Low bandwidth input.avi

This video contains the simulated Stokes spectra with a relatively low spectral bandwidth

High bandwidth input.avi

This video contains the simulated Stokes spectra with a relatively high spectral bandwidth

Supplementary document for Spectral-temporal channeled spectropolarimetry using deep-learning-based adaptive filtering - 5381374.pdf

Supplement

BASARD- Bayesian Approach for Short Adjacent Repeat Detection

<p>the source code and windows executable file for Bayesian Approach for Short Adjacent Repeat Detection, a tool written in C++</p> <p> </p

DataSheet1_Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.PDF

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.</p

Cytidine-Directed Rapid Synthesis of Water-Soluble and Highly Yellow Fluorescent Bimetallic AuAg Nanoclusters

Fluorescent gold/silver nanoclusters templated by DNA or oligonucleotides have been widely reported since DNA or oligonucleotides could be designed to position a few metal ions at close proximity prior to their reduction, but nucleoside-templated synthesis is more challenging. In this work, a novel type of strategy taking cytidine (C) as template to rapid synthesis of fluorescent, water-soluble gold and silver nanoclusters (C-AuAg NCs) has been developed. The as-prepared C-AuAg NCs have been characterized by UV–vis absorption spectroscopy, fluorescence, transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), and inductively coupled plasma mass spectroscopy (ICP-MS). The characterizations demonstrate that C-AuAg NCs with a diameter of 1.50 ± 0.31 nm, a quantum yield ∼9%, and an average lifetime ∼6.07 μs possess prominent fluorescence properties, good dispersibility, and easy water solubility, indicating the promising application in bioanalysis and biomedical diagnosis. Furthermore, this strategy by rapid producing of highly fluorescent nanoclusters could be explored for the possible recognition of some disease-related changes in blood serum. This raises the possibility of their promising application in bioanalysis and biomedical diagnosis

Means and standard deviations (in parenthesis) of differences in marginal probability between correctly predicted secondary structure (Correct) and the next highest probability, and between secondary structure predicted incorrectly (Wrong) and highest probability for ASTRAL30 and CASP9 data sets.

<p>Means and standard deviations (in parenthesis) of differences in marginal probability between correctly predicted secondary structure (Correct) and the next highest probability, and between secondary structure predicted incorrectly (Wrong) and highest probability for ASTRAL30 and CASP9 data sets.</p

Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images

Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this article, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value 0.001). Supplementary materials for this article are available online.</p

Classification Q3 recall (%) of TorusDBN, PSIPRED, and our method under different priors on ASTRAL30 test dataset.

<p>^*Each column of the matrix represents the instances in an actual class, while each row represents the instances in a predicted class. Note that the sum of elements of each column equals to 100.</p><p>Classification Q3 recall (%) of TorusDBN, PSIPRED, and our method under different priors on ASTRAL30 test dataset.</p

Marginal probability (MP) curves across positions for the phospholipase protein 3<i>rvc</i>[38].

<p>Shown at the top is the true secondary structure, TorusDBN’s prediction, PSIPREDs’ prediction, and the prediction from our method (MP-MSA).</p