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Our previous studies find that subcutaneously administered (s.c.) subanesthetic ketamine promotes sustained cortical disinhibition and plasticity in adult mouse binocular visual cortex (bV1). We hypothesized that intranasal delivery (i.n.) of subanesthetic ketamine may have similar actions. To test this, we delivered ketamine (10 mg/kg, i.n.) to adult mice and then recorded excitatory pyramidal neurons or PV+ interneurons in L2/3 of bV1 slices. In pyramidal neurons the baseline IPSC amplitudes from mice treated with ketamine are significantly weaker than those in control mice. Acute bath application of neuregulin-1 (NRG1) to cortical slices increases these IPSC amplitudes in mice treated with ketamine but not in controls. In PV+ interneurons, the baseline EPSC amplitudes from mice treated with ketamine are significantly weaker than those in control mice. Acute bath application of NRG1 to cortical slices increases these EPSC amplitudes in mice treated with ketamine but not in controls. We also found that mice treated with ketamine exhibit increased pCREB staining in L2/3 of bV1. Together, our results show that a single intranasal delivery of ketamine reduces PV+ interneuron excitation and reduces pyramidal neuron inhibition and that these effects are acutely reversed by NRG1. These results are significant as they show that intranasal delivery of ketamine induces cortical disinhibition, which has implications for the treatment of psychiatric, neurologic, and ophthalmic disorders.

JTE-607 is an anticancer and anti-inflammatory compound and its active form, compound 2, directly binds to and inhibits CPSF73, the endonuclease for the cleavage step in pre-messenger RNA (pre-mRNA) 3' processing. Surprisingly, compound 2-mediated inhibition of pre-mRNA cleavage is sequence specific and the drug sensitivity is predominantly determined by sequences flanking the cleavage site (CS). Using massively parallel in vitro assays, we identified key sequence features that determine drug sensitivity. We trained a machine learning model that can predict poly(A) site (PAS) relative sensitivity to compound 2 and provide the molecular basis for understanding the impact of JTE-607 on PAS selection and transcription termination genome wide. We propose that CPSF73 and associated factors bind to the CS region in a sequence-dependent manner and the interaction affinity determines compound 2 sensitivity. These results have not only elucidated the mechanism of action of JTE-607, but also unveiled an evolutionarily conserved sequence specificity of the mRNA 3' processing machinery.

Inhibitory interneurons are crucial to brain function and their dysfunction is implicated in neuropsychiatric conditions. Emerging evidence indicates that cholecystokinin (CCK)-expressing interneurons (CCK+) are highly heterogenous. We find that a large subset of parvalbumin-expressing (PV+) interneurons express CCK strongly; between 40 and 56% of PV+ interneurons in mouse hippocampal CA1 express CCK. Primate interneurons also exhibit substantial PV/CCK co-expression. Mouse PV+/CCK+ and PV+/CCK- cells show distinguishable electrophysiological and molecular characteristics. Analysis of single nuclei RNA-seq and ATAC-seq data shows that PV+/CCK+ cells are a subset of PV+ cells, not of synuclein gamma positive (SNCG+) cells, and that they strongly express oxidative phosphorylation (OXPHOS) genes. We find that mitochondrial complex I and IV-associated OXPHOS gene expression is strongly correlated with CCK expression in PV+ interneurons at both the transcriptomic and protein levels. Both PV+ interneurons and dysregulation of OXPHOS processes are implicated in neuropsychiatric conditions, including autism spectrum (ASD) disorder and schizophrenia (SCZ). Analysis of human brain samples from patients with these conditions shows alterations in OXPHOS gene expression. Together these data reveal important molecular characteristics of PV-CCK co-expressing interneurons and support their implication in neuropsychiatric conditions.

BACKGROUND: In the demyelinating disease multiple sclerosis (MS), chronic-active brain inflammation, remyelination failure and neurodegeneration remain major issues despite immunotherapy. While B cell depletion and blockade/sequestration of T and B cells potently reduces episodic relapses, they act peripherally to allow persistence of chronic-active brain inflammation and progressive neurological dysfunction. N-acetyglucosamine (GlcNAc) is a triple modulator of inflammation, myelination and neurodegeneration. GlcNAc promotes biosynthesis of Asn (N)-linked-glycans, which interact with galectins to co-regulate the clustering/signaling/endocytosis of multiple glycoproteins simultaneously. In mice, GlcNAc crosses the blood brain barrier to raise N-glycan branching, suppress inflammatory demyelination by T and B cells and trigger stem/progenitor cell mediated myelin repair. MS clinical severity, demyelination lesion size and neurodegeneration inversely associate with a marker of endogenous GlcNAc, while in healthy humans, age-associated increases in endogenous GlcNAc promote T cell senescence. OBJECTIVES AND METHODS: An open label dose-escalation mechanistic trial of oral GlcNAc at 6 g (n = 18) and 12 g (n = 16) for 4 weeks was performed in MS patients on glatiramer acetate and not in relapse from March 2016 to December 2019 to assess changes in serum GlcNAc, lymphocyte N-glycosylation and inflammatory markers. Post-hoc analysis examined changes in serum neurofilament light chain (sNfL) as well as neurological disability via the Expanded Disability Status Scale (EDSS). RESULTS: Prior to GlcNAc therapy, high serum levels of the inflammatory cytokines IFNγ, IL-17 and IL-6 associated with reduced baseline levels of a marker of endogenous serum GlcNAc. Oral GlcNAc therapy was safe, raised serum levels and modulated N-glycan branching in lymphocytes. Glatiramer acetate reduces TH1, TH17 and B cell activity as well as sNfL, yet the addition of oral GlcNAc dose-dependently lowered serum IFNγ, IL-17, IL-6 and NfL. Oral GlcANc also dose-dependently reduced serum levels of the anti-inflammatory cytokine IL-10, which is increased in the brain of MS patients. 30% of treated patients displayed confirmed improvement in neurological disability, with an average EDSS score decrease of 0.52 points. CONCLUSIONS: Oral GlcNAc inhibits inflammation and neurodegeneration markers in MS patients despite concurrent immunomodulation by glatiramer acetate. Blinded studies are required to investigate GlcNAcs potential to control residual brain inflammation, myelin repair and neurodegeneration in MS.

BackgroundGetting and maintaining Hepatitis C Virus (HCV) cure is challenging among people experiencing homelessness (PEH) as a result of critical social determinants of health such as unstable housing, mental health disorders, and drug and alcohol use.ObjectivesThe purpose of this exploratory pilot study was to compare a registered nurse/community health worker (RN/CHW)-led HCV intervention tailored for PEH, "I am HCV Free," with a clinic-based standard of care (cbSOC) for treating HCV. Efficacy was measured by sustained virological response at 12 weeks after stopping antivirals (SVR12), and improvement in mental health, drug and alcohol use, and access to healthcare.MethodsAn exploratory randomized controlled trial design was used to assign PEH recruited from partner sites in the Skid Row Area of Los Angeles, California, to the RN/CHW or cbSOC programs. All received direct-acting antivirals. The RN/CHW group received directly observed therapy in community-based settings, incentives for taking HCV medications, and wrap-around services, including connection to additional healthcare services, housing support, and referral to other community services. For all PEH, drug and alcohol use and mental health symptoms were measured at month 2 or 3 and 5 or 6 follow-up, depending on HCV medication type, while SVR12 was measured at month 5 or 6 follow-up.ResultsAmong PEH in the RN/CHW group, 75% (3 of 4) completed SVR12 and all three attained undetectable viral load. This was compared with 66.7% (n = 4 of 6) of the cbSOC group who completed SVR12; all four attained undetectable viral load. The RN/CHW group, as compared to the cbSOC, also showed greater improvements in mental health, and significant improvement in drug use, and access to healthcare services.DiscussionWhile this study shows significant improvements in drug use and health service access among the RN/-CHW group, the sample size of the study limits the validity and generalizability of the results. Further studies using larger sample sizes are necessitated.

