Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modelling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While the simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

In many Machine Learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised Domain Adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from Electronic Health Records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection, are discussed.

National Association of Wine Retailers Release Letter Delivered to Attorneys General and Alcohol Regulatory Chiefs Concerning Unconstitutional and Unenforceable Wine Shipping Bans

(PRWeb April 15, 2020)

Read the full story at https://www.prweb.com/releases/wine_retailers_seek_alcohol_shipping_compromise_with_18_states/prweb17050617.htm

Alessandro Rinaldo, Larry Wasserman, Max G’Sell.

Source: The Annals of Statistics, Volume 47, Number 6, 3438--3469.

Abstract:
Several new methods have been recently proposed for performing valid inference after model selection. An older method is sample splitting: use part of the data for model selection and the rest for inference. In this paper, we revisit sample splitting combined with the bootstrap (or the Normal approximation). We show that this leads to a simple, assumption-lean approach to inference and we establish results on the accuracy of the method. In fact, we find new bounds on the accuracy of the bootstrap and the Normal approximation for general nonlinear parameters with increasing dimension which we then use to assess the accuracy of regression inference. We define new parameters that measure variable importance and that can be inferred with greater accuracy than the usual regression coefficients. Finally, we elucidate an inference-prediction trade-off: splitting increases the accuracy and robustness of inference but can decrease the accuracy of the predictions.

Li Zhu, Zhiguang Huo, Tianzhou Ma, Steffi Oesterreich, George C. Tseng.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2611--2636.

Abstract:
Variable selection is a pervasive problem in modern high-dimensional data analysis where the number of features often exceeds the sample size (a.k.a. small-n-large-p problem). Incorporation of group structure knowledge to improve variable selection has been widely studied. Here, we consider prior knowledge of a hierarchical overlapping group structure to improve variable selection in regression setting. In genomics applications, for instance, a biological pathway contains tens to hundreds of genes and a gene can be mapped to multiple experimentally measured features (such as its mRNA expression, copy number variation and methylation levels of possibly multiple sites). In addition to the hierarchical structure, the groups at the same level may overlap (e.g., two pathways can share common genes). Incorporating such hierarchical overlapping groups in traditional penalized regression setting remains a difficult optimization problem. Alternatively, we propose a Bayesian indicator model that can elegantly serve the purpose. We evaluate the model in simulations and two breast cancer examples, and demonstrate its superior performance over existing models. The result not only enhances prediction accuracy but also improves variable selection and model interpretation that lead to deeper biological insight of the disease.

Ying Chen, J. S. Marron, Jiejie Zhang.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1590--1616.

Abstract:
Electricity prices are high dimensional, serially dependent and have seasonal variations. We propose a Warping Functional AutoRegressive (WFAR) model that simultaneously accounts for the cross time-dependence and seasonal variations of the large dimensional data. In particular, electricity price curves are obtained by smoothing over the $24$ discrete hourly prices on each day. In the functional domain, seasonal phase variations are separated from level amplitude changes in a warping process with the Fisher–Rao distance metric, and the aligned (season-adjusted) electricity price curves are modeled in the functional autoregression framework. In a real application, the WFAR model provides superior out-of-sample forecast accuracy in both a normal functioning market, Nord Pool, and an extreme situation, the California market. The forecast performance as well as the relative accuracy improvement are stable for different markets and different time periods.

Pavel Zorin-Kranich.

Source: Bernoulli, Volume 26, Number 3, 2311--2318.

Abstract:
We prove an estimate for weighted $p$th moments of the pathwise $r$-variation of a martingale in terms of the $A_{p}$ characteristic of the weight. The novelty of the proof is that we avoid real interpolation techniques.

Holger Sambale, Arthur Sinulis.

Source: Bernoulli, Volume 26, Number 3, 1863--1890.

Abstract:
We derive sufficient conditions for a probability measure on a finite product space (a spin system ) to satisfy a (modified) logarithmic Sobolev inequality. We establish these conditions for various examples, such as the (vertex-weighted) exponential random graph model, the random coloring and the hard-core model with fugacity. This leads to two separate branches of applications. The first branch is given by mixing time estimates of the Glauber dynamics. The proofs do not rely on coupling arguments, but instead use functional inequalities. As a byproduct, this also yields exponential decay of the relative entropy along the Glauber semigroup. Secondly, we investigate the concentration of measure phenomenon (particularly of higher order) for these spin systems. We show the effect of better concentration properties by centering not around the mean, but around a stochastic term in the exponential random graph model. From there, one can deduce a central limit theorem for the number of triangles from the CLT of the edge count. In the Erdős–Rényi model the first-order approximation leads to a quantification and a proof of a central limit theorem for subgraph counts.

King's Orphan Schools (New Town, Tas.)

Savvas Learning Company will continue to provide its K-12 products and services, and is working to support districts with their remote learning needs during school closures.

The post Pearson K12 Spinoff Rebranded as ‘Savvas Learning Company’ appeared first on Market Brief.

The Justice Department announced Thursday that it is dropping its criminal case against President Trump's first national security adviser Michael Flynn. Flynn twice admitted in court he lied to the FBI about his conversations with Russia's U.S. ambassador, and then cooperated in Special Counsel Robert Mueller's investigation. It was an unusual move by the Justice Department, and CNN's legal and political analysts smelled a rat."Attorney General [William] Barr is already being accused of creating a special justice system just for President Trump's friends," and this will only feed that perception, CNN's Jake Tapper suggested. Political correspondent Sara Murray agreed, noting that the prosecutor in the case, Brandon Van Grack, withdrew right before the Justice Department submitted its filing, just like when Barr intervened to request a reduced sentence for Roger Stone.National security correspondent Jim Sciutto laid out several reason why the substance of Flynn's admitted lie was a big deal, and chief legal analyst Jeffrey Toobin was appalled. "It is one of the most incredible legal documents I have read, and certainly something that I never expected to see from the United States Department of Justice," Toobin said. "The idea that the Justice Department would invent an argument -- an argument that the judge in this case has already rejected -- and say that's a basis for dropping a case where a defendant admitted his guilt shows that this is a case where the fix was in."Barr told CBS News' Cathrine Herridge on Thursday that dropping Flynn's case actually "sends the message that there is one standard of justice in this country." Herridge told Barr he would take flak for this, asking: "When history looks back on this decision, how do you think it will be written?" Barr laughed: "Well, history's written by the winners. So it largely depends on who's writing the history." Watch below. More stories from theweek.com Outed CIA agent Valerie Plame is running for Congress, and her launch video looks like a spy movie trailer 7 scathing cartoons about America's rush to reopen Trump says he couldn't have exposed WWII vets to COVID-19 because the wind was blowing the wrong way

Abhirup Datta, Sudipto Banerjee, James S. Hodges, Leiwen Gao.

Source: Bayesian Analysis, Volume 14, Number 4, 1221--1244.

Abstract:
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

KM Harris
Jul 1, 1992; 12:2685-2705
Articles

KM Harris
Aug 1, 1989; 9:2982-2997
Articles

Randy L. Buckner
Feb 11, 2009; 29:1860-1873
Neurobiology of Disease

Chris Englund
Jan 5, 2005; 25:247-251
BRIEF COMMUNICATION

Merchants soon will be able to sell products on Google Shopping at no charge. Previously, they had to pay per click, but the cost was not fixed. There was no minimum, but they had to set a maximum for ad spend and Google would stop displaying their ads once the maximum was reached. Starting next week, search results on the Google Shopping tab will consist primarily of free product listings.

02020-02-03: Pink Lakes in Tunisia

Despite their opposing actions on food intake, POMC and NPY/AgRP neurons in the arcuate nucleus of the hypothalamus (ARH) are derived from the same progenitors that give rise to ARH neurons. However, the mechanism whereby common neuronal precursors subsequently adopt either the anorexigenic (POMC) or the orexigenic (NPY/AgRP) identity remains elusive. We hypothesize that POMC and NPY/AgRP cell fates are specified and maintained by distinct intrinsic factors. In search of them, we profiled the transcriptomes of developing POMC and NPY/AgRP neurons in mice. Moreover, cell-type-specific transcriptomic analyses revealed transcription regulators that are selectively enriched in either population, but whose developmental functions are unknown in these neurons. Among them, we found the expression of the PR domain-containing factor 12 (Prdm12) was enriched in POMC neurons but absent in NPY/AgRP neurons. To study the role of Prdm12 in vivo, we developed and characterized a floxed Prdm12 allele. Selective ablation of Prdm12 in embryonic POMC neurons led to significantly reduced Pomc expression as well as early-onset obesity in mice of either sex that recapitulates symptoms of human POMC deficiency. Interestingly, however, specific deletion of Prdm12 in adult POMC neurons showed that it is no longer required for Pomc expression or energy balance. Collectively, these findings establish a critical role for Prdm12 in the anorexigenic neuron identity and suggest that it acts developmentally to program body weight homeostasis. Finally, the combination of cell-type-specific genomic and genetic analyses provides a means to dissect cellular and functional diversity in the hypothalamus whose neurodevelopment remains poorly studied.

