Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference

Sparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed and their theoretical properties are investigated. Both minimax upper and lower bounds are established and the results show that the proposed algorithms are rate-optimal, up to a logarithmic factor. Moreover, a refitting algorithm is proposed to establish asymptotic normality and construct valid confidence intervals for the individual entries of the word-topic and topic-document matrices. Simulation studies are carried out to investigate the numerical performance of the proposed algorithms. The results show that the proposed algorithms perform well numerically and are more accurate in a range of simulation settings comparing to the existing literature. In addition, the methods are illustrated through an analysis of the COVID-19 Open Research Dataset (CORD-19).</p

Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer

Transfer learning provides a powerful tool for incorporating data from related studies into a target study of interest. In epidemiology and medical studies, the classification of a target disease could borrow information across other related diseases and populations. In this work, we consider transfer learning for high-dimensional generalized linear models (GLMs). A novel algorithm, TransHDGLM, that integrates data from the target study and the source studies is proposed. Minimax rate of convergence for estimation is established and the proposed estimator is shown to be rate-optimal. Statistical inference for the target regression coefficients is also studied. Asymptotic normality for a debiased estimator is established, which can be used for constructing coordinate-wise confidence intervals of the regression coefficients. Numerical studies show significant improvement in estimation and inference accuracy over GLMs that only use the target data. The proposed methods are applied to a real data study concerning the classification of colorectal cancer using gut microbiomes, and are shown to enhance the classification accuracy in comparison to methods that only use the target data.</p

Tone contours of the continuum from /ba2/ to /ba4/.

<p>Continua 3, 7 and 11 are marked with thick lines.</p

Overt discrimination of stimuli in the fMRI experiment obtained from 13 participants 6 months after scanning.

<p>* <i>p</i><0.05, *** <i>p</i><0.001.</p

Areas of significant activation.

<p>Note: Areas identified in the group analysis for all the planned comparisons, thresholded at a voxel-wise <i>P</i><0.005 (<i>t</i>>2.248). Cluster level activated volume ≥328 mm³ (<i>P</i><0.05, corrected). Coordinates are in Talairach and Tournoux (1988) space.</p

Mechanically Robust and Healable Bromobutyl Rubber Ionomer via Designing the Resonance Isomerization Effect

Integrating mechanical robustness with efficient healability under mild conditions is an inevitable requirement for the commercial development of self-healing ionomers. This work reveals that the resonance isomerization effect can be adopted to construct vigorous ionic networks in ionomers. As a proof of concept, 4-(alkylamino) pyridine (DMAP) and its derivatives are selected to react with bromobutyl rubber (BIIR) to fabricate BIIR ionomers. The ionized DMAP moieties manifest a resonance isomerization effect, leading to stronger ionic interactions, larger regular ionic aggregations, and more obvious microphase separation. Benefiting from the resonance isomerization effect, the BIIR ionomers in our work possess superior tensile strength (21 MPa) and toughness (92 MJ/m3), exceeding those of the existing BIIR-based materials. Moreover, the plasticizing effect of the alkyl substituent group on the DMAP-based derivatives can be further used to tailor the ionic cluster relaxation, thereby overcoming the compromise between mechanical performance and self-healing ability. Despite the dynamic network, the self-healing BIIR ionomers show a high gas barrier property which is very close to that of covalently crosslinked BIIR, enabling their potential to be used in next-generation repairable automobile tires. This work will expand the toolbox of ionic bond chemistry and afford an effective molecular design approach for optimizing mechanical and self-healing properties simultaneously

Activation in within-category deviant vs. across-category deviant contrast.

<p>(A) Significant foci of activity for within-category deviant > across-category deviant (blue) and across-category deviant > within-category deviant (orange). (B) Mean percent BOLD signal change extracted from ROIs based on functionally defined activation clusters in the left mMTG (x = −53, y = −25, z = −10) and right lateral HG (x = 49, y = −9, z = 6). ** <i>p</i><0.01, *** <i>p</i><0.001.</p

Schematic illustration of the experimental paradigm.

<p>Schematic illustration of the experimental paradigm.</p

Blood Prefabrication Subcutaneous Small Animal Model for the Evaluation of Bone Substitute Materials

Because of the size of bone substitute material particles, large animal bone defect models are usually required for the assessment of these materials. However, these models have several disadvantages including high cost, complicated operation procedures, ethical issues, and difficulties in sample analysis. In addition, for mimicking the bone environment, conventional subcutaneous models require the addition of osteogenic factors and stem cells, resulting in an expensive model with a complex experimental procedure. To avoid these issues, in this study, we proposed a convenient and effective blood prefabrication subcutaneous small animal model that could be applied to assess bone substitute materials. Our results demonstrated that blood prefabrication could be an economical, convenient, and useful “adhesive” for handling bone substitute particles. This process provided porcine hydroxyapatite (PHA) with a microenvironment enriched with mesenchymal stem cells and growth factors. Using this strategy, a bonelike structure could form in a rat subcutaneous pocket. Furthermore, the optimized subcutaneous model was used to evaluate the PHA’s osteoinductivity, producing results similar to those of the calvarial bone defect in terms of osteogenesis, osteoclastogenesis, and blood vessel formation. These results collectively imply that the blood prefabrication subcutaneous small animal model is convenient and effective for the assessment of osteoinductivity of bone substitute materials

Highly Sensitive Multifunctional Electronic Skin Based on Nanocellulose/MXene Composite Films with Good Electromagnetic Shielding Biocompatible Antibacterial Properties

Electronic skin has aroused extensive research interest due to high similarity with human skin. Realizing a multifunctional electronic skin that is highly consistent with skin functions and endowed with more other functions is now a more urgent need and important challenge. Here, we use 2,2,6,6-tetramethyl­piperidinyl-1-oxyl (TEMPO)-oxidized cellulose nanofibril (TOCN) dispersion and highly conductive Ti3C2TX dispersion to prepare TOCN/Ti3C2TX composite film through vacuum-assisted filtration. The obtained composite film imitating the nacre-like lamellar structure of natural shells has good mechanical properties (124.6 MPa of tensile strength). Meanwhile, the composite film also showed excellent electromagnetic shielding performance (36 dB), biocompatibility, and antibacterial properties. In addition, the piezoresistive sensor assembled from the composite film exhibited a high sensitivity (11.6 kPa–1), fast response and recovery time (≤10 ms), ultralow monitoring limit (0.2 Pa), and long-term stability (>10 000 cycles). It also could detect human daily activities such as finger bent, chewing, and so on