Cryptochrome (CRY) is a short-wavelength light-sensitive photoreceptor expressed in a subset of circadian neurons and eyes in Drosophila that regulates light-evoked circadian clock resetting. Acutely, light evokes rapid electrical excitation of the ventral lateral subset of circadian neurons and confers circadian-modulated avoidance behavioral responses to short-wavelength light. Recent work shows dramatically different avoidance versus attraction behavioral responses to short-wavelength light in day-active versus night-active mosquitoes and that these behavioral responses are attenuated by CRY protein degradation by constant light exposure in mosquitoes. To determine whether CRY1s mediate species-specific coding for behavioral and electrophysiological light responses, we used an empty neuron approach and transgenically expressed diurnal Aedes aegypti (AeCRY1) versus nocturnal Anopheles gambiae (AgCRY1) in a cry-null Drosophila background. AeCRY1 is much less light sensitive than either AgCRY1 or DmCRY as shown by partial behavioral rhythmicity following constant light exposure. Remarkably, expression of nocturnal AgCRY1 confers low survival to constant white light as does expression of AeCRY1 to a lesser extent. AgCRY1 mediates significantly stronger electrophysiological cell-autonomous responses to 365 nm ultraviolet (UV) light relative to AeCRY1. AgCRY1 expression mediates electrophysiological sensitivity to 635 nm red light, whereas AeCRY1 does not, consistent with species-specific mosquito red light responses. AgCRY1 and DmCRY mediate intensity-dependent avoidance behavior to UV light at different light intensity thresholds, whereas AeCRY1 does not, thus mimicking mosquito and fly behaviors. These findings highlight CRY as a key non-image-forming visual photoreceptor that mediates physiological and behavioral light responses in a species-specific fashion.

One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.

Nocturnal Anopheles mosquitoes exhibit strong behavioral avoidance to blue-light while diurnal Aedes mosquitoes are behaviorally attracted to blue-light and a wide range of other wavelengths of light. To determine the molecular mechanism of these effects, we expressed light-sensing Anopheles gambiae (AgCRY1) and Aedes aegypti (AeCRY1) Cryptochrome 1 (CRY) genes under a crypGAL4-24 driver line in a mutant Drosophila genetic background lacking native functional CRY, then tested behavioral and electrophysiological effects of mosquito CRY expression relative to positive and negative CRY control conditions. Neither mosquito CRY stops the circadian clock as shown by robust circadian behavioral rhythmicity in constant darkness in flies expressing either AgCRY1 or AeCRY1. AgCRY1 and AeCRY1 both mediate acute increases in large ventral lateral neuronal firing rate evoked by 450 nm blue-light, corresponding to CRY's peak absorbance in its base state, indicating that both mosquito CRYs are functional, however, AgCRY1 mediates significantly stronger sustained electrophysiological light-evoked depolarization in response to blue-light relative to AeCRY1. In contrast, neither AgCRY1 nor AeCRY1 expression mediates measurable increases in large ventral lateral neuronal firing rates in response to 405 nm violet-light, the peak of the Rhodopsin-7 photoreceptor that is co-expressed in the large lateral ventral neurons. These results are consistent with the known action spectra of type 1 CRYs and lack of response in cry-null controls. AgCRY1 and AeCRY1 expressing flies show behavioral attraction to low intensity blue-light, but AgCRY1 expressing flies show behavioral avoidance to higher intensity blue-light. These results show that nocturnal and diurnal mosquito Cryptochrome 1 proteins mediate differential physiological and behavioral responses to blue-light that are consistent with species-specific mosquito behavior.

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

BackgroundWith the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step.MethodsIn this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding.ResultsWe found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods.Comparison with existing methodsOur simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods.ConclusionWe conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.

High-dimensionality is ubiquitous in various scientific fields such as imaging genetics, where a deluge of functional and structural data on brain-relevant genetic polymorphisms are investigated. It is crucial to identify which genetic variations are consequential in identifying neurological features of brain connectivity compared to merely random noise. Statistical inference in high-dimensional settings poses multiple challenges involving analytical and computational complexity. A widely implemented strategy in addressing inference goals is penalized inference. In particular, the role of the ridge penalty in high-dimensional prediction and estimation has been actively studied in the past several years. This study focuses on ridge-penalized tests in high-dimensional hypothesis testing problems by proposing and examining a class of methods for choosing the optimal ridge penalty. We present our findings on strategies to improve the statistical power of ridge-penalized tests and what determines the optimal ridge penalty for hypothesis testing. The application of our work to an imaging genetics study and biological research will be presented.

Myelination plays an important role in cognitive development and in demyelinating diseases like multiple sclerosis (MS), where failure of remyelination promotes permanent neuro-axonal damage. Modification of cell surface receptors with branched N-glycans coordinates cell growth and differentiation by controlling glycoprotein clustering, signaling, and endocytosis. GlcNAc is a rate-limiting metabolite for N-glycan branching. Here we report that GlcNAc and N-glycan branching trigger oligodendrogenesis from precursor cells by inhibiting platelet-derived growth factor receptor-α cell endocytosis. Supplying oral GlcNAc to lactating mice drives primary myelination in newborn pups via secretion in breast milk, whereas genetically blocking N-glycan branching markedly inhibits primary myelination. In adult mice with toxin (cuprizone)-induced demyelination, oral GlcNAc prevents neuro-axonal damage by driving myelin repair. In MS patients, endogenous serum GlcNAc levels inversely correlated with imaging measures of demyelination and microstructural damage. Our data identify N-glycan branching and GlcNAc as critical regulators of primary myelination and myelin repair and suggest that oral GlcNAc may be neuroprotective in demyelinating diseases like MS.

Subanesthetic ketamine evokes rapid and long-lasting antidepressant effects in human patients. The mechanism for ketamine's effects remains elusive, but ketamine may broadly modulate brain plasticity processes. We show that single-dose ketamine reactivates adult mouse visual cortical plasticity and promotes functional recovery of visual acuity defects from amblyopia. Ketamine specifically induces downregulation of neuregulin-1 (NRG1) expression in parvalbumin-expressing (PV) inhibitory neurons in mouse visual cortex. NRG1 downregulation in PV neurons co-tracks both the fast onset and sustained decreases in synaptic inhibition to excitatory neurons, along with reduced synaptic excitation to PV neurons in vitro and in vivo following a single ketamine treatment. These effects are blocked by exogenous NRG1 as well as PV targeted receptor knockout. Thus, ketamine reactivation of adult visual cortical plasticity is mediated through rapid and sustained cortical disinhibition via downregulation of PV-specific NRG1 signaling. Our findings reveal the neural plasticity-based mechanism for ketamine-mediated functional recovery from adult amblyopia.

Resting-state functional connectivity profiles have been increasingly shown to be important endophenotypes that are tightly linked to human cognitive functions and psychiatric diseases, yet the genetic architecture of this multidimensional trait is barely understood. Using a unique sample of 1,704 unrelated, young and healthy Chinese Han individuals, we revealed a significant heritability of functional connectivity patterns in the whole brain and several subnetworks. We further proposed a partitioned heritability analysis for multidimensional functional connectivity patterns, which revealed the common and unique enrichment patterns of the genetic contributions to brain connectivity patterns for several gene sets linked to brain functions, including the genes expressed preferentially in the central nervous system and those associated with intelligence, educational attainment, attention-deficit/hyperactivity disorder, and schizophrenia. These results for the first time reveal the genetic architecture of multidimensional brain connectivity patterns across different networks and advance our understanding of the complex relationship between gene sets, neural networks, and behaviors.

