Spontaneous Cell Detachment and Reattachment in Cancer Cell Lines: An In Vitro Model of Metastasis and Malignancy

There is an unmet need for simplified in vitro models of malignancy and metastasis that facilitate fast, affordable and scalable gene and compound analysis. “Adherent” cancer cell lines frequently release “free-floating” cells into suspension that are viable and can reattach. This, in a simplistic way, mimics the metastatic process. We compared the gene expression profiles of naturally co-existing populations of floating and adherent cells in SW620 (colon), C33a (cervix) and HeLa (cervix) cancer cells. We found that 1227, 1367 and 1333 genes were at least 2-fold differentially expressed in the respective cell lines, of which 122 were shared among the three cell lines. As proof of principle, we focused on the anti-metastatic gene NM23-H1, which was downregulated both at the RNA and protein level in the floating cell populations of all three cell lines. Knockdown of NM23-H1 significantly increased the number of floating (and viable) cells, whereas overexpression of NM23-H1 significantly reduced the proportion of floating cells. Other potential regulators of these cellular states were identified through pathway analysis, including hypoxia, mTOR (mechanistic target of rapamycin), cell adhesion and cell polarity signal transduction pathways. Hypoxia, a condition linked to malignancy and metastasis, reduced NM23-H1 expression and significantly increased the number of free-floating cells. Inhibition of mTOR or Rho-associated protein kinase (ROCK) significantly increased cell death specifically in the floating and not the adherent cell population. In conclusion, our study suggests that dynamic subpopulations of free-floating and adherent cells is a useful model to screen and identify genes, drugs and pathways that regulate the process of cancer metastasis, such as cell detachment and anoikis.     

Marine Phospholipids from Fishery By-Products Attenuate Atherosclerosis

In this study, the anti-atherosclerotic properties of three marine phospholipids (MPLs) extracts from fishery by-products including codfish roe, squid gonad, and shrimp head are verified. Their effects on key factors involved in atherosclerosis are examined and compared to explore whether the differences in their constitutions lead to the differences in the function. All three MPLs dampen oxidation of low- density lipoproteins (LDL) in vitro. Treating RAW264.7 macrophages and HUVECs endothelial cells with each MPLs ranging 10–100 µg mL⁻¹ does not decrease cell viability, yet ox-LDL caused cytotoxicity of both cells are alleviated by 50 or 100 µg mL⁻¹ MPLs treatment. In addition, the three MPLs reduce ox-LDL induced macrophage foam-like transition, mainly through inhibition of lipid uptake. Of the three MPLs, the one from squid gonad exhibits the best effect. On the other hand, all three MPLs modulate inflammatory responses, equally, by inhibiting the adhesion of monocytes to endothelial cells, and decreasing secretion of pro-inflammatory cytokines IL-6 and MCP-1. Using a high-cholesterol diet induced zebrafish model, it is found that all three MPLs, especially the one from squid gonad, alleviates cholesterol accumulation in early plaques, and decreases total cholesterol as well as lipid peroxide in vivo. Practical Applications: As a way of making the best of the increasingly scarce marine resources, valuable lipid components can be recovered from by-products and wastes from the fishery industry. Here, we tested the anti-atherosclerotic effects and the mechanisms of three MPLs extracted from codfish roe, squid gonad, and shrimp head. Our study provides further evidence that marine phospholipids extracted from fishery by-products could protect against atherosclerosis, and helps to elucidate the structure-function relationship of MPLs.     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

单增李斯特菌生态行为模型构建及机制研究进展

目的 探究食品中的单增李斯特菌随食品链相关条件的变化而发生的"生长-失活-再生长"生态行为规律.方法 以热加工为失活条件,综述单增李斯特菌生态行为的影响因素、模型构建和相关机制.结果 单增李斯特菌在"生长-失活"过程中,细胞的历史生长条件会对其失活特性产生影响,在"失活-再生长"过程中,细胞的历史生理状态会影响其修复再生长的延滞期.结论 食品链中细菌的生长和失活是连续的过程,有必要研究在连续的环境条件变化过程中细胞的生态行为,以期为食品风险评估和食品安全防控提供支持.     

Historical and contemporary movement and survival rates of walleye (Sander vitreus) in Lake Winnipeg,Canada

Understanding patterns of fish movement in large lake ecosystems is essential for determining appropriate management actions as differences in movement behaviour can influence life history traits such as growth and survival. Lake Winnipeg in Manitoba, Canada supports the 2nd largest walleye (Sander vitreus) commercial fishery in North America. We used mark-recapture models to determine movement and estimate survival of walleye between basins of Lake Winnipeg in historical and contemporary contexts, comparing a tