Jere Koskela, Paul A. Jenkins, Adam M. Johansen, Dario Spanò.

Source: The Annals of Statistics, Volume 48, Number 1, 560--583.

Abstract:
We study weighted particle systems in which new generations are resampled from current particles with probabilities proportional to their weights. This covers a broad class of sequential Monte Carlo (SMC) methods, widely-used in applied statistics and cognate disciplines. We consider the genealogical tree embedded into such particle systems, and identify conditions, as well as an appropriate time-scaling, under which they converge to the Kingman $n$-coalescent in the infinite system size limit in the sense of finite-dimensional distributions. Thus, the tractable $n$-coalescent can be used to predict the shape and size of SMC genealogies, as we illustrate by characterising the limiting mean and variance of the tree height. SMC genealogies are known to be connected to algorithm performance, so that our results are likely to have applications in the design of new methods as well. Our conditions for convergence are strong, but we show by simulation that they do not appear to be necessary.

Chengchun Shi, Rui Song, Zhao Chen, Runze Li.

Source: The Annals of Statistics, Volume 47, Number 5, 2671--2703.

Abstract:
This paper is concerned with testing linear hypotheses in high dimensional generalized linear models. To deal with linear hypotheses, we first propose the constrained partial regularization method and study its statistical properties. We further introduce an algorithm for solving regularization problems with folded-concave penalty functions and linear constraints. To test linear hypotheses, we propose a partial penalized likelihood ratio test, a partial penalized score test and a partial penalized Wald test. We show that the limiting null distributions of these three test statistics are $chi^{2}$ distribution with the same degrees of freedom, and under local alternatives, they asymptotically follow noncentral $chi^{2}$ distributions with the same degrees of freedom and noncentral parameter, provided the number of parameters involved in the test hypothesis grows to $infty$ at a certain rate. Simulation studies are conducted to examine the finite sample performance of the proposed tests. Empirical analysis of a real data example is used to illustrate the proposed testing procedures.

Anru Zhang, Lawrence D. Brown, T. Tony Cai.

Source: The Annals of Statistics, Volume 47, Number 5, 2538--2566.

Abstract:
We propose a general semi-supervised inference framework focused on the estimation of the population mean. As usual in semi-supervised settings, there exists an unlabeled sample of covariate vectors and a labeled sample consisting of covariate vectors along with real-valued responses (“labels”). Otherwise, the formulation is “assumption-lean” in that no major conditions are imposed on the statistical or functional form of the data. We consider both the ideal semi-supervised setting where infinitely many unlabeled samples are available, as well as the ordinary semi-supervised setting in which only a finite number of unlabeled samples is available. Estimators are proposed along with corresponding confidence intervals for the population mean. Theoretical analysis on both the asymptotic distribution and $ell_{2}$-risk for the proposed procedures are given. Surprisingly, the proposed estimators, based on a simple form of the least squares method, outperform the ordinary sample mean. The simple, transparent form of the estimator lends confidence to the perception that its asymptotic improvement over the ordinary sample mean also nearly holds even for moderate size samples. The method is further extended to a nonparametric setting, in which the oracle rate can be achieved asymptotically. The proposed estimators are further illustrated by simulation studies and a real data example involving estimation of the homeless population.

Qiyang Han, Tengyao Wang, Sabyasachi Chatterjee, Richard J. Samworth.

Source: The Annals of Statistics, Volume 47, Number 5, 2440--2471.

Abstract:
We study the least squares regression function estimator over the class of real-valued functions on $[0,1]^{d}$ that are increasing in each coordinate. For uniformly bounded signals and with a fixed, cubic lattice design, we establish that the estimator achieves the minimax rate of order $n^{-min{2/(d+2),1/d}}$ in the empirical $L_{2}$ loss, up to polylogarithmic factors. Further, we prove a sharp oracle inequality, which reveals in particular that when the true regression function is piecewise constant on $k$ hyperrectangles, the least squares estimator enjoys a faster, adaptive rate of convergence of $(k/n)^{min(1,2/d)}$, again up to polylogarithmic factors. Previous results are confined to the case $dleq2$. Finally, we establish corresponding bounds (which are new even in the case $d=2$) in the more challenging random design setting. There are two surprising features of these results: first, they demonstrate that it is possible for a global empirical risk minimisation procedure to be rate optimal up to polylogarithmic factors even when the corresponding entropy integral for the function class diverges rapidly; second, they indicate that the adaptation rate for shape-constrained estimators can be strictly worse than the parametric rate.

Yen-Chi Chen.

Source: The Annals of Statistics, Volume 47, Number 4, 2174--2203.

Abstract:
In this paper we study the $alpha $-cluster tree ($alpha $-tree) under both singular and nonsingular measures. The $alpha $-tree uses probability contents within a set created by the ordering of points to construct a cluster tree so that it is well defined even for singular measures. We first derive the convergence rate for a density level set around critical points, which leads to the convergence rate for estimating an $alpha $-tree under nonsingular measures. For singular measures, we study how the kernel density estimator (KDE) behaves and prove that the KDE is not uniformly consistent but pointwise consistent after rescaling. We further prove that the estimated $alpha $-tree fails to converge in the $L_{infty }$ metric but is still consistent under the integrated distance. We also observe a new type of critical points—the dimensional critical points (DCPs)—of a singular measure. DCPs are points that contribute to cluster tree topology but cannot be defined using density gradient. Building on the analysis of the KDE and DCPs, we prove the topological consistency of an estimated $alpha $-tree.

Trang Quynh Nguyen, Elizabeth A. Stuart.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 518--520.

Zhonghua Liu, Ian Barnett, Xihong Lin.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 433--451.

Abstract:
Principal component analysis (PCA) is a popular method for dimension reduction in unsupervised multivariate analysis. However, existing ad hoc uses of PCA in both multivariate regression (multiple outcomes) and multiple regression (multiple predictors) lack theoretical justification. The differences in the statistical properties of PCAs in these two regression settings are not well understood. In this paper we provide theoretical results on the power of PCA in genetic association testings in both multiple phenotype and SNP-set settings. The multiple phenotype setting refers to the case when one is interested in studying the association between a single SNP and multiple phenotypes as outcomes. The SNP-set setting refers to the case when one is interested in studying the association between multiple SNPs in a SNP set and a single phenotype as the outcome. We demonstrate analytically that the properties of the PC-based analysis in these two regression settings are substantially different. We show that the lower order PCs, that is, PCs with large eigenvalues, are generally preferred and lead to a higher power in the SNP-set setting, while the higher-order PCs, that is, PCs with small eigenvalues, are generally preferred in the multiple phenotype setting. We also investigate the power of three other popular statistical methods, the Wald test, the variance component test and the minimum $p$-value test, in both multiple phenotype and SNP-set settings. We use theoretical power, simulation studies, and two real data analyses to validate our findings.

Wanghuan Chu, Runze Li, Jingyuan Liu, Matthew Reimherr.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 276--298.

Abstract:
Motivated by an empirical analysis of data from a genome-wide association study on obesity, measured by the body mass index (BMI), we propose a two-step gene-detection procedure for generalized varying coefficient mixed-effects models with ultrahigh dimensional covariates. The proposed procedure selects significant single nucleotide polymorphisms (SNPs) impacting the mean BMI trend, some of which have already been biologically proven to be “fat genes.” The method also discovers SNPs that significantly influence the age-dependent variability of BMI. The proposed procedure takes into account individual variations of genetic effects and can also be directly applied to longitudinal data with continuous, binary or count responses. We employ Monte Carlo simulation studies to assess the performance of the proposed method and further carry out causal inference for the selected SNPs.

Kerstin Spitzer, Marta Pelizzola, Andreas Futschik.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 202--220.

Abstract:
Evolve and resequence studies provide a popular approach to simulate evolution in the lab and explore its genetic basis. In this context, Pearson’s chi-square test, Fisher’s exact test as well as the Cochran–Mantel–Haenszel test are commonly used to infer genomic positions affected by selection from temporal changes in allele frequency. However, the null model associated with these tests does not match the null hypothesis of actual interest. Indeed, due to genetic drift and possibly other additional noise components such as pool sequencing, the null variance in the data can be substantially larger than accounted for by these common test statistics. This leads to $p$-values that are systematically too small and, therefore, a huge number of false positive results. Even, if the ranking rather than the actual $p$-values is of interest, a naive application of the mentioned tests will give misleading results, as the amount of overdispersion varies from locus to locus. We therefore propose adjusted statistics that take the overdispersion into account while keeping the formulas simple. This is particularly useful in genome-wide applications, where millions of SNPs can be handled with little computational effort. We then apply the adapted test statistics to real data from Drosophila and investigate how information from intermediate generations can be included when available. We also discuss further applications such as genome-wide association studies based on pool sequencing data and tests for local adaptation.

Joe Watson, James V. Zidek, Gavin Shaddick.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2662--2700.

Abstract:
This paper presents a general model framework for detecting the preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a site-selection process that considers where and when sites are placed to measure the process. The environmental process may be spatial, temporal or spatio-temporal in nature. By sharing random effects between the two processes, the joint model is able to establish whether site placement was stochastically dependent of the environmental process under study. Furthermore, if stochastic dependence is identified between the two processes, then inferences about the probability distribution of the spatio-temporal process will change, as will predictions made of the process across space and time. The embedding into a spatio-temporal framework also allows for the modelling of the dynamic site-selection process itself. Real-world factors affecting both the size and location of the network can be easily modelled and quantified. Depending upon the choice of the population of locations considered for selection across space and time under the site-selection process, different insights about the precise nature of preferential sampling can be obtained. The general framework developed in the paper is designed to be easily and quickly fit using the R-INLA package. We apply this framework to a case study involving particulate air pollution over the UK where a major reduction in the size of a monitoring network through time occurred. It is demonstrated that a significant response-biased reduction in the air quality monitoring network occurred, namely the relocation of monitoring sites to locations with the highest pollution levels, and the routine removal of sites at locations with the lowest. We also show that the network was consistently unrepresenting levels of particulate matter seen across much of GB throughout the operating life of the network. Finally we show that this may have led to a severe overreporting of the population-average exposure levels experienced across GB. This could have great impacts on estimates of the health effects of black smoke levels.

Bret Zeldow, Vincent Lo Re III, Jason Roy.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1989--2010.

Abstract:
Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing nonlinearities between an outcome and covariates and interactions among covariates. We extend BART to a semiparametric regression framework in which the conditional expectation of an outcome is a function of treatment, its effect modifiers, and confounders. The confounders are allowed to have unspecified functional form, while treatment and effect modifiers that are directly related to the research question are given a linear form. The result is a Bayesian semiparametric linear regression model where the posterior distribution of the parameters of the linear part can be interpreted as in parametric Bayesian regression. This is useful in situations where a subset of the variables are of substantive interest and the others are nuisance variables that we would like to control for. An example of this occurs in causal modeling with the structural mean model (SMM). Under certain causal assumptions, our method can be used as a Bayesian SMM. Our methods are demonstrated with simulation studies and an application to dataset involving adults with HIV/Hepatitis C coinfection who newly initiate antiretroviral therapy. The methods are available in an R package called semibart.

Galatia Cleanthous, Athanasios G. Georgiadis, Gerard Kerkyacharian, Pencho Petrushev, Dominique Picard.

Source: Bernoulli, Volume 26, Number 3, 1832--1862.

Abstract:
We consider the problem of estimating the density of observations taking values in classical or nonclassical spaces such as manifolds and more general metric spaces. Our setting is quite general but also sufficiently rich in allowing the development of smooth functional calculus with well localized spectral kernels, Besov regularity spaces, and wavelet type systems. Kernel and both linear and nonlinear wavelet density estimators are introduced and studied. Convergence rates for these estimators are established and discussed.

Sumit Mukherjee.

Source: Bernoulli, Volume 26, Number 2, 1016--1043.

Abstract:
A sufficient criterion for “non-degeneracy” is given for Exponential Random Graph Models on sparse graphs with sufficient statistics which are functions of the degree sequence. This criterion explains why statistics such as alternating $k$-star are non-degenerate, whereas subgraph counts are degenerate. It is further shown that this criterion is “almost” tight. Existence of consistent estimates is then proved for non-degenerate Exponential Random Graph Models.

Jie Yen Fan, Kais Hamza, Peter Jagers, Fima Klebaner.

