Jon Cockayne, Chris J. Oates, Ilse C.F. Ipsen, Mark Girolami.

Source: Bayesian Analysis, Volume 14, Number 3, 937--1012.

Abstract:
A fundamental task in numerical computation is the solution of large linear systems. The conjugate gradient method is an iterative method which offers rapid convergence to the solution, particularly when an effective preconditioner is employed. However, for more challenging systems a substantial error can be present even after many iterations have been performed. The estimates obtained in this case are of little value unless further information can be provided about, for example, the magnitude of the error. In this paper we propose a novel statistical model for this error, set in a Bayesian framework. Our approach is a strict generalisation of the conjugate gradient method, which is recovered as the posterior mean for a particular choice of prior. The estimates obtained are analysed with Krylov subspace methods and a contraction result for the posterior is presented. The method is then analysed in a simulation study as well as being applied to a challenging problem in medical imaging.

L. F. South, A. N. Pettitt, C. C. Drovandi.

Source: Bayesian Analysis, Volume 14, Number 3, 773--796.

Abstract:
Sequential Monte Carlo (SMC) methods for sampling from the posterior of static Bayesian models are flexible, parallelisable and capable of handling complex targets. However, it is common practice to adopt a Markov chain Monte Carlo (MCMC) kernel with a multivariate normal random walk (RW) proposal in the move step, which can be both inefficient and detrimental for exploring challenging posterior distributions. We develop new SMC methods with independent proposals which allow recycling of all candidates generated in the SMC process and are embarrassingly parallelisable. A novel evidence estimator that is easily computed from the output of our independent SMC is proposed. Our independent proposals are constructed via flexible copula-type models calibrated with the population of SMC particles. We demonstrate through several examples that more precise estimates of posterior expectations and the marginal likelihood can be obtained using fewer likelihood evaluations than the more standard RW approach.

Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy.

Source: Bayesian Analysis, Volume 14, Number 2, 495--519.

Abstract:
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multiple continuous parameters, but existing approaches degrade in performance when the noise level is high, limiting its applicability to many randomized experiments. We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized. Simulations with synthetic functions show that optimization performance on noisy, constrained problems outperforms existing methods. We further demonstrate the effectiveness of the method with two real-world experiments conducted at Facebook: optimizing a ranking system, and optimizing server compiler flags.

Peter D. Hoff

Source: Bayesian Anal., Volume 6, Number 2, 179--196.

Abstract:
Modern datasets are often in the form of matrices or arrays, potentially having correlations along each set of data indices. For example, data involving repeated measurements of several variables over time may exhibit temporal correlation as well as correlation among the variables. A possible model for matrix-valued data is the class of matrix normal distributions, which is parametrized by two covariance matrices, one for each index set of the data. In this article we discuss an extension of the matrix normal model to accommodate multidimensional data arrays, or tensors. We show how a particular array-matrix product can be used to generate the class of array normal distributions having separable covariance structure. We derive some properties of these covariance structures and the corresponding array normal distributions, and show how the array-matrix product can be used to define a semi-conjugate prior distribution and calculate the corresponding posterior distribution. We illustrate the methodology in an analysis of multivariate longitudinal network data which take the form of a four-way array.

Ruosi Guo, Chunming Zhang, Zhengjun Zhang.

Source: Statistical Science, Volume 35, Number 1, 145--157.

Abstract:
In many scientific disciplines, finding hidden influential factors behind observational data is essential but challenging. The majority of existing approaches, such as the independent component analysis (${mathrm{ICA}}$), rely on linear transformation, that is, true signals are linear combinations of hidden components. Motivated from analyzing nonlinear temporal signals in neuroscience, genetics, and finance, this paper proposes the “maximum independent component analysis” (${mathrm{MaxICA}}$), based on max-linear combinations of components. In contrast to existing methods, ${mathrm{MaxICA}}$ benefits from focusing on significant major components while filtering out ignorable components. A major tool for parameter learning of ${mathrm{MaxICA}}$ is an augmented genetic algorithm, consisting of three schemes for the elite weighted sum selection, randomly combined crossover, and dynamic mutation. Extensive empirical evaluations demonstrate the effectiveness of ${mathrm{MaxICA}}$ in either extracting max-linearly combined essential sources in many applications or supplying a better approximation for nonlinearly combined source signals, such as $mathrm{EEG}$ recordings analyzed in this paper.