SIGNIFICANCE STATEMENT POMC and NPY/AgRP neurons are derived from the same hypothalamic progenitors but have opposing effects on food intake. We profiled the transcriptomes of genetically labeled POMC and NPY/AgRP neurons in the developing mouse hypothalamus to decipher the transcriptional codes behind the versus orexigenic neuron identity. Our analyses revealed 29 transcription regulators that are selectively enriched in one of the two populations. We generated new mouse genetic models to selective ablate one of POMC-neuron enriched transcription factors Prdm12 in developing and adult POMC neurons. Our studies establish a previously unrecognized role for Prdm12 in the anorexigenic neuron identity and suggest that it acts developmentally to program body weight homeostasis.

Pattern separation and completion are fundamental hippocampal computations supporting memory encoding and retrieval. However, despite extensive exploration of these processes, it remains unclear whether and how top-down processes adaptively modulate the dynamics between these computations. Here we examine the role of expectation in shifting the hippocampus to perform pattern separation. In a behavioral task, 29 participants (7 males) learned a cue-object category contingency. Then, at encoding, one-third of the cues preceding the to-be-memorized objects, violated the studied rule. At test, participants performed a recognition task with old objects (targets) and a set of parametrically manipulated (very similar to dissimilar) foils for each object. Accuracy was found to be better for foils of high similarity to targets that were contextually unexpected at encoding compared with expected ones. Critically, there were no expectation-driven differences for targets and low similarity foils. To further explore these effects, we implemented a computational model of the hippocampus, performing the same task as the human participants. We used representational similarity analysis to examine how top-down expectation interacts with bottom-up perceptual input, in each layer. All subfields showed more dissimilar representations for unexpected items, with dentate gyrus (DG) and CA3 being more sensitive to expectation violation than CA1. Again, representational differences between expected and unexpected inputs were prominent for moderate to high levels of input similarity. This effect diminished when inputs from DG and CA3 into CA1 were lesioned. Overall, these novel findings strongly suggest that pattern separation in DG/CA3 underlies the effect that violation of expectation exerts on memory.

SIGNIFICANCE STATEMENT What makes some events more memorable than others is a key question in cognitive neuroscience. Violation of expectation often leads to better memory performance, but the neural mechanism underlying this benefit remains elusive. In a behavioral study, we found that memory accuracy is enhanced selectively for unexpected highly similar foils, suggesting expectation violation does not enhance memory indiscriminately, but specifically aids the disambiguation of overlapping inputs. This is further supported by our subsequent investigation using a hippocampal computational model, revealing increased representational dissimilarity for unexpected highly similar foils in DG and CA3. These convergent results provide the first evidence that pattern separation plays an explicit role in supporting memory for unexpected information.

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.

Structural plasticity of dendritic spines is a key component of the refinement of synaptic connections during learning. Recent studies highlight a novel role for the NMDA receptor (NMDAR), independent of ion flow, in driving spine shrinkage and LTD. Yet little is known about the molecular mechanisms that link conformational changes in the NMDAR to changes in spine size and synaptic strength. Here, using two-photon glutamate uncaging to induce plasticity at individual dendritic spines on hippocampal CA1 neurons from mice and rats of both sexes, we demonstrate that p38 MAPK is generally required downstream of non-ionotropic NMDAR signaling to drive both spine shrinkage and LTD. In a series of pharmacological and molecular genetic experiments, we identify key components of the non-ionotropic NMDAR signaling pathway driving dendritic spine shrinkage, including the interaction between NOS1AP (nitric oxide synthase 1 adaptor protein) and neuronal nitric oxide synthase (nNOS), nNOS enzymatic activity, activation of MK2 (MAPK-activated protein kinase 2) and cofilin, and signaling through CaMKII. Our results represent a large step forward in delineating the molecular mechanisms of non-ionotropic NMDAR signaling that can drive shrinkage and elimination of dendritic spines during synaptic plasticity.

SIGNIFICANCE STATEMENT Signaling through the NMDA receptor (NMDAR) is vitally important for the synaptic plasticity that underlies learning. Recent studies highlight a novel role for the NMDAR, independent of ion flow, in driving synaptic weakening and dendritic spine shrinkage during synaptic plasticity. Here, we delineate several key components of the molecular pathway that links conformational signaling through the NMDAR to dendritic spine shrinkage during synaptic plasticity.

Nestin, an intermediate filament protein widely used as a marker of neural progenitors, was recently found to be expressed transiently in developing cortical neurons in culture and in developing mouse cortex. In young cortical cultures, nestin regulates axonal growth cone morphology. In addition, nestin, which is known to bind the neuronal cdk5/p35 kinase, affects responses to axon guidance cues upstream of cdk5, specifically, to Sema3a. Changes in growth cone morphology require rearrangements of cytoskeletal networks, and changes in microtubules and actin filaments are well studied. In contrast, the roles of intermediate filament proteins in this process are poorly understood, even in cultured neurons. Here, we investigate the molecular mechanism by which nestin affects growth cone morphology and Sema3a sensitivity. We find that nestin selectively facilitates the phosphorylation of the lissencephaly-linked protein doublecortin (DCX) by cdk5/p35, but the phosphorylation of other cdk5 substrates is not affected by nestin. We uncover that this substrate selectivity is based on the ability of nestin to interact with DCX, but not with other cdk5 substrates. Nestin thus creates a selective scaffold for DCX with activated cdk5/p35. Last, we use cortical cultures derived from Dcx KO mice to show that the effects of nestin on growth cone morphology and on Sema3a sensitivity are DCX-dependent, thus suggesting a functional role for the DCX-nestin complex in neurons. We propose that nestin changes growth cone behavior by regulating the intracellular kinase signaling environment in developing neurons. The sex of animal subjects is unknown.

SIGNIFICANCE STATEMENT Nestin, an intermediate filament protein highly expressed in neural progenitors, was recently identified in developing neurons where it regulates growth cone morphology and responsiveness to the guidance cue Sema3a. Changes in growth cone morphology require rearrangements of cytoskeletal networks, but the roles of intermediate filaments in this process are poorly understood. We now report that nestin selectively facilitates phosphorylation of the lissencephaly-linked doublecortin (DCX) by cdk5/p35, but the phosphorylation of other cdk5 substrates is not affected. This substrate selectivity is based on preferential scaffolding of DCX, cdk5, and p35 by nestin. Additionally, we demonstrate a functional role for the DCX-nestin complex in neurons. We propose that nestin changes growth cone behavior by regulating intracellular kinase signaling in developing neurons.

FAO is working to help typhoon-affected farmers to ensure the next harvests in 2014 – You can help as well. Philippine farmers need urgent assistance  to avoid a double tragedy befalling rural survivors of Typhoon Haiyan. The typhoon hit just as farmers were beginning a new planting season, and FAO estimates that over one million farmers have been affected and hundreds of [...]

In 2015 we celebrated the “International Year of Soils” and with good reason. Soil sustains all our agricultural and livestock food production, wood for fuel production, filters water so that we can drink it and fish can live in it. We also use it for construction - therefore it sustains our homes and infrastructure. As we approach the end of #IYS [...]

In 2016 we celebrated the International Year of Pulses and it is obvious why. Pulses are good for you, beneficial to farmers' livelihoods and have a positive impact on the environment. It is clear that even though dried beans, lentils and peas have been around for centuries, they will play a fundamental role in our sustainable future. Even though #IYP2016 has [...]

Constituting only 5 percent of the world population, indigenous peoples nevertheless are vital stewards of the environment. Traditional indigenous territories encompass 22 percent of the world’s land surface, but 80 percent of the planet’s biodiversity.  A third of global forests, crucial for curbing gas emissions, are primarily managed by indigenous peoples, families, smallholders and local communities. Indigenous foods are also particularly [...]

Peste des petits ruminants (PPR) or sheep and goat plague is a highly contagious animal disease affecting small ruminants. An estimated 300 million families who rely on small ruminants, such as sheep and goats, as a source of food and income are at risk of losing their livelihoods and may be forced to migrate, particularly in areas where food insecurity, other resource shortages [...]

We often talk about the future of food, but what about its history? In our day to day lives, we might not realize that some of our staple foods have come from extraordinary agricultural traditions that are deeply rooted in our cultures and identity.

When one ponders the vast stretches of wheat being culled from the swaths of farmland in the Ukraine the mind doesn’t quickly leap to the thought of a pastry shop in Cairo. Or a bakery in Indonesia.  

Where would you find the world’s largest recreation center and the most natural supermarket? Forests wouldn’t have been your first answer, would it? That’s the thing about forests. They keep secrets.

A digital-savvy historian discusses his popular @HistOpinion Twitter account

Pizza and pasta are pretty obviously out, but what are the other no-nos?

A new biography explores the remarkable feats of Virginia Hall, a disabled secret agent determined to play her part in the fight against the Nazis

Two sites preserve around 50 footprints, a discovery that highlights the richness of prehistoric life on the island

But will that keep susceptible populations safe?

In its first year, the class attracted more than 1,200 students. The online version is abbreviated, but free

Despite the name, moon won’t have a rosy hue. The name alludes to flowers that bloom in April

Traces of lead pollution frozen in a glacier confirm that British lead production waned just before the death of Thomas Becket

Its paddle-like tail, unearthed in Morocco, suggests the Cretaceous carnivore ventured into the water to hunt

This jumping spider was personable and very easy to photograph as it was willing to sit still, facing me.