Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures

Several spectral unmixing techniques have been developed for subpixel mapping using hyperspectral data in the past two decades, among which the fully constrained least squares method based on the linear spectral mixture model (LSMM) has been widely accepted. However, the shortage of this method is that the Euclidean spectral distance measure is used, and therefore, it is sensitive to the magnitude of the spectra. While other spectral matching criteria are available, such as spectral angle mapping (SAM) and spectral information divergence (SID), the current unmixing algorithm is unable to be extended to these measures. In this paper, we propose a unified subpixel mapping framework that models the unmixing process as a best match of the unknown pixel\u27s spectrum to a weighted sum of the endmembers\u27 spectra. We introduce sequential quadratic programming to solve the nonlinear optimization problem encountered in the implementation of this framework. The main feature of this proposed method is that it is not restricted to any particular similarity measures. Experiments were conducted with both simulated and Hyperion data. The tests demonstrated the proposed framework\u27s advantage in accommodating various spectral similarity measures and provided performance comparisons of the Euclidean distance measure with other spectral matching criteria including SAM, spectral correlation measure, and SID

Tadalafil-loaded PLGA microspheres for pulmonary administration: preparation and evaluation

Tadalafil, a long-acting PED-5 inhibitor, is commonly used for the treatment of pulmonary arterial hypertension (PAH). However, its efficacy and clinical application are severely limited by the poor water solubility, low bioavailability and a series adverse effects (e.g. headaches, indigestion). In this study, tadalafil was prepared and loaded into biodegradable PLGA (poly(lactic-co-glycolic acid)) microspheres (TDF-PLGA-MS) via emulsification-solvent evaporation. The resulting microspheres were processed into pulmonary inhalant by freeze drying. The TDF-PLGA-MS was spherical and uniform, with an average particle diameter ~10.29 μm. The encapsulation efficiency and drug loading yield of TDF‑PLGA‑MS were 81.68% and 8.52%, respectively. The investigation of micromeritics showed that the TDF‑PLGA‑MS had low moisture content. The fluidity of powders was relatively good. The aerodynamic diameter and emptying rate of microspheres powders were 3.92 μm and 95.41%, respectively. Therefore, the microspheres powders were easy to be atomized, and can meet the requirements of pulmonary administration. In vitro release results showed that the microspheres group released slowly. The cumulative release in 24 h and 10 d was 46.87% and 84.06%, respectively. The in vitro release profile of TDF‑PLGA‑MS was in accordance with the Weibull model. The results of Pharmacokinetics showed that tadalafil from microspheres slowly released into the blood after intratracheal instillation. The pulmonary drug residue in 0.5 h was 3.5 times compared with solution group. The residual concentration in lung after 10d was still higher than that of solution group in 48 h. The t1/2β and MRT0-∞ were 3.10 times and 3.96 times that of solution group, respectively. Moreover, the Cmax and AUC of drug residues in lung were 3.48 times and 16.36 times that of solution group, respectively. The results of tissue distribution showed that the Re in lung was 16.358, which indicated the lung targeting. In conclusion, the TDF-PLGA-MS for pulmonary administration in this study can significantly improve the pulmonary targeting, increase efficacy of tadalafil and reduce other non-target organs toxicity. This study will have an important clinical significance for PAH patients who need long-term drug therapy

Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes

In this paper, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i.e., front and side views). Different from our prior work {\cite{wang2003virtual}}, a supervised learning approach based on \textit{convolutional neural network} (CNN) is investigated to solve the problem by establishing a mapping function that can effectively extract features from two silhouettes and fuse them into coefficients in the shape space of human bodies. A new CNN structure is proposed in our work to exact not only the discriminative features of front and side views and also their mixed features for the mapping function. 3D human models with high accuracy are synthesized from coefficients generated by the mapping function. Existing CNN approaches for 3D human modeling usually learn a large number of parameters (from {8.5M} to {355.4M}) from two binary images. Differently, we investigate a new network architecture and conduct the samples on silhouettes as input. As a consequence, more accurate models can be generated by our network with only {2.4M} coefficients. The training of our network is conducted on samples obtained by augmenting a publicly accessible dataset. Learning transfer by using datasets with a smaller number of scanned models is applied to our network to enable the function of generating results with gender-oriented (or geographical) patterns

SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent chronic breathing disorder caused by upper airway obstruction. Previous studies advanced OSAHS evaluation through machine learning-based systems trained on sleep snoring or speech signal datasets. However, constructing datasets for training a precise and rapid OSAHS evaluation system poses a challenge, since 1) it is time-consuming to collect sleep snores and 2) the speech signal is limited in reflecting upper airway obstruction. In this paper, we propose a new snoring dataset for OSAHS evaluation, named SimuSOE, in which a novel and time-effective snoring collection method is introduced for tackling the above problems. In particular, we adopt simulated snoring which is a type of snore intentionally emitted by patients to replace natural snoring. Experimental results indicate that the simulated snoring signal during wakefulness can serve as an effective feature in OSAHS preliminary screening

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin