Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening

Unsupervised learning of pixel clustering in Mueller matrix images for mapping microstructural features in pathological tissues

In histopathology, doctors identify diseases by characterizing abnormal cells and their spatial organization within tissues. Polarization microscopy and supervised learning have been proved as an effective tool for extracting polarization parameters to highlight pathological features. Here, we present an alternative approach based on unsupervised learning to group polarization-pixels into clusters, which correspond to distinct pathological structures. For pathological samples from different patients, it is confirmed that such unsupervised learning technique can decompose the histological structures into a stable basis of characteristic microstructural clusters, some of which correspond to distinctive pathological features for clinical diagnosis. Using hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) samples, we demonstrate how the proposed framework can be utilized for segmentation of histological image, visualization of microstructure composition associated with lesion, and identification of polarization-based microstructure markers that correlates with specific pathology variation. This technique is capable of unraveling invisible microstructures in non-polarization images, and turn them into visible polarization features to pathologists and researchers

Mobile payment

This article briefly introduces a service method that allows user to use their mobile terminal (usually a mobile phone) to pay for the goods or services they consume – Mobile Payment

New insights into effect of alkaline cleaning on fouling behavior of polyamide nanofiltration membrane for wastewater treatment

Membrane fouling is an intractable issue in wastewater treatment by nanofiltration (NF) membrane, and alkaline cleaning is the most effective approach to remove organic fouling on NF membrane. However, it was found that pore swelling of NF membrane induced by alkaline cleaning might reduce cleaning efficiency, and it is never quantified and its effect on membrane fouling behavior is still mysterious. In this work, membrane pore swelling effect (similar to 9.7%, increment of effective pore size) induced by alkaline cleaning (pH 11) is confirmed and its effect on fouling behavior of the polyamide NF membrane is investigated based on experimental and modelling results. It is found that the alkali-induced pore swelling phenomenon would disappear after water filtration at neutral pH for 30 min, and if such cleaned membrane is faced by the small foulants during this pore shrinkage period, the concentration polarization and membrane fouling would be severer, and the subsequent alkaline cleaning is less effective because more foulants enter the enlarged pores and are tightly embedded in the membrane. Thus, the irreversible fouling of the NF membrane increases from 20% to 40% while its permeability recovery declines from 100% to 67% after six fouling/cleaning cycles. When an anionic surfactant sodium dodecyl sulfate (SDS, 10 mM) is added in the alkaline cleaning solution, the adsorption of SDS in/on the membrane can not only improve its hydrophilicity and negative charge, but also quickly eliminate the alkali-induced pore swelling effect and avoid the accumulation of foulants in the pores, thereby enhancing the antifouling performance of the NF membrane. Using the alkaline SDS cleaning, the irreversible fouling of the NF membrane maintains below 10% while its permeability recovery keeps above 100% in six continuous fouling/cleaning cycles. (C) 2021 Elsevier B.V. All rights reserved

How Do Chemical Cleaning Agents Act on Polyamide Nanofiltration Membrane and Fouling Layer?

Nanofiltration (NF) membranes, especially polyamide (PA) ones, are widely applied in water treatment, food processing, and resource recovery thanks to their excellent separation selectivity to small solutes and high water permeability. However, membrane fouling is inevitable during the long-term operation and limits the large-scale applications of NF technology. Chemical cleaning is considered the most effective way to avoid fouling aggravation and quickly restore membrane permeability on site. However, frequent chemical cleaning would cause reversible/ irreversible changes in the physical and chemical properties of NF membranes, resulting in membrane damage and deterioration of separation performance. Moreover, the PA NF membrane with high selectivity and a relatively low cross-linking degree is more sensitive to chemical cleaning. Many efforts, therefore, have been dedicated to clarifying the effect of chemical cleaning on the NF membrane properties and its separation performance. This review summarizes recent developments in the field and epitomizes the synergistic/inhibitory effect among the chemical cleaning agents on the fouling removal. Besides the practical guides for chemical cleaning of the PA NF membrane, many opportunities to advance the field are also pointed out

Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues

Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis

Modeling of a Rotary Adsorber for Continuous Capture of Indoor Carbon Dioxide

Removing indoor CO2 as a pollutant via solid sorbents is a promising solution to maintaining acceptable indoor air quality while minimizing the energy consumption of ventilation. Compared to fixed-bed and fluidized-bed configurations, which require at least two beds to allow for continuous operation, a rotary adsorber is more compact and suitable to be integrated into the ventilation systems of buildings. In the present study, a regenerative rotary adsorber based on temperature swing adsorption was modeled to investigate continuous CO2 capture in an indoor environment. The governing equations of heat and mass transfer processes associated with the capture were established and coded in ANSYS Fluent software. The spatiotemporal variations of CO2 concentration and temperature in gas and solid phases within the rotary adsorber were obtained. The key findings are: (1) adjusting the speed mainly affects circumferential concentration and temperature distribution, but has little impact on axial concentration and temperature; (2) Increasing desorption inlet flow rate has little impact on adsorption outlet concentration, but significantly decreases desorption outlet concentration; (3) Raising desorption inlet temperature can increase both adsorption and desorption outlet average concentrations; (4) Reducing the volume proportion of the desorption sector will slightly increase adsorption outlet concentration and slightly decrease desorption outlet concentration, but barely affects average adsorption and desorption outlet temperatures

No Evidence to Support a Causal Relationship between Circulating Adiponectin Levels and Ankylosing Spondylitis: A Bidirectional Two-Sample Mendelian Randomization Study

Based on previous observational studies, the causal association between circulating adiponectin (CA) levels and ankylosing spondylitis (AS) risk remains unclear. Therefore, this study aims to investigate whether CA levels are related to the risk of AS. We carried out a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal correlation between CA levels and AS via published genome-wide association study (GWAS) datasets. Single-nucleotide polymorphisms (SNPs) related to CA levels were derived from a large GWAS that included 39,883 individuals of European descent. SNPs related to AS were obtained from the FinnGen consortium (2252 cases and 227,338 controls). The random-effects inverse variance weighted (IVW) method was the primary method utilized in our research. We also used four complementary approaches to improve the dependability of this study (MR–Egger regression, Weighted median, Weighted mode, and Simple mode). Random-effects IVW (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.79–1.27, p = 0.984) and four complementary methods all indicated that genetically predicted CA levels were not causally related to the risk of AS. In reverse MR analysis, there is little evidence to support the genetic causality between the risk of AS and CA levels