We analyse more than 500 000 songs released in the UK between 1985 and 2015 to understand the dynamics of success (defined as 'making it' into the top charts), correlate success with acoustic features and explore the predictability of success. Several multi-decadal trends have been uncovered. For example, there is a clear downward trend in 'happiness' and 'brightness', as well as a slight upward trend in 'sadness'. Furthermore, songs are becoming less 'male'. Interestingly, successful songs exhibit their own distinct dynamics. In particular, they tend to be 'happier', more 'party-like', less 'relaxed' and more 'female' than most. The difference between successful and average songs is not straightforward. In the context of some features, successful songs pre-empt the dynamics of all songs, and in others they tend to reflect the past. We used random forests to predict the success of songs, first based on their acoustic features, and then adding the 'superstar' variable (informing us whether the song's artist had appeared in the top charts in the near past). This allowed quantification of the contribution of purely musical characteristics in the songs' success, and suggested the time scale of fashion dynamics in popular music.

Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying group-specific sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered "group-specific" in our study. Our main purpose is to discover group-specific sequence regions between control and case groups as disease-associated markers. We developed a long k-mer (k ≥ 30 bps)-based computational pipeline to detect group-specific sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide de novo assembly. We called our method MetaGO: Group-specific oligonucleotide analysis for metagenomic samples. An open-source pipeline on Apache Spark was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified group-specific markers. In the simulated dataset, 99.11% of group-specific logical 40-mers covered 98.89% disease-specific regions from the disease-associated strain. In addition, 97.90% of group-specific numerical 40-mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 group-specific 40-mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 group-specific features yielded a higher AUC (from ∼0.8 to ∼0.9) than that of previous studies. All group-specific 40-mers were present in LC patients, but not healthy controls. All the assembled 11 LC-specific sequences can be mapped to two strains of Veillonella parvula: UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying group-specific k-mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO.

Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.

In a recent study on multiple sclerosis (MS), we observed additive effects and epistatic interactions between variants of four genes that converge to induce T-cell hyperactivity by altering Asn-(N)-linked protein glycosylation: namely, the Golgi enzyme MGAT1, cytotoxic T-lymphocyte antigen 4 (CTLA-4), interleukin-2 receptor-α (IL2RA) and interleukin-7 receptor-α (IL7RA). As the CTLA-4, IL2RA and IL7RA variants are associated with type 1 diabetes (T1D), we examined for joint effects in T1D. Employing a novel conditional logistic regression for family-based data sets, epistatic and additive effects were observed using 1423 multiplex families from the Type 1 Diabetes Genetic Consortium data set. The IL2RA and IL7RA variants had univariate association in MS and T1D, whereas the MGAT1 and CTLA-4 variants associated with only MS or T1D, respectively. However, similar to MS, the MGAT1 variant haplotype interacted with CTLA4 (P=0.03), and a combination of IL2RA and IL7RA (P=0.01). The joint effects of MGAT1, CTLA4, IL2RA, IL7RA and the two interactions using a multiple conditional logistic regression were statistically highly significant (P

Assumptions regarding the true underlying genetic model, or mode of inheritance, are necessary when quantifying genetic associations with disease phenotypes. Here we propose new methods to ascertain the underlying genetic model from parental data in family-based association studies. Specifically, for parental mating-type data, we propose a novel statistic to test whether the underlying genetic model is additive, dominant, or recessive; for parental genotype-phenotype data, we propose three strategies to determine the true mode of inheritance. We illustrate how to incorporate the information gleaned from these strategies into family-based association tests. Because family-based association tests are conducted conditional on parental genotypes, the type I error rate of these procedures is not inflated by the information learned from parental data. This result holds even if such information is weak or when the assumption of Hardy-Weinberg equilibrium is violated. Our simulations demonstrate that incorporating parental data into family-based association tests can improve power under common inheritance models. The application of our proposed methods to a candidate-gene study of type 1 diabetes successfully detects a recessive effect in MGAT5 that would otherwise be missed by conventional family-based association tests.

Deficiency of the Golgi N-glycan branching enzyme Mgat5 in mice promotes T cell hyperactivity, endocytosis of CTLA-4 and autoimmunity, including a spontaneous multiple sclerosis (MS)-like disease. Multiple genetic and environmental MS risk factors lower N-glycan branching in T cells. These include variants in interleukin-2 receptor-α (IL2RA), interleukin-7 receptor-α (IL7RA), and MGAT1, a Golgi branching enzyme upstream of MGAT5, as well as vitamin D3 deficiency and Golgi substrate metabolism. Here we describe linked intronic variants of MGAT5 that are associated with reduced N-glycan branching, CTLA-4 surface expression and MS (p=5.79×10(-9), n=7,741), the latter additive with the MGAT1, IL2RA and IL7RA MS risk variants (p=1.76×10(-9), OR=0.67-1.83, n=3,518).

PurposeDuring anesthesia, maneuvers which cause the least disturbance of cerebral oxygenation with the greatest decrease in intracranial pressure would be most beneficial to patients with intracranial hypertension. Both head-up tilt (HUT) and hyperventilation are used to decrease brain bulk, and both may be associated with decreases in cerebral oxygenation. In this observational study, our null hypothesis was that the impact of HUT and hyperventilation on cerebral tissue oxygen saturation (SctO2) and cerebral blood volume (CBV) are comparable.MethodsSurgical patients without neurological disease were anesthetized with propofol-remifentanil. Before the start of surgery, frequency-domain near-infrared spectroscopy was used to measure SctO2 and CBV at the supine position, at the 30° head-up and head-down positions, as well as during hypoventilation and hyperventilation.ResultsThirty-three patients were studied. Both HUT and hyperventilation induced small decreases in SctO2 [3.5 (2.6)%; P

BackgroundRecently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.ResultsWe describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA.ConclusionsThe SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

How environmental factors combine with genetic risk at the molecular level to promote complex trait diseases such as multiple sclerosis (MS) is largely unknown. In mice, N-glycan branching by the Golgi enzymes Mgat1 and/or Mgat5 prevents T cell hyperactivity, cytotoxic T-lymphocyte antigen 4 (CTLA-4) endocytosis, spontaneous inflammatory demyelination and neurodegeneration, the latter pathologies characteristic of MS. Here we show that MS risk modulators converge to alter N-glycosylation and/or CTLA-4 surface retention conditional on metabolism and vitamin D(3), including genetic variants in interleukin-7 receptor-α (IL7RA*C), interleukin-2 receptor-α (IL2RA*T), MGAT1 (IV(A)V(T-T)) and CTLA-4 (Thr17Ala). Downregulation of Mgat1 by IL7RA*C and IL2RA*T is opposed by MGAT1 (IV(A)V(T-T)) and vitamin D(3), optimizing branching and mitigating MS risk when combined with enhanced CTLA-4 N-glycosylation by CTLA-4 Thr17. Our data suggest a molecular mechanism in MS whereby multiple environmental and genetic inputs lead to dysregulation of a final common pathway, namely N-glycosylation.

We describe an algorithm based on shrinkage in the curvelet domain to attenuate speckles in optical coherence tomography (OCT) images. The algorithm exploits the curvelet transform's sparse representation of edge discontinuities that are common in OCT images and its ability to map signals and noise into different areas in the curvelet domain. The speckle attenuation is controlled by a single parameter that determines the threshold in the curvelet domain. Applying the algorithm to OCT images shows significant improvement of image quality.

Background: Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information. Results: We propose a novel genotype calling framework. In this framework, we consider the signal intensities and underlying LD structure of SNPs simultaneously by estimating both cluster parameters and haplotype frequencies. As a result, our new method outperforms some existing algorithms in terms of both call rates and genotyping accuracy. Our studies also suggest that jointly analyzing multiple SNPs in LD provides more accurate estimation of haplotypes than haplotype reconstruction methods that only use called genotypes. Conclusion: Our study demonstrates that jointly analyzing signal intensities and LD structure of multiple SNPs is a better way to determine genotypes and estimate LD parameters.