An Asymptotically Optimal Adaptive Selection Procedure in the Proportional Hazards Model with Conditionally Independent Censoring

Assume k independent populations are given which are distributed according to R, …,R (ϑ_i ∈ Θ ⊆ R ). Taking samples of size n the population with the smallest ϑ-value is to be selected. Using the framework of Le Cam's decision theory (Le Cam , 1986; Strasser , 1985) under mild regularity assumptions, an asymptotically optimal selection procedure is derived for the sequence of localized models. In the proportional hazards model with conditionally independent censoring, an asymptotically optimal adaptive selection procedure is constructed by substituting the unknown nuisance parameter by a kernel estimator.     

An Automated Procedure for Evaluating Song Imitation

Songbirds have emerged as an excellent model system to understand the neural basis of vocal and motor learning. Like humans, songbirds learn to imitate the vocalizations of their parents or other conspecific “tutors.” Young songbirds learn by comparing their own vocalizations to the memory of their tutor song, slowly improving until over the course of several weeks they can achieve an excellent imitation of the tutor. Because of the slow progression of vocal learning, and the large amounts of singing generated, automated algorithms for quantifying vocal imitation have become increasingly important for studying the mechanisms underlying this process. However, methodologies for quantifying song imitation are complicated by the highly variable songs of either juvenile birds or those that learn poorly because of experimental manipulations. Here we present a method for the evaluation of song imitation that incorporates two innovations: First, an automated procedure for selecting pupil song segments, and, second, a new algorithm, implemented in Matlab, for computing both song acoustic and sequence similarity. We tested our procedure using zebra finch song and determined a set of acoustic features for which the algorithm optimally differentiates between similar and non-similar songs.     

An Improved Isolation Procedure for Adult Mouse Cardiomyocytes

Isolated adult mouse cardiomyocytes are an important tool in cardiovascular research, but are challenging to prepare. Because the energy supply determines cell function and viability, we compared total creatine ([Cr]) and [ATP] in isolated cardiomyocytes with the intact mouse heart. Isolated myocytes suffered severe losses of Cr (−70%) and ATP (−53%). Myocytes were not able to replete [Cr] during a 5 h incubation period in medium supplemented with 1 mM Cr. In contrast, adding 20 mM Cr to the digestion buffers was sufficient to maintain normal [Cr]. Supplementing buffers with 5 mM of inosine (Ino) and adenosine (Ado) to prevent loss of cellular nucleosides partially protected against loss of ATP. To test whether maintaining [ATP] and [Cr] improves contractile function, myocytes were challenged by varying pacing rate from 0.5 to 10 Hz and by adding isoproterenol (Iso) at 5 and 10 Hz. All groups performed well up to 5 Hz, showing a positive cell shortening–frequency relationship; however, only 16% of myocytes isolated under standard conditions were able to sustain pacing with Iso challenge at 10 Hz. In contrast, 30–50% of the myocytes with normal Cr levels were able to contract and maintain low diastolic [Ca²⁺]. Cell yield also improved in Cr and the Cr/Ino/Ado-treated groups (85–90% vs. 70–75% rod shaped in untreated myocytes). These data suggest that viability and performance of isolated myocytes are improved when they are protected from the severe loss of Cr and ATP during the isolation, making them an even better research tool.     

An Automated Double Staining Procedure for Bone and Cartilage

Differential skeletal staining is an important part of developmental toxicologic studies. Traditionally these studies have required time-consuming differentiation of one or both stains used and careful attention to the maceration step to prevent specimen destruction. We present a fully automated protocol which does not require differentiation of either dye and incorporates a controlled maceration step which is highly reproducible. This has resulted in high quality staining that is reproducible, stable, and can be done in volume with minimal personnel time. The process involves the staining of skinned, eviscerated specimens fixed in 95% ethanol. Using an automated tissue processor, the specimen is stained in alcian blue for 24 hr, macerated in 3% potassium hydroxide for 24 hr and stained with murexide for 24 hr. The specimens are cleared and preserved in glycerol. Within three days specimens have red stained bone and blue stained cartilage. The procedure was developed using 20-day-old Sprague-Dawley rat fetuses to evaluate the feasibility of using the procedure for teratology studies involving the fetal skeleton. Evenly stained specimens can be examined within three days and stored for years without loss of staining.     

An Improved Procedure for Subcellular Spatial Alignment during Live-Cell CLEM

Live-cell correlative light and electron microscopy (CLEM) offers unique insights into the ultrastructure of dynamic cellular processes. A critical and technically challenging part of CLEM is the 3-dimensional relocation of the intracellular region of interest during sample processing. We have developed a simple CLEM procedure that uses toner particles from a laser printer as orientation marks. This facilitates easy tracking of a region of interest even by eye throughout the whole procedure. Combined with subcellular fluorescence markers for the plasma membrane and nucleus, the toner particles allow for precise subcellular spatial alignment of the optical and electron microscopy data sets. The toner-based reference grid is printed and transferred onto a polymer film using a standard office printer and laminator. We have also designed a polymer film holder that is compatible with most inverted microscopes, and have validated our strategy by following the ultrastructure of mitochondria that were selectively photo-irradiated during live-cell microscopy. In summary, our inexpensive and robust CLEM procedure simplifies optical imaging, without limiting the choice of optical microscope.     

An Enzyme-Linked Immunosorbent Assay-Based Isolation Procedure for Verotoxigenic Escherichia coli

[This corrects the article on p. 4228 in vol. 59.].     

An On-the-Slide Procedure for Microscopic Observation During Decalcification of Sections

A thin section of tooth was cemented to a microscope slide with Kodak 910 Adhesive and ground down to a thickness of approximately 6 μ The section was covered with a thin layer of dental impression material by squeezing it out under cellophane, using pressure on a second microscope slide. A channel was cut in the impression material exposing a portion of the tooth substance. The channel was covered with a coverglass enabling decalcifying solutions to be passed over the exposed area of the tooth and the section to be observed at the same time. This procedure enables sections of calcified tissue to be decalcified very slowly and the histological changes to be observed microscopically.     

Low-Rank Regularization for Learning Gene Expression Programs

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.     

An Adaptive Complex Network Model for Brain Functional Networks

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.     

An Exponential Combination Procedure for Set-Based Association Tests in Sequencing Studies

State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives—that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk—a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs.     

Ridge regression for longitudinal biomarker data

Technological advances facilitating the acquisition of large arrays of biomarker data have led to new opportunities to understand and characterize disease progression over time. This creates an analytical challenge, however, due to the large numbers of potentially informative markers, the high degrees of correlation among them, and the time-dependent trajectories of association. We propose a mixed ridge estimator, which integrates ridge regression into the mixed effects modeling framework in order to account for both the correlation induced by repeatedly measuring an outcome on each individual over time, as well as the potentially high degree of correlation among possible predictor variables. An expectation-maximization algorithm is described to account for unknown variance and covariance parameters. Model performance is demonstrated through a simulation study and an application of the mixed ridge approach to data arising from a study of cardiometabolic biomarker responses to evoked inflammation induced by experimental low-dose endotoxemia.     

Regularization network-based gene selection for microarray data analysis

Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.     

An Improved Isolation Procedure for the Gas Chromatographic Analysis of Urinary Polyamines

The isolation of polyamines from urinary hydrolysates in a sufficiently pure state for subsequent analysis by gas chromatography has proved to be difficult. However, by using columns of Porapak-Q and ion-exchange resins, urinary hydrolysates are readily purified and formation of trifluoroacetyl derivatives of polyamines proceeds in high yield without carryover of artifacts in the gas chromatographic elution profile. Good yields from the trifluoroacetylation reaction are not achieved if large quantities of salts or urinary pigments are present. By obtaining the polyamine carbonates in the final stages of the method described, the trifluoroacetylation reaction yields excellent derivatives of nanogram or microgram amounts, particularly after standing over-night at room temperature. The procedure described in detail should permit routine urinary polyamine analysis where rapidity, ease of handling many samples, freedom from complications and artifacts are a consideration. The recent reports by Russell^{1, 2} that the urinary excretion of polyamines are greatly elevated in cancer patients has stimulated interest in these compounds as possible biological “markers” for the diagnosis of cancer. The polyamines usually considered are: putrescine, 1, 4-diaminobutane; cadaverine, 1, 5-diaminopentane; spermidine, and spermine. An extensive literature has developed over the last 50 years concerning the isolation and determination of polyamines including many excellent reviews. ^3–5 However, the isolation and determination of small quantities of polyamines from biological sources has proven to be difficult. This has led to conflicting conclusions among investigators as to which polyamine is the major excretion product in the urine of cancer patients. ^{2, 6, 7, 8} The following report presents in detail a new procedure of isolation of urinary polyamines in high yield and pure state that facilitates quantitation of these amines by gas chromatography.     

A Direct Screening Procedure for Gravitropism Mutants in Arabidopsis thaliana (L.) Heynh.

In order to isolate gravitropism mutants of Arabidopsis thaliana (L.) Heynh. var Estland for the genetic dissection of the gravitropism pathway, a direct screening procedure has been developed in which mutants are selected on the basis of their gravitropic response. Variability in hypocotyl curvature was dependent on the germination time of each seed stock, resulting in the incorrect identification of several lines as gravitropism mutants when a standard protocol for the potentiation of germination was used. When the protocol was adjusted to allow for differences in germination time, these lines were eliminated from the collection. Out of the 60,000 M2 seedlings screened, 0.3 to 0.4% exhibited altered gravitropism. In approximately 40% of these mutant lines, only gravitropism by the root or the hypocotyl was altered, while the response of the other organ was unaffected. These data support the hypothesis that root and hypocotyl gravitropism are genetically separable.     

An Approach for Adaptive Limbless Locomotion Using a CPG-Based Reflex Mechanism

Animals＇ free movement in natural environments has attracted many researchers to explore control methods for bio-inspired robots. This paper presents a novel reflex mechanism based on a Central Pattern Generator （CPG） for adaptive locomotion of limbless robots. First, inspired by the concept of reflex arc, the reflex mechanism is designed on a connectionist CPG model. Since the CPG model inspired by the spinal cord of lampreys is developed at the neuron level, it provides a possible natural solution for sensory reflex integration. Therefore, sensory neurons that bridge the external stimuli and the CPG model, together with the concept of reflex arc, are utilized for designing the sensory reflex mechanism. Then, a border reflex and a body reflex are further developed and applied on the ends and the middle part of a limbless robot, respectively. Finally, a ball hitting scenario and a corridor passing scenario are designed to verify the proposed method. Results of simulations and on-site experiments show the feasibility and effectiveness of the reflex mechanism in realizing fast response and adaptive limbless locomotion.     

Yahtzee: An Anonymized Group Level Matching Procedure

Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects ever more important. However, strict privacy procedures can hinder the process of joining diverse sources of data that contain information about specific individual behaviors. In this paper we present a procedure to keep information about specific individuals from being “leaked” or shared in either direction between two sources of data without need of a trusted third party. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as predicted by theoretical analysis when we apply it to data from Facebook and public voter records.