Roderick J. Little.

Source: Statistical Science, Volume 34, Number 4, 580--583.

Andreas Buja, Lawrence Brown, Richard Berk, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 523--544.

Abstract:
In the early 1980s, Halbert White inaugurated a “model-robust” form of statistical inference based on the “sandwich estimator” of standard error. This estimator is known to be “heteroskedasticity-consistent,” but it is less well known to be “nonlinearity-consistent” as well. Nonlinearity, however, raises fundamental issues because in its presence regressors are not ancillary, hence cannot be treated as fixed. The consequences are deep: (1) population slopes need to be reinterpreted as statistical functionals obtained from OLS fits to largely arbitrary joint ${x extrm{-}y}$ distributions; (2) the meaning of slope parameters needs to be rethought; (3) the regressor distribution affects the slope parameters; (4) randomness of the regressors becomes a source of sampling variability in slope estimates of order $1/sqrt{N}$; (5) inference needs to be based on model-robust standard errors, including sandwich estimators or the ${x extrm{-}y}$ bootstrap. In theory, model-robust and model-trusting standard errors can deviate by arbitrary magnitudes either way. In practice, significant deviations between them can be detected with a diagnostic test.

Peter M. Atkinson, Jorge Mateu.

Source: Statistical Science, Volume 34, Number 3, 504--521.

Abstract:
Peter John Diggle was born on February 24, 1950, in Lancashire, England. Peter went to school in Scotland, and it was at the end of his school years that he found that he was good at maths and actually enjoyed it. Peter went to Edinburgh to do a maths degree, but transferred halfway through to Liverpool where he completed his degree. Peter studied for a year at Oxford and was then appointed in 1974 as a lecturer in statistics at the University of Newcastle-upon-Tyne where he gained his PhD, and was promoted to Reader in 1983. A sabbatical at the Swedish Royal College of Forestry gave him his first exposure to real scientific data and problems, prompting a move to CSIRO, Australia. After five years with CSIRO where he was Senior, then Principal, then Chief Research Scientist and Chief of the Division of Mathematics and Statistics, he returned to the UK in 1988, to a Chair at Lancaster University. Since 2011 Peter has held appointments at Lancaster and Liverpool, together with honorary appointments at Johns Hopkins, Columbia and Yale. At Lancaster, Peter was the founder and Director of the Medical Statistics Unit (1995–2001), University Dean for Research (1998–2001), EPSRC Senior Fellow (2004–2008), Associate Dean for Research at the School of Health and Medicine (2007–2011), Distinguished University Professor, and leader of the CHICAS Research Group (2007–2017). A Fellow of the Royal Statistical Society since 1974, he was a Member of Council (1983–1985), Joint Editor of JRSSB (1984–1987), Honorary Secretary (1990–1996), awarded the Guy Medal in Silver (1997) and the Barnett Award (2018), Associate Editor of Applied Statistics (1998–2000), Chair of the Research Section Committee (1998–2000), and President (2014–2016). Away from work, Peter enjoys music, playing folk-blues guitar and tenor recorder, and listening to jazz. His running days are behind him, but he can just about hold his own in mixed-doubles badminton with his family. His boyhoood hero was Stirling Moss, and he retains an enthusiasm for classic cars, not least his 1988 Porsche 924S. His favorite authors are George Orwell, Primo Levi and Nigel Slater. This interview was done prior to the fourth Spatial Statistics conference held in Lancaster, July 2017 where a session was dedicated to Peter celebrating his contributions to statistics.

Pantelis Samartsidis, Shaun R. Seaman, Anne M. Presanis, Matthew Hickman, Daniela De Angelis.

Source: Statistical Science, Volume 34, Number 3, 486--503.

Abstract:
Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is nonrandomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.

Jacqueline J. Meulman, Anita J. van der Kooij, Kevin L. W. Duisters.

Source: Statistical Science, Volume 34, Number 3, 361--390.

Abstract:
We present a methodology for multiple regression analysis that deals with categorical variables (possibly mixed with continuous ones), in combination with regularization, variable selection and high-dimensional data ($Pgg N$). Regularization and optimal scaling (OS) are two important extensions of ordinary least squares regression (OLS) that will be combined in this paper. There are two data analytic situations for which optimal scaling was developed. One is the analysis of categorical data, and the other the need for transformations because of nonlinear relationships between predictors and outcome. Optimal scaling of categorical data finds quantifications for the categories, both for the predictors and for the outcome variables, that are optimal for the regression model in the sense that they maximize the multiple correlation. When nonlinear relationships exist, nonlinear transformation of predictors and outcome maximize the multiple correlation in the same way. We will consider a variety of transformation types; typically we use step functions for categorical variables, and smooth (spline) functions for continuous variables. Both types of functions can be restricted to be monotonic, preserving the ordinal information in the data. In combination with optimal scaling, three popular regularization methods will be considered: Ridge regression, the Lasso and the Elastic Net. The resulting method will be called ROS Regression (Regularized Optimal Scaling Regression). The OS algorithm provides straightforward and efficient estimation of the regularized regression coefficients, automatically gives the Group Lasso and Blockwise Sparse Regression, and extends them by the possibility to maintain ordinal properties in the data. Extended examples are provided.

Christopher K. Wikle, Jay M. Ver Hoef.

Source: Statistical Science, Volume 34, Number 2, 349--359.

Abstract:
Noel Cressie, FAA is Director of the Centre for Environmental Informatics in the National Institute for Applied Statistics Research Australia (NIASRA) and Distinguished Professor in the School of Mathematics and Applied Statistics at the University of Wollongong, Australia. He is also Adjunct Professor at the University of Missouri (USA), Affiliate of Org 398, Science Data Understanding, at NASA’s Jet Propulsion Laboratory (USA), and a member of the Science Team for NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite. Cressie was awarded a B.Sc. with First Class Honours in Mathematics in 1972 from the University of Western Australia, and an M.A. and Ph.D. in Statistics in 1973 and 1975, respectively, from Princeton University (USA). Two brief postdoctoral periods followed, at the Centre de Morphologie Mathématique, ENSMP, in Fontainebleau (France) from April 1975–September 1975, and at Imperial College, London (UK) from September 1975–January 1976. His past appointments have been at The Flinders University of South Australia from 1976–1983, at Iowa State University (USA) from 1983–1998, and at The Ohio State University (USA) from 1998–2012. He has authored or co-authored four books and more than 280 papers in peer-reviewed outlets, covering areas that include spatial and spatio-temporal statistics, environmental statistics, empirical-Bayesian and Bayesian methods including sequential design, goodness-of-fit, and remote sensing of the environment. Many of his papers also address important questions in the sciences. Cressie is a Fellow of the Australian Academy of Science, the American Statistical Association, the Institute of Mathematical Statistics, and the Spatial Econometrics Association, and he is an Elected Member of the International Statistical Institute. Noel Cressie’s refereed, unrefereed, and other publications are available at: https://niasra.uow.edu.au/cei/people/UOW232444.html.

Sam Behseta.

Source: Statistical Science, Volume 34, Number 2, 334--348.

Abstract:
Rob Kass has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981; he joined the Center for the Neural Basis of Cognition (CNBC) in 1997, and the Machine Learning Department (in the School of Computer Science) in 2007. He served as Department Head of Statistics from 1995 to 2004 and served as Interim Co-Director of the CNBC 2015–2018. He became the Maurice Falk Professor of Statistics and Computational Neuroscience in 2016. Kass has served as Chair of the Section for Bayesian Statistical Science of the American Statistical Association, Chair of the Statistics Section of the American Association for the Advancement of Science, founding Editor-in-Chief of the journal Bayesian Analysis and Executive Editor of Statistical Science . He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the American Association for the Advancement of Science. He has been recognized by the Institute for Scientific Information as one of the 10 most highly cited researchers, 1995–2005, in the category of mathematics. Kass is the recipient of the 2017 Fisher Award and lectureship by the Committee of the Presidents of the Statistical Societies. This interview took place at Carnegie Mellon University in November 2017.

Gregorio Quintana-Ortí, Amelia Simó.

Source: Statistical Science, Volume 34, Number 2, 236--252.

Abstract:
This paper is focused on kernel regression when the response variable is the shape of a 3D object represented by a configuration matrix of landmarks. Regression methods on this shape space are not trivial because this space has a complex finite-dimensional Riemannian manifold structure (non-Euclidean). Papers about it are scarce in the literature, the majority of them are restricted to the case of a single explanatory variable, and many of them are based on the approximated tangent space. In this paper, there are several methodological innovations. The first one is the adaptation of the general method for kernel regression analysis in manifold-valued data to the three-dimensional case of Kendall’s shape space. The second one is its generalization to the multivariate case and the addressing of the curse-of-dimensionality problem. Finally, we propose bootstrap confidence intervals for prediction. A simulation study is carried out to check the goodness of the procedure, and a comparison with a current approach is performed. Then, it is applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children’s wear.

Vladimir Koltchinskii, Richard Nickl, Philippe Rigollet.

Source: Statistical Science, Volume 34, Number 1, 169--175.

Abstract:
Richard Mansfield Dudley (Dick Dudley) was born in 1938. He received the A.B. from Harvard in 1952 and the Ph.D. from Princeton in 1962 (under the supervision of Gilbert Hunt and Edward Nelson). Following an appointment at UC Berkeley as an assistant professor, he joined the Department of Mathematics at MIT in 1967. Dick Dudley has made fundamental contributions to the theory of Gaussian processes and Probability in Banach Spaces. Among his major achievements is the development of a general framework for empirical processes theory, in particular, for uniform central limit theorems. These results have had and continue having tremendous impact in contemporary statistics and in mathematical foundations of machine learning. A more extensive biographical sketch is contained in the preface to the Selected works of R. M. Dudley (editors: E. Giné, V. Koltchinskii and R. Norvaisa) published in 2010. This conversation took place (mostly, via email) in the fall of 2017.

Geurt Jongbloed.

Source: Statistical Science, Volume 34, Number 1, 156--168.

Abstract:
Petrus (Piet) Groeneboom was born in Scheveningen in 1941 and grew up in Voorburg. Both villages are located near The Hague in The Netherlands; Scheveningen actually being part of The Hague. He attended the gymnasium of the Huygens lyceum. In 1959, he entered the University of Amsterdam, where he studied psychology. After his “candidate” exam (comparable to BSc) in 1963, he worked at the psychological laboratory of the University of Amsterdam until 1966. In 1965, he took up mathematics as a part-time study. After having obtained his master’s degree in 1971, he had a position at the psychological laboratory again until 1973, when he was appointed to the Mathematical Center in Amsterdam. There, he wrote between 1975 and 1979 his Ph.D. thesis with Kobus Oosterhoff as advisor, graduating in 1979. After a period of two years as visiting professor at the University of Washington (UW) in Seattle, Piet moved back to the Mathematical Center until he was appointed full professor of statistics at the University of Amsterdam in 1984. Four years later, he moved to Delft University of Technology where he became professor of statistics and stayed until his retirement in 2006. Between 2000 and 2006 he also held a part-time professorship at the Vrije Universiteit in Amsterdam. From 1999 till 2013 he was Affiliate Professor at the statistics department of UW, Seattle. Apart from being visiting professor at the UW in Seattle, he was also visiting professor at Stanford University, Université Paris 6 and ETH Zürich. Piet is well known for his work on shape constrained statistical inference. He worked on asymptotic theory for these problems, created algorithms to compute nonparametric estimates in such models and applied these models to real data. He also worked on interacting particle systems, extreme value analysis and efficiency theory for testing procedures. Piet (co-)authored four books and 64 papers and served as promotor of 13 students. He is the recipient of the 1985 Rollo Davidson prize, a fellow of the IMS and elected member of the ISI. In 2015, he delivered the Wald lecture at the Joint Statistical Meeting in Montreal. Piet and his wife Marijke live in Naarden. He has two sons, Thomas and Tim, and (since June 12, 2018) one grandson, Tarik. This conversation was held at Piet’s house in Naarden, on February 28 and April 24, 2018.

London, [29 January 1994]

Once you try them, you’ll never want to wear anything else

Steven Poser
Jun 1, 2003; 23:4420-4427
Cellular

Barbara J. Weiland
Jan 28, 2015; 35:1505-1512
Neurobiology of Disease

Catherine Orr
Mar 6, 2019; 39:1817-1827
BehavioralSystemsCognitive

Masaki Yamamoto
May 22, 2019; 39:4179-4192
Neurobiology of Disease

KM Harris
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Articles

Jeremy R. Manning
Oct 28, 2009; 29:13613-13620
BehavioralSystemsCognitive

WR Softky
Jan 1, 1993; 13:334-350
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Lennart Mucke
Jun 1, 2000; 20:4050-4058
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Microsoft has introduced a slew of new products, including the Surface Go 2, the Surface Book 3, Surface Headphones 2 and Surface Earbuds. Both the Surface Go 2 and the Surface Book 3 come in consumer and corporate versions. "The two products are very different," noted Rob Enderle, principal analyst at the Enderle Group. "The Go 2 is a high-value product -- the Surface Book 3 high innovation."

If you are looking for a well-designed Linux distro that is far from mainstream, loaded with performance features not found elsewhere, check out the 2020 upgrade of the MakuluLinux Core distro. It could change your perspective on what a daily computing driver should offer. Developer Jacque Montague Raymer recently released the 2020 edition of MakuluLinux Core OS.

Contact tracing is a big job, like trying to drain an ocean with a teaspoon. It involves finding people who have been exposed to the coronavirus and testing them to determine if they are infected or are carriers. Public health officials then can take necessary steps to prevent the virus' spread. It's a perfect fit for CRM, and Salesforce's core technology is coming to the forefront.

Original quotes from interview by Mr Agustin Carstens, General Manager of the BIS, with Exame, conducted by Mr Felipe Serrano on 9 October 2019 and published on 24 October 2019.

A prominent theory claims that the right temporoparietal junction (rTPJ) is especially associated with embodied processes relevant to perspective-taking. In the present study, we use high-definition transcranial direct current stimulation to provide evidence that the rTPJ is causally associated with the embodied processes underpinning perspective-taking. Eighty-eight young human adults were stratified to receive either rTPJ or dorsomedial PFC anodal high-definition transcranial direct current stimulation in a sham-controlled, double-blind, repeated-measures design. Perspective-tracking (line-of-sight) and perspective-taking (embodied rotation) were assessed using a visuo-spatial perspective-taking task that required understanding what another person could see or how they see it, respectively. Embodied processing was manipulated by positioning the participant in a manner congruent or incongruent with the orientation of an avatar on the screen. As perspective-taking, but not perspective-tracking, is influenced by bodily position, this allows the investigation of the specific causal role for the rTPJ in embodied processing. Crucially, anodal stimulation to the rTPJ increased the effect of bodily position during perspective-taking, whereas no such effects were identified during perspective-tracking, thereby providing evidence for a causal role for the rTPJ in the embodied component of perspective-taking. Stimulation to the dorsomedial PFC had no effect on perspective-tracking or taking. Therefore, the present study provides support for theories postulating that the rTPJ is causally involved in embodied cognitive processing relevant to social functioning.

SIGNIFICANCE STATEMENT The ability to understand another's perspective is a fundamental component of social functioning. Adopting another perspective is thought to involve both embodied and nonembodied processes. The present study used high-definition transcranial direct current stimulation (HD-tDCS) and provided causal evidence that the right temporoparietal junction is involved specifically in the embodied component of perspective-taking. Specifically, HD-tDCS to the right temporoparietal junction, but not another hub of the social brain (dorsomedial PFC), increased the effect of body position during perspective-taking, but not tracking. This is the first causal evidence that HD-tDCS can modulate social embodied processing in a site-specific and task-specific manner.

Endogenous neuropeptide Y (NPY) and corticotrophin-releasing factor (CRF) modulate the responses of the basolateral amygdala (BLA) to stress and are associated with the development of stress resilience and vulnerability, respectively. We characterized persistent effects of repeated NPY and CRF treatment on the structure and function of BLA principal neurons in a novel organotypic slice culture (OTC) model of male rat BLA, and examined the contributions of specific NPY receptor subtypes to these neural and behavioral effects. In BLA principal neurons within the OTCs, repeated NPY treatment caused persistent attenuation of excitatory input and induced dendritic hypotrophy via Y₅ receptor activation; conversely, CRF increased excitatory input and induced hypertrophy of BLA principal neurons. Repeated treatment of OTCs with NPY followed by an identical treatment with CRF, or vice versa, inhibited or reversed all structural changes in OTCs. These structural responses to NPY or CRF required calcineurin or CaMKII, respectively. Finally, repeated intra-BLA injections of NPY or a Y₅ receptor agonist increased social interaction, a validated behavior for anxiety, and recapitulated structural changes in BLA neurons seen in OTCs, while a Y₅ receptor antagonist prevented NPY's effects both on behavior and on structure. These results implicate the Y₅ receptor in the long-term, anxiolytic-like effects of NPY in the BLA, consistent with an intrinsic role in stress buffering, and highlight a remarkable mechanism by which BLA neurons may adapt to different levels of stress. Moreover, BLA OTCs offer a robust model to study mechanisms associated with resilience and vulnerability to stress in BLA.

SIGNIFICANCE STATEMENT Within the basolateral amygdala (BLA), neuropeptide Y (NPY) is associated with buffering the neural stress response induced by corticotropin releasing factor, and promoting stress resilience. We used a novel organotypic slice culture model of BLA, complemented with in vivo studies, to examine the cellular mechanisms associated with the actions of NPY. In organotypic slice cultures, repeated NPY treatment reduces the complexity of the dendritic extent of anxiogenic BLA principal neurons, making them less excitable. NPY, via activation of Y5 receptors, additionally inhibits and reverses the increases in dendritic extent and excitability induced by the stress hormone, corticotropin releasing factor. This NPY-mediated neuroplasticity indicates that resilience or vulnerability to stress may thus involve neuropeptide-mediated dendritic remodeling in BLA principal neurons.

Functional recovery after cortical injury, such as stroke, is associated with neural circuit reorganization, but the underlying mechanisms and efficacy of therapeutic interventions promoting neural plasticity in primates are not well understood. Bone marrow mesenchymal stem cell-derived extracellular vesicles (MSC-EVs), which mediate cell-to-cell inflammatory and trophic signaling, are thought be viable therapeutic targets. We recently showed, in aged female rhesus monkeys, that systemic administration of MSC-EVs enhances recovery of function after injury of the primary motor cortex, likely through enhancing plasticity in perilesional motor and premotor cortices. Here, using in vitro whole-cell patch-clamp recording and intracellular filling in acute slices of ventral premotor cortex (vPMC) from rhesus monkeys (Macaca mulatta) of either sex, we demonstrate that MSC-EVs reduce injury-related physiological and morphologic changes in perilesional layer 3 pyramidal neurons. At 14-16 weeks after injury, vPMC neurons from both vehicle- and EV-treated lesioned monkeys exhibited significant hyperexcitability and predominance of inhibitory synaptic currents, compared with neurons from nonlesioned control brains. However, compared with vehicle-treated monkeys, neurons from EV-treated monkeys showed lower firing rates, greater spike frequency adaptation, and excitatory:inhibitory ratio. Further, EV treatment was associated with greater apical dendritic branching complexity, spine density, and inhibition, indicative of enhanced dendritic plasticity and filtering of signals integrated at the soma. Importantly, the degree of EV-mediated reduction of injury-related pathology in vPMC was significantly correlated with measures of behavioral recovery. These data show that EV treatment dampens injury-related hyperexcitability and restores excitatory:inhibitory balance in vPMC, thereby normalizing activity within cortical networks for motor function.

SIGNIFICANCE STATEMENT Neuronal plasticity can facilitate recovery of function after cortical injury, but the underlying mechanisms and efficacy of therapeutic interventions promoting this plasticity in primates are not well understood. Our recent work has shown that intravenous infusions of mesenchymal-derived extracellular vesicles (EVs) that are involved in cell-to-cell inflammatory and trophic signaling can enhance recovery of motor function after injury in monkey primary motor cortex. This study shows that this EV-mediated enhancement of recovery is associated with amelioration of injury-related hyperexcitability and restoration of excitatory-inhibitory balance in perilesional ventral premotor cortex. These findings demonstrate the efficacy of mesenchymal EVs as a therapeutic to reduce injury-related pathologic changes in the physiology and structure of premotor pyramidal neurons and support recovery of function.

The Drosophila nervous system is ensheathed by a layer of outer glial cells, the perineurial glia, and a specialized extracellular matrix, the neural lamella. The function of perineurial glial cells and how they interact with the extracellular matrix are just beginning to be elucidated. Integrin-based focal adhesion complexes link the glial membrane to the extracellular matrix, but little is known about integrin's regulators in the glia. The transmembrane Ig domain protein Basigin/CD147/EMMPRIN is highly expressed in the perineurial glia surrounding the Drosophila larval nervous system. Here we show that Basigin associates with integrin at the focal adhesions to uphold the structure of the glia-extracellular matrix sheath. Knockdown of Basigin in perineurial glia using RNAi results in significant shortening of the ventral nerve cord, compression of the glia and extracellular matrix in the peripheral nerves, and reduction in larval locomotion. We determined that Basigin is expressed in close proximity to integrin at the glial membrane, and that expression of the extracellular integrin-binding domain of Basigin is sufficient to rescue peripheral glial compression. We also found that a reduction in expression of integrin at the membrane rescues the ventral nerve cord shortening, peripheral glial compression, and locomotor phenotypes, and that reduction in the integrin-binding protein Talin can partially rescue glial compression. These results identify Basigin as a potential negative regulator of integrin in the glia, supporting proper glial and extracellular matrix ensheathment of the nervous system.

SIGNIFICANCE STATEMENT The glial cells and extracellular matrix play important roles in supporting and protecting the nervous system, but the interactions between these components have not been well characterized. Our study identified expression of a conserved Ig superfamily protein, Basigin, at the glial membrane of Drosophila where it associates with the integrin-based focal adhesion complexes to ensure proper ensheathment of the CNS and PNS. Loss of Basigin in the glia results in an overall compression of the nervous system due to integrin dysregulation, which causes locomotor defects in the animals. This underlies the importance of glia-matrix communication for structural and functional support of the nervous system.

MECP2 gain-of-function and loss-of-function in genetically engineered monkeys recapitulates typical phenotypes in patients with autism, yet where MECP2 mutation affects the monkey brain and whether/how it relates to autism pathology remain unknown. Here we report a combination of gene–circuit–behavior analyses including MECP2 coexpression network, locomotive and cognitive behaviors, and EEG and fMRI findings in 5 MECP2 overexpressed monkeys (Macaca fascicularis; 3 females) and 20 wild-type monkeys (Macaca fascicularis; 11 females). Whole-genome expression analysis revealed MECP2 coexpressed genes significantly enriched in GABA-related signaling pathways, whereby reduced β-synchronization within fronto-parieto-occipital networks was associated with abnormal locomotive behaviors. Meanwhile, MECP2-induced hyperconnectivity in prefrontal and cingulate networks accounted for regressive deficits in reversal learning tasks. Furthermore, we stratified a cohort of 49 patients with autism and 72 healthy controls of 1112 subjects using functional connectivity patterns, and identified dysconnectivity profiles similar to those in monkeys. By establishing a circuit-based construct link between genetically defined models and stratified patients, these results pave new avenues to deconstruct clinical heterogeneity and advance accurate diagnosis in psychiatric disorders.