Source: Bernoulli, Volume 26, Number 2, 893--926.

Abstract:
We consider a family of general branching processes with reproduction parameters depending on the age of the individual as well as the population age structure and a parameter $K$, which may represent the carrying capacity. These processes are Markovian in the age structure. In a previous paper ( Proc. Steklov Inst. Math. 282 (2013) 90–105), the Law of Large Numbers as $K o infty $ was derived. Here we prove the central limit theorem, namely the weak convergence of the fluctuation processes in an appropriate Skorokhod space. We also show that the limit is driven by a stochastic partial differential equation.

Elena Issoglio, Francesco Russo.

Source: Bernoulli, Volume 26, Number 1, 728--766.

Abstract:
In this paper, we investigate BSDEs where the driver contains a distributional term (in the sense of generalised functions) and derive general Feynman–Kac formulae related to these BSDEs. We introduce an integral operator to give sense to the equation and then we show the existence of a strong solution employing results on a related PDE. Due to the irregularity of the driver, the $Y$-component of a couple $(Y,Z)$ solving the BSDE is not necessarily a semimartingale but a weak Dirichlet process.

Tobias Zwingmann, Hajo Holzmann.

Source: Bernoulli, Volume 26, Number 1, 323--351.

Abstract:
We show weak convergence of quantile and expectile processes to Gaussian limit processes in the space of bounded functions endowed with an appropriate semimetric which is based on the concepts of epi- and hypo- convergence as introduced in A. Bücher, J. Segers and S. Volgushev (2014), ‘ When Uniform Weak Convergence Fails: Empirical Processes for Dependence Functions and Residuals via Epi- and Hypographs ’, Annals of Statistics 42 . We impose assumptions for which it is known that weak convergence with respect to the supremum norm generally fails to hold. For quantiles, we consider stationary observations, where the marginal distribution function is assumed to be strictly increasing and continuous except for finitely many points and to admit strictly positive – possibly infinite – left- and right-sided derivatives. For expectiles, we focus on independent and identically distributed (i.i.d.) observations. Only a finite second moment and continuity at the boundary points but no further smoothness properties of the distribution function are required. We also show consistency of the bootstrap for this mode of convergence in the i.i.d. case for quantiles and expectiles.

Kay Bogerd, Rui M. Castro, Remco van der Hofstad.

Source: Bernoulli, Volume 26, Number 1, 253--285.

Abstract:
We study the asymptotic behavior of the clique number in rank-1 inhomogeneous random graphs, where edge probabilities between vertices are roughly proportional to the product of their vertex weights. We show that the clique number is concentrated on at most two consecutive integers, for which we provide an expression. Interestingly, the order of the clique number is primarily determined by the overall edge density, with the inhomogeneity only affecting multiplicative constants or adding at most a $log log (n)$ multiplicative factor. For sparse enough graphs the clique number is always bounded and the effect of inhomogeneity completely vanishes.

Genetic genealogy -- Handbooks, manuals, etc.

Rungie (Family)

Jarno Vanhatalo, Marcelo Hartmann, Lari Veneranta.

Source: Bayesian Analysis, Volume 15, Number 2, 415--447.

Abstract:
Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description for species distribution response along environmental covariates and the spatial random effect that captures deviations from the distribution patterns explained by environmental covariates. Joint species distribution models (JSDMs) additionally include interspecific correlations which have been shown to improve their descriptive and predictive performance compared to single species models. However, current JSDMs are restricted to hierarchical generalized linear modeling framework. Their limitation is that parametric models have trouble in explaining changes in abundance due, for example, highly non-linear physical tolerance limits which is particularly important when predicting species distribution in new areas or under scenarios of environmental change. On the other hand, semi-parametric response functions have been shown to improve the predictive performance of SDMs in these tasks in single species models. Here, we propose JSDMs where the responses to environmental covariates are modeled with additive multivariate Gaussian processes coded as linear models of coregionalization. These allow inference for wide range of functional forms and interspecific correlations between the responses. We propose also an efficient approach for inference with Laplace approximation and parameterization of the interspecific covariance matrices on the Euclidean space. We demonstrate the benefits of our model with two small scale examples and one real world case study. We use cross-validation to compare the proposed model to analogous semi-parametric single species models and parametric single and joint species models in interpolation and extrapolation tasks. The proposed model outperforms the alternative models in all cases. We also show that the proposed model can be seen as an extension of the current state-of-the-art JSDMs to semi-parametric models.

Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, Wing Hung Wong.

Source: Statistical Science, Volume 35, Number 1, 2--13.

Abstract:
Unsupervised methods, including clustering methods, are essential to the analysis of single-cell genomic data. Model-based clustering methods are under-explored in the area of single-cell genomics, and have the advantage of quantifying the uncertainty of the clustering result. Here we develop a model-based approach for the integrative analysis of single-cell chromatin accessibility and gene expression data. We show that combining these two types of data, we can achieve a better separation of the underlying cell types. An efficient Markov chain Monte Carlo algorithm is also developed.

Qingyuan Zhao, Jingshu Wang, Wes Spiller, Jack Bowden, Dylan S. Small.

Source: Statistical Science, Volume 34, Number 2, 317--333.

Abstract:
Instrumental variable analysis is a widely used method to estimate causal effects in the presence of unmeasured confounding. When the instruments, exposure and outcome are not measured in the same sample, Angrist and Krueger ( J. Amer. Statist. Assoc. 87 (1992) 328–336) suggested to use two-sample instrumental variable (TSIV) estimators that use sample moments from an instrument-exposure sample and an instrument-outcome sample. However, this method is biased if the two samples are from heterogeneous populations so that the distributions of the instruments are different. In linear structural equation models, we derive a new class of TSIV estimators that are robust to heterogeneous samples under the key assumption that the structural relations in the two samples are the same. The widely used two-sample two-stage least squares estimator belongs to this class. It is generally not asymptotically efficient, although we find that it performs similarly to the optimal TSIV estimator in most practical situations. We then attempt to relax the linearity assumption. We find that, unlike one-sample analyses, the TSIV estimator is not robust to misspecified exposure model. Additionally, to nonparametrically identify the magnitude of the causal effect, the noise in the exposure must have the same distributions in the two samples. However, this assumption is in general untestable because the exposure is not observed in one sample. Nonetheless, we may still identify the sign of the causal effect in the absence of homogeneity of the noise.

Víctor Elvira, Luca Martino, David Luengo, Mónica F. Bugallo.

Source: Statistical Science, Volume 34, Number 1, 129--155.

Abstract:
Importance sampling (IS) methods are broadly used to approximate posterior distributions or their moments. In the standard IS approach, samples are drawn from a single proposal distribution and weighted adequately. However, since the performance in IS depends on the mismatch between the targeted and the proposal distributions, several proposal densities are often employed for the generation of samples. Under this multiple importance sampling (MIS) scenario, extensive literature has addressed the selection and adaptation of the proposal distributions, interpreting the sampling and weighting steps in different ways. In this paper, we establish a novel general framework with sampling and weighting procedures when more than one proposal is available. The new framework encompasses most relevant MIS schemes in the literature, and novel valid schemes appear naturally. All the MIS schemes are compared and ranked in terms of the variance of the associated estimators. Finally, we provide illustrative examples revealing that, even with a good choice of the proposal densities, a careful interpretation of the sampling and weighting procedures can make a significant difference in the performance of the method.

