create this web portal, and address people’s day-to-day challenges, so our vision could be more easily shared and seen, and so that our reader’s might more easily apply this to their lives.

It has been joked that, so often in life, our Plan A turns to Plan B, we might as well start off with our Plan B. Yet in no other way could we arrive at the place of designing and executing flawless Plan A’s.

Trust in Appearances or Trust in God?

The choice is yours! We can look around us and thoroughly convince ourselves that what we think we see is what is really there. However, quantum physics has made that assumption totally obsolete. All is energy and consciousness. Matter is form, and form is information. We are a delightful dance in universal consciousness.

It takes great courage to trust in Supreme Being despite contrary appearances. Sometimes, it really seems the world is about to end, as one ice shelf after another slips into the ocean, in both the Arctic region and Antarctica.

Yet, as we go within, we can find direct access to the greatest peace and the greatest love imaginable. When things are going well for us, and life seems easy, we have no time for God. Yet, when we are driven to a point of total surrender, we find a power within us that can move the stars.

Cosmic Humor Is the Perfect Prescription

Russians have a great sense of humor, although often it is very deep and black. It comes from massive challenges over the centuries, including fighting off the Monguls, fighting back Napoleon’s armies, and suffering the loss of 20 million people when the Nazi’s again tried to take the country. To top it all off, Russia had to muddle through 70 years of official State Communism, where free speech was too often curtailed, and free thought usually discouraged.

Yakov Smirnoff, who emigrated to the U.S. from Russia in the 1970’s, became a leading American comedian, who played off being both Russian and Jewish. He turned oppression into a source of amusement.

Even though Yakov revealed many weaknesses in the former Soviet Union, he also fostered a love for the Russian people themselves, apart from any political or economic system:

In America, you can always find a party.

In Soviet Russia, The Party can always find you!

Yakov

In my earlier days after my first transformational training, I obsessed about surviving. One of the friends in the movement countered my insanity by suggesting, “You may NOT survive!” Then it dawned on my how absurd such a preoccupation was.

Life is a precious gift. We live by the grace of God (your inner power). As long as God gives me breath, I have a purpose here. Let it be fulfilled!

Whatever doesn't kill you simply makes you stronger.

Friedrich Nietzsche

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