Berlin : S. Karger, 1899.

Stuttgart : F. Enke, 1878.

Breslau : W. Koebner, 1888.

Wien : L.W. Seidel, 1875.

London : printed for Knight and Lacey, 1826.

London : Kenny & Co, [1895?]

London : Longmans, Green, 1868.

London : Bailliere, Tindall and Cox, 1884.

Wien : SafÏar, 1905.

Genova : G. Bonaudo, 1812.

Many are whipsawed by shutdown recommendations aimed at stemming the coronavirus, and the logistical and financial consequences of those actions.

Michael Martirano led the state's schools through dramatic budget cuts, academic challenges, and a state-versus-local battle over school construction.

State lawmakers have made it illegal for school districts to spend any public money on the Mississippi Association of School Superintendents, saying district leaders personally attacked state officials while they were seeking votes for a school funding initiative last year.

Brian Whiston since 2015 has led the state's education department while it put together its plan under the Every Student Succeeds Act and took control of the state's school takeover district.

Nathel Burtley, 79, was a lifelong educator who led the Flint, Mich., school system, paving the way for other African-American educators to become the heads of districts. He died earlier this month from the coronavirus.

David Schuler, the superintendent of Township High School District 214 in Arlington Heights, Ill., has been named 2018 National Superintendent of the Year.

Controversy over an intiative aimed a reducing inequities in Lee's Summit, Mo., schools led the police department to provide security protection for the district's first African-American superintendent. Now the school board has reversed course.

Rockville, Maryland : National Institute on Drug Abuse, 1978.

[London] : [publisher not identified], [2019]

[London] : [publisher not identified], [2019]

Uttam Bandyopadhyay, Shirsendu Mukherjee, Atanu Biswas.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 291--303.

Abstract:
In adaptive crossover design, our goal is to allocate more patients to a promising treatment sequence. The present work contains a very simple three period crossover design for two competing treatments where the allocation in period 3 is done on the basis of the data obtained from the first two periods. Assuming normality of response variables we use a reliability functional for the choice between two treatments. We calculate the allocation proportions and their standard errors corresponding to the possible treatment combinations. We also derive some asymptotic results and provide solutions on related inferential problems. Moreover, the proposed procedure is compared with a possible competitor. Finally, we use a data set to illustrate the applicability of the proposed design.

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods.

Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMINis presented, which incorporates all of the techniques studied.

I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes may vary in a given set. When this set is permutation symmetric, the optimal design is complete randomization, and using a single partition (i.e., the design that only randomizes the treatment labels for each side of the partition) has minimax risk larger by a factor of $n-1$. More generally, the optimal design is shown to be the mixed-strategy optimal design (MSOD) of Kallus (2018). Notably, even when the set of conditional mean outcomes has structure (i.e., is not permutation symmetric), being minimax-optimal for variance still requires randomization beyond a single partition. Nonetheless, since this targets precision, it may still not ensure sufficient uniformity in randomization to enable randomization (i.e., design-based) inference by Fisher's exact test to appropriately detect violations of null. I therefore propose the inference-constrained MSOD, which is minimax-optimal among all designs subject to such uniformity constraints. On the way, I discuss Johansson et al. (2020) who recently compared rerandomization of Morgan and Rubin (2012) and the pure-strategy optimal design (PSOD) of Kallus (2018). I point out some errors therein and set straight that randomization is minimax-optimal and that the "no free lunch" theorem and example in Kallus (2018) are correct.

The last seminar in the 2017–18 History of Pre-Modern Medicine seminar series takes place on Tuesday 27 February. Speaker: Dr Dror Weil (Max Planck Institute for the History of Science, Berlin) Bodies translated: the circulation of Arabo-Persian physiological theories in late… Continue reading

Xinran Li, Peng Ding, Donald B. Rubin.

Source: The Annals of Statistics, Volume 48, Number 1, 43--63.

Abstract:
With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of factorial effects and covariates based on their relative importances. This is rerandomization in factorial experiments. We study the asymptotic properties of this experimental design under the randomization inference framework without imposing any distributional or modeling assumptions of the covariates and outcomes. We derive the joint asymptotic sampling distribution of the usual estimators of the factorial effects, and show that it is symmetric, unimodal and more “concentrated” at the true factorial effects under rerandomization than under the classical factorial experiment. We quantify this advantage of rerandomization using the notions of “central convex unimodality” and “peakedness” of the joint asymptotic sampling distribution. We also construct conservative large-sample confidence sets for the factorial effects.

Kun Liang.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2452--2482.

Abstract:
Finding differentially expressed genes is a common task in high-throughput transcriptome studies. While traditional statistical methods rank the genes by their test statistics alone, we analyze an RNA sequencing dataset using the auxiliary information of gene length and the test statistics from a related microarray study. Given the auxiliary information, we propose a novel nonparametric empirical Bayes procedure to estimate the posterior probability of differential expression for each gene. We demonstrate the advantage of our procedure in extensive simulation studies and a psoriasis RNA sequencing study. The companion R package calm is available at Bioconductor.

Hannah Worthington, Rachel McCrea, Ruth King, Richard Griffiths.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2043--2064.

Abstract:
Capture-recapture studies often involve collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example, annually, resulting in capture information spanning multiple sampling periods. To account for the different temporal scales, the robust design class of models have traditionally been applied providing a framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically require strong constraints, either the assumption of closure within a sampling period (the closed robust design) or conditioning on the number of individuals captured within a sampling period (the open robust design). For real datasets these assumptions may not be appropriate. We develop a general modelling structure that requires neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance within a single consistent framework. The flexibility of the novel model structure is further demonstrated by including the computationally challenging case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the significant improvement in parameter estimation using our new modelling approach in terms of both the multi-period and multi-state components through both a simulation study and a real dataset relating to the protected species of great crested newts, Triturus cristatus .

Antony Overstall, James McGree.

Source: Bayesian Analysis, Volume 15, Number 1, 103--131.

Abstract:
A Bayesian design is given by maximising an expected utility over a design space. The utility is chosen to represent the aim of the experiment and its expectation is taken with respect to all unknowns: responses, parameters and/or models. Although straightforward in principle, there are several challenges to finding Bayesian designs in practice. Firstly, the utility and expected utility are rarely available in closed form and require approximation. Secondly, the design space can be of high-dimensionality. In the case of intractable likelihood models, these problems are compounded by the fact that the likelihood function, whose evaluation is required to approximate the expected utility, is not available in closed form. A strategy is proposed to find Bayesian designs for intractable likelihood models. It relies on the development of an automatic, auxiliary modelling approach, using multivariate Gaussian process emulators, to approximate the likelihood function. This is then combined with a copula-based approach to approximate the marginal likelihood (a quantity commonly required to evaluate many utility functions). These approximations are demonstrated on examples of stochastic process models involving experimental aims of both parameter estimation and model comparison.

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.

Chao Du, S. C. Kou.

Source: Statistical Science, Volume 35, Number 1, 75--91.

Abstract:
Toward the last quarter of the 20th century, the emergence of single-molecule experiments enabled scientists to track and study individual molecules’ dynamic properties in real time. Unlike macroscopic systems’ dynamics, those of single molecules can only be properly described by stochastic models even in the absence of external noise. Consequently, statistical methods have played a key role in extracting hidden information about molecular dynamics from data obtained through single-molecule experiments. In this article, we survey the major statistical methodologies used to analyze single-molecule experimental data. Our discussion is organized according to the types of stochastic models used to describe single-molecule systems as well as major experimental data collection techniques. We also highlight challenges and future directions in the application of statistical methodologies to single-molecule experiments.

Karim Fifel
Jan 24, 2018; 38:784-786
Journal Club

Stefan Klöppel
Mar 3, 2010; 30:3271-3275
BRIEF COMMUNICATION

Joshua A. Gordon
May 15, 1996; 16:3274-3286
Articles

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.

The superior colliculus (SC) is arguably the most important visual structure in the mouse brain and is well known for its involvement in innate responses to visual threats and prey items. In other species, the SC plays a central role in voluntary as well as innate visual functions, including crucial contributions to selective attention and perceptual decision-making. In the mouse, the possible role of the SC in voluntary visual choice behaviors has not been established. Here, we demonstrate that the mouse SC of both sexes plays a causal role in visual perceptual decision-making by transiently inhibiting SC activity during an orientation change detection task. First, unilateral SC inhibition-induced spatially specific deficits in detection. Hit rates were reduced, and reaction times increased for orientation changes in the contralateral but not ipsilateral visual field. Second, the deficits caused by SC inhibition were specific to a temporal epoch coincident with early visual burst responses in the SC. Inhibiting SC during this 100-ms period caused a contralateral detection deficit, whereas inhibition immediately before or after did not. Third, SC inhibition reduced visual detection sensitivity. Psychometric analysis revealed that inhibiting SC visual activity significantly increased detection thresholds for contralateral orientation changes. In addition, effects on detection thresholds and lapse rates caused by SC inhibition were larger in the presence of a competing visual stimulus, indicating a role for the mouse SC in visual target selection. Together, our results demonstrate that the mouse SC is necessary for the normal performance of voluntary visual choice behaviors.

SIGNIFICANCE STATEMENT The mouse superior colliculus (SC) has become a popular model for studying the circuit organization and development of the visual system. Although the SC is a fundamental component of the visual pathways in mice, its role in visual perceptual decision-making is not clear. By investigating how temporally precise SC inhibition influenced behavioral performance during a visually guided orientation change detection task, we identified a 100-ms temporal epoch of SC visual activity that is crucial for the ability of mice to detect behaviorally relevant visual changes. In addition, we found that SC inhibition also caused deficits in visual target selection. Thus, our findings highlight the importance of the SC for visual perceptual choice behavior in the mouse.

The spacecraft Hayabusa2 hurled a four-pound copper ball toward the asteroid's surface at about 4,500 miles an hour to create an artificial crater

Traditionalist "two-spacers" can still disable the function

Stay-at-home orders have inspired those with ample free time to pick up hands-on projects

Serving in West and Central Asia for many years, Paul and Soonok have one lasting dream: to see a church planting movement there.

Press release: CPMI and IOSCO share authorities´ experiences in cooperation to increase FMI safety and efficiency, 10 December 2019

What this country needs is another group with a very long name.

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