Irune Diaz-Aparicio
Feb 12, 2020; 40:1453-1482
Development Plasticity Repair

Amanda Avona
May 29, 2019; 39:4323-4331
Systems/Circuits

CJ Pike
Apr 1, 1993; 13:1676-1687
Articles

Zhengui Xia
Sep 1, 1996; 16:5425-5436
Articles

HG Kuhn
Mar 15, 1996; 16:2027-2033
Articles

Synaptic dysfunction provoking dysregulated cortical neural circuits is currently hypothesized as a key pathophysiological process underlying clinical manifestations in Alzheimer's disease and related neurodegenerative tauopathies. Here, we conducted PET along with postmortem assays to investigate time course changes of excitatory and inhibitory synaptic constituents in an rTg4510 mouse model of tauopathy, which develops tau pathologies leading to noticeable brain atrophy at 5-6 months of age. Both male and female mice were analyzed in this study. We observed that radiosignals derived from [¹¹C]flumazenil, a tracer for benzodiazepine receptor, in rTg4510 mice were significantly lower than the levels in nontransgenic littermates at 2-3 months of age. In contrast, retentions of (E)-[¹¹C]ABP688, a tracer for mGluR5, were unaltered relative to controls at 2 months of age but then gradually declined with aging in parallel with progressive brain atrophy. Biochemical and immunohistochemical assessment of postmortem brain tissues demonstrated that inhibitory, but not excitatory, synaptic constituents selectively diminished without overt loss of somas of GABAergic interneurons in the neocortex and hippocampus of rTg4510 mice at 2 months of age, which was concurrent with enhanced immunoreactivity of cFos, a well-characterized immediate early gene, suggesting that impaired inhibitory neurotransmission may cause hyperexcitability of cortical circuits. Our findings indicate that tau-induced disruption of the inhibitory synapse may be a critical trigger of progressive neurodegeneration, resulting in massive neuronal loss, and PET assessments of inhibitory versus excitatory synapses potentially offer in vivo indices for hyperexcitability and excitotoxicity early in the etiologic pathway of neurodegenerative tauopathies.

SIGNIFICANCE STATEMENT In this study, we examined the in vivo status of excitatory and inhibitory synapses in the brain of the rTg4510 tauopathy mouse model by PET imaging with (E)-[¹¹C]ABP688 and [¹¹C]flumazenil, respectively. We identified inhibitory synapse as being significantly dysregulated before brain atrophy at 2 months of age, while excitatory synapse stayed relatively intact at this stage. In line with this observation, postmortem assessment of brain tissues demonstrated selective attenuation of inhibitory synaptic constituents accompanied by the upregulation of cFos before the formation of tau pathology in the forebrain at young ages. Our findings indicate that selective degeneration of inhibitory synapse with hyperexcitability in the cortical circuit constitutes the critical early pathophysiology of tauopathy.

To determine whether Cav1.2 voltage-gated Ca²⁺ channels contribute to astrocyte activation, we generated an inducible conditional knock-out mouse in which the Cav1.2 α subunit was deleted in GFAP-positive astrocytes. This astrocytic Cav1.2 knock-out mouse was tested in the cuprizone model of myelin injury and repair which causes astrocyte and microglia activation in the absence of a lymphocytic response. Deletion of Cav1.2 channels in GFAP-positive astrocytes during cuprizone-induced demyelination leads to a significant reduction in the degree of astrocyte and microglia activation and proliferation in mice of either sex. Concomitantly, the production of proinflammatory factors such as TNFα, IL1β and TGFβ1 was significantly decreased in the corpus callosum and cortex of Cav1.2 knock-out mice through demyelination. Furthermore, this mild inflammatory environment promotes oligodendrocyte progenitor cells maturation and myelin regeneration across the remyelination phase of the cuprizone model. Similar results were found in animals treated with nimodipine, a Cav1.2 Ca²⁺ channel inhibitor with high affinity to the CNS. Mice of either sex injected with nimodipine during the demyelination stage of the cuprizone treatment displayed a reduced number of reactive astrocytes and showed a faster and more efficient brain remyelination. Together, these results indicate that Cav1.2 Ca²⁺ channels play a crucial role in the induction and proliferation of reactive astrocytes during demyelination; and that attenuation of astrocytic voltage-gated Ca²⁺ influx may be an effective therapy to reduce brain inflammation and promote myelin recovery in demyelinating diseases.

SIGNIFICANCE STATEMENT Reducing voltage-gated Ca²⁺ influx in astrocytes during brain demyelination significantly attenuates brain inflammation and astrocyte reactivity. Furthermore, these changes promote myelin restoration and oligodendrocyte maturation throughout remyelination.

The tuberal hypothalamus is comprised of the dorsomedial, ventromedial, and arcuate nuclei, as well as parts of the lateral hypothalamic area, and it governs a wide range of physiologies. During neurogenesis, tuberal hypothalamic neurons are thought to be born in a dorsal-to-ventral and outside-in pattern, although the accuracy of this description has been questioned over the years. Moreover, the intrinsic factors that control the timing of neurogenesis in this region are poorly characterized. Proneural genes, including Achate-scute-like 1 (Ascl1) and Neurogenin 3 (Neurog3) are widely expressed in hypothalamic progenitors and contribute to lineage commitment and subtype-specific neuronal identifies, but the potential role of Neurogenin 2 (Neurog2) remains unexplored. Birthdating in male and female mice showed that tuberal hypothalamic neurogenesis begins as early as E9.5 in the lateral hypothalamic and arcuate and rapidly expands to dorsomedial and ventromedial neurons by E10.5, peaking throughout the region by E11.5. We confirmed an outside-in trend, except for neurons born at E9.5, and uncovered a rostrocaudal progression but did not confirm a dorsal-ventral patterning to tuberal hypothalamic neuronal birth. In the absence of Neurog2, neurogenesis stalls, with a significant reduction in early-born BrdU⁺ cells but no change at later time points. Further, the loss of Ascl1 yielded a similar delay in neuronal birth, suggesting that Ascl1 cannot rescue the loss of Neurog2 and that these proneural genes act independently in the tuberal hypothalamus. Together, our findings show that Neurog2 functions as a classical proneural gene to regulate the temporal progression of tuberal hypothalamic neurogenesis.

SIGNIFICANCE STATEMENT Here, we investigated the general timing and pattern of neurogenesis within the tuberal hypothalamus. Our results confirmed an outside-in trend of neurogenesis and uncovered a rostrocaudal progression. We also showed that Neurog2 acts as a classical proneural gene and is responsible for regulating the birth of early-born neurons within the ventromedial hypothalamus, acting independently of Ascl1. In addition, we revealed a role for Neurog2 in cell fate specification and differentiation of ventromedial -specific neurons. Last, Neurog2 does not have cross-inhibitory effects on Neurog1, Neurog3, and Ascl1. These findings are the first to reveal a role for Neurog2 in hypothalamic development.

Dopaminergic neurons innervate extensive areas of the brain and release dopamine (DA) onto a wide range of target neurons. However, DA release is also precisely regulated. In Drosophila melanogaster brain explant preparations, DA is released specifically onto α3/α'3 compartments of mushroom body (MB) neurons that have been coincidentally activated by cholinergic and glutamatergic inputs. The mechanism for this precise release has been unclear. Here we found that coincidentally activated MB neurons generate carbon monoxide (CO), which functions as a retrograde signal evoking local DA release from presynaptic terminals. CO production depends on activity of heme oxygenase in postsynaptic MB neurons, and CO-evoked DA release requires Ca²⁺ efflux through ryanodine receptors in DA terminals. CO is only produced in MB areas receiving coincident activation, and removal of CO using scavengers blocks DA release. We propose that DA neurons use two distinct modes of transmission to produce global and local DA signaling.

SIGNIFICANCE STATEMENT Dopamine (DA) is needed for various higher brain functions, including memory formation. However, DA neurons form extensive synaptic connections, while memory formation requires highly specific and localized DA release. Here we identify a mechanism through which DA release from presynaptic terminals is controlled by postsynaptic activity. Postsynaptic neurons activated by cholinergic and glutamatergic inputs generate carbon monoxide, which acts as a retrograde messenger inducing presynaptic DA release. Released DA is required for memory-associated plasticity. Our work identifies a novel mechanism that restricts DA release to the specific postsynaptic sites that require DA during memory formation.

Biodiversity for food and agriculture is among the earth’s most important resources. Biodiversity is indispensable: be it the insects that pollinate plants, the microscopic bacteria used for making cheese, the diverse livestock breeds used to make a living in harsh environments, the thousands species of fish, and other aquatic species in our lakes, rivers and oceans, or the thousands of [...]

Climate change is what most of us perceive as the top global threat, and the dangers it poses affect present and future generations alike.  How global warming is threatening the planet has been a theme in children’s books for all ages for some time.   How everyone, especially today’s youth, can make a difference to the future of the world [...]

Perhaps it was not just a massive slice that made Pizza Rat a true New Yorker

Retail stores in Fort William First Nation (FWFN) reopened to the general public on May 5, but they are under new operating requirements, the band council announced Friday. However, the residential area of the First Nation remains accessible only to people who live in the community.

It works like a very fine "molecular knob" able to modulate the electrical activity of the neurons of our cerebral cortex, crucial to the functioning of our brain.

One of the main mechanisms of azole resistance of Aspergillus fumigatus is thought to be a reduction in the drug’s affinity for the target molecule, Cyp51A, due to its amino acid mutation(s). It is known that the azole resistance pattern is closely related to the mutation site(s) of the molecule. In this study, we tried to develop a simple and rapid detection method for cyp51A mutations using the endonuclease Surveyor nuclease. The Surveyor nuclease assay was verified using several azole-resistant strains of A. fumigatus that possess point mutations in Cyp51A. For validation of the Surveyor nuclease assay, blind tests were conducted using 48 strains of A. fumigatus (17 azole-resistant and 31 azole-susceptible strains). The Surveyor nuclease assay could rapidly detect cyp51A mutations with one primer set. Also, all the tested strains harboring different cyp51A single point mutations could be clearly distinguished from the wild type. The Surveyor nuclease assay is a simple method that can detect cyp51A mutations rapidly.

Through six camps over the summer, OM Hungary reached over 300 children and youth with the message of the Gospel while teaching English.

Lincoln and Manna from Hong Kong are two of the four candidates to participate in OM EAP’s first Timothy Trek training programme this year.

Name: Sam Castro Home: Pachuca, Mexico Born in: March 1988 Joined OM Ships: September 2013 Previous employment: Veterinarian Current job on board: Shift leader in the book fair

Sunday School isn't just for Sundays anymore - it can be on any given day of the week in Ukraine.

A glance through Jamie Genevieve's social media pages and a fairly intimidating impression emerges.

"The messages teens hear are 'Enjoy life: no commitment, keep your choices open and choose comfort'. Is this the consequence of their own choices or of the generation that raised them? Probably both," shares Ewout.

THE Herald has reported (May 6) on another economically and socially “lost generation” of children and young people due to Covid-19.

Source: Ámbito - A dos meses de la cumbre mundial que este año tendrá lugar en Bonn, el cambio climático volvió a irrumpir con toda su fuerza y la temperatura amenaza con convertir a 2017 en el año más caluroso desde que se tiene registro. En nuestro país, la fatídica serie de inundaciones sobre la cuenca del río Salado dejó miles de hectáreas bajo las aguas en La Pampa, el sur de Córdoba y el oeste de Buenos Aires. "El cambio climático está afectando y afectará el desarrollo de los países y Argentina es una de las economías emergentes más vulnerables", alertaron especialistas del Banco Mundial.

Several studies have focused on birth weight heritability, reporting results that range between 40% and 80%. Few studies have focused on the process of weight gain and were mainly based on heterogeneous samples of infants.

The present work looks at a uniform set of healthy preterm newborn twins. The resulting high heritability estimate could suggest using the inclusion criteria to identify genes that regulate postnatal weight gain or failure. (Read the full article)

Macroglossia is a clinical feature of several disorders and a common reason for additional diagnostic investigations during infancy. Limited research has been done on the evaluation of macroglossia when other features are not suggestive of Beckwith-Wiedemann syndrome.

All patients with apparently isolated macroglossia should have at least initial evaluation with abdominal ultrasounds and molecular studies for Beckwith-Wiedemann syndrome before a final diagnosis is given. Other common diagnoses included isolated macroglossia, chromosomal abnormalities, hypothyroidism, and mucopolysaccharidoses. (Read the full article)

There is evidence of an impaired respiratory regulation in SIDS, in which serotonergic and noradrenergic neurons are involved. Monoamine oxidase A is the enzyme that degrades both neurotransmitters, and genetic variation of this gene might contribute to SIDS.

Alleles with weak effect on the monoamine oxidase A gene activity (*2/*3) appear to be associated with sudden infant death syndrome in boys. This association is strongest in infants who died at the age with the highest SIDS prevalence. (Read the full article)

Twin studies provide a natural experiment that can determine the extent of genetic and environmental influences on sleep behavior. Previous studies have indicated that genes contribute moderately to sleep.

In the largest pediatric study to date, we demonstrate that the shared environment strongly influences sleep behavior in infants, with no gender differences in the results. This research provides strong impetus to future work identifying the key modifiable environmental drivers. (Read the full article)