SIGNIFICANCE STATEMENT Autism spectrum disorder (ASD) is a complex disorder with co-occurring symptoms caused by multiple genetic variations and brain circuit abnormalities. To dissect the gene–circuit–behavior causal chain underlying ASD, animal models are established by manipulating causative genes such as MECP2. However, it is unknown whether such models have captured any circuit-level pathology in ASD patients, as demonstrated by human brain imaging studies. Here, we use transgenic macaques to examine the causal effect of MECP2 overexpression on gene coexpression, brain circuits, and behaviors. For the first time, we demonstrate that the circuit abnormalities linked to MECP2 and autism-like traits in the monkeys can be mapped to a homogeneous ASD subgroup, thereby offering a new strategy to deconstruct clinical heterogeneity in ASD.

Behavior can be guided by neuronal activity in visual, auditory, or somatosensory cerebral cortex, depending on task requirements. In contrast to this flexible access of cortical signals, several observations suggest that behaviors depend more on neurons in later areas of visual cortex than those in earlier areas, although neurons in earlier areas would provide more reliable signals for many tasks. We recorded from neurons in different levels of visual cortex of 2 male rhesus monkeys while the animals did a visual discrimination task and examined trial-to-trial correlations between neuronal and behavioral responses. These correlations became stronger in primary visual cortex as neuronal signals in that area became more reliable relative to the other areas. The results suggest that the mechanisms that read signals from cortex might access any cortical area depending on the relative value of those signals for the task at hand.

SIGNIFICANCE STATEMENT Information is encoded by the action potentials of neurons in various cortical areas in a hierarchical manner such that increasingly complex stimulus features are encoded in successive stages. The brain must extract information from the response of appropriate neurons to drive optimal behavior. A widely held view of this decoding process is that the brain relies on the output of later cortical areas to make decisions, although neurons in earlier areas can provide more reliable signals. We examined correlations between perceptual decisions and the responses of neurons in different levels of monkey visual cortex. The results suggest that the brain may access signals in any cortical area depending on the relative value of those signals for the task at hand.

FAO Director-General José Graziano da Silva underlined his firm belief that a hunger-free world is possible "within our lifetimes," during high-level talks in New York. "The Zero Hunger Challenge calls for something new – something bold, but long overdue," he said. It was a "decisive global commitment to end hunger, eliminate childhood stunting, make all food systems sustainable, eradicate rural poverty, [...]

Forests are one of the Earth’s greatest natural resources. There is a reason why we often figuratively speak of ‘the tree of life’; forests are key to supporting life on Earth. Eight thousand years ago, half of the Earth’s land surface was covered by forests or wooded areas. Today, these areas represent less than one third. Forests are home to 80% [...]

At the beginning of the year we took a tour of 6 incredible plants you might not have heard of. Diets worldwide – from forest roots and leaves such as the moringa in Africa and parts of Asia to cardoon, the close relative of the artichoke in Europe – are varied, suited to local environment and can counter malnutrition and [...]

Smiley and energetic, Christine lives in the semi-arid region of Karamoja in north-east Uganda. Her husband passed away some time ago and she is now taking care of her six children on her own. Christine struggled in managing her household and securing the basic needs for her children. “I was permanently asking somebody for something,” she describes. Agriculture had always been [...]

For the first time, the indigenous community is allowed to gather the cherished plant on protected land

The National Cherry Blossom Festival has moved online due to the novel coronavirus pandemic

The 15th-century edition of Hafez's "Divan" will be sold at Sotheby's next month

It’s not quite a meat dress, but Kaikaia gaga does boast some impressive horn-like appendages