Assessment of discrimination with area under the receiver operating characteristic (ROC) curve.

<p>Discrimination of APACHE III to predict mortality in overall ALI patients was moderate with an AUC of 0.68 (95% CI: 0.64–0.73). The red line shows sensitivity analysis by restricting to patients in the control arm. The results showed an AUC of 0.68 (95% CI: 0.62–0.75).</p

Multivariate Functional Regression Via Nested Reduced-Rank Regularization

We propose a nested reduced-rank regression (NRRR) approach in fitting a regression model with multivariate functional responses and predictors to achieve tailored dimension reduction and facilitate model interpretation and visualization. Our approach is based on a two-level low-rank structure imposed on the functional regression surfaces. A global low-rank structure identifies a small set of latent principal functional responses and predictors that drives the underlying regression association. A local low-rank structure then controls the complexity and smoothness of the association between the principal functional responses and predictors. The functional problem boils down to an integrated matrix approximation task through basis expansion, where the blocks of an integrated low-rank matrix share some common row space and/or column space. This nested reduced-rank structure also finds potential applications in multivariate time series modeling and tensor regression. A blockwise coordinate descent algorithm is developed. We establish the consistency of NRRR and show through nonasymptotic analysis that it can achieve at least a comparable error rate to that of the reduced-rank regression. Simulation studies demonstrate the effectiveness of NRRR. We apply the proposed methods in an electricity demand problem to relate daily electricity consumption trajectories with daily temperatures. Supplementary files for this article are available online.</p

Comparisons of baseline characteristics between survivors and non-survivors.

<p>Note:</p><p>* p<0.001 compared between survivors and nonsurvivors.</p><p>^<b>¶</b> The “primary” should be the most immediate cause. For example, a patient with multiple trauma who develops sepsis and then ALI: primary cause = sepsis; secondary cause = trauma.</p><p>^<b>§</b> For volume targeted mode.</p><p>^<b>$</b> Sepsis can be defined as proven or suspected. Suspected infection here means those without proven infection and the infection site is not known.</p><p>Abbreviations:</p><p>APACHE III: Acute Physiology and Chronic Health Evaluation III</p><p>ALI: acute lung injury.</p><p>MICU: medical ICU</p><p>SICU: surgical ICU</p><p>CCU: coronary care unit</p><p>MAP: mean arterial pressure.</p><p>Comparisons of baseline characteristics between survivors and non-survivors.</p

Predicted versus observed probability of death.

<p>The observed probability was calculated by categorizing predicted probability of death into eight subgroups. It is obvious that the observed probability of death increases monotonically.</p

The complete chloroplast genome and phylogenetic analysis of <i>Cyperus malaccensis</i> Lam (Cyperaceae)

Cyperus malaccensis Lam is a perennial herbaceous plant that is distributed over a large area along the southern coast of China. Some plants of the Cyperaceae family are highly similar morphologically, which makes them difficult to classify and identify. In this study, the complete chloroplast genome of C. malaccensis was sequenced and assembled. The chloroplast genome is 186,098 bp long with a 33.18% content of GC. The structure of chloroplast genome includes a quadripartite structure that is composed of a pair of inverted repeats (IRs) of 37,434 bp, a small single copy (SSC) region of 10,296 bp, and a large single copy (LSC) region of 100,934 bp. The genome contains 141 genes, including 94 protein-coding genes, 39 tRNA genes, and 8 rRNA genes. A phylogenetic analysis showed that C. malaccensis is the most closely related to the congeneric species C. rotundus. These results enrich the genetic resources of the Cyperaceae and provide a molecular basis for further study on the phylogeny of this family.</p

Markov-HTN Planning Approach to Enhance Flexibility of Automatic Web Services Composition

Automatic Web services composition can be achieved by using AI planning techniques. HTN planning has been adopted to handle the OWL-S Web service composition problem. However, existing composition methods based on HTN planning have not considered the choice of decompositions available to a problem which can lead to a variety of valid solutions. In this paper, we propose a model of combining a Markov decision process model and HTN planning to address Web services composition. In the model, HTN planning is enhanced to decompose a task in multiple ways and hence be able to find more than one plan, taking both functional and non-functional properties into account. Furthermore, an evaluation method to choose the optimal plan and some experimental results illustrate that the proposed approach works effectively

Protocol for the Nanocasting Method: Preparation of Ordered Mesoporous Metal Oxides

Ordered mesoporous transition metal oxides have attracted considerable research attention due to their unique properties and wide applications. The preparation of these materials has been reported in the literature using soft and hard templating pathways. Compared with soft templating, hard templating, namely, nanocasting, is advantageous for synthesizing rigid mesostructures with high crystallinity and has already been applied to numerous transition metal oxides such as Co₃O₄, NiO, Fe₂O₃, and Mn₃O₄. However, nanocasting is often complicated by the multiple steps involved: first, the preparation of ordered mesoporous silica as the hard template, then infiltration of the metal precursor into the pores, and finally, formation of the metal oxide and removal of the hard template. In this paper, we provide a complete protocol that covers the preparation of most widely used ordered mesoporous silica templates (MCM-41, KIT-6, SBA-15) and the nanocasting process for obtaining ordered mesoporous metal oxides, with emphasizing cobalt oxide as an example. Characterization of the products is presented, and the factors that can potentially affect the process are discussed

Iridium-Based Lab-on-a-Molecule for Hg²⁺ and ClO^– with Two Distinct Light-Up Emissions

The nonemissive iridium complex 2 is a lab-on-a-molecule for the highly selective detection of Hg2+ and ClO– among 33 analytes using its oxime residues as reactive units. At pH 5, chemodosimeter 2 responds to Hg2+ by dehydration, whereas at pH 8, it is oxidized by ClO–, resulting in 450- and 235-fold emission increases, respectively, at two distinct wavelengths

Iridium-Based Lab-on-a-Molecule for Hg²⁺ and ClO^– with Two Distinct Light-Up Emissions

The nonemissive iridium complex <b>2</b> is a lab-on-a-molecule for the highly selective detection of Hg²⁺ and ClO^– among 33 analytes using its oxime residues as reactive units. At pH 5, chemodosimeter <b>2</b> responds to Hg²⁺ by dehydration, whereas at pH 8, it is oxidized by ClO^–, resulting in 450- and 235-fold emission increases, respectively, at two distinct wavelengths

Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data

Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer from the inability of producing interpretable results or clinically-meaningful risk factors. To improve EHR-based modeling and use the natural hierarchical structure of disease classification, we propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features, in which dimension reduction is achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of “or”. We convert the combinatorial problem into a convex linearly-constrained regularized estimation, which enables scalable computation with theoretical guarantees. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases. By balancing the rarity and specificity of the EHR diagnosis records, our strategy improves both prediction and interpretation. We identify important higher-level categories and subcategories of mental health conditions and simultaneously determine the level of specificity needed for each of them in associating with suicide risk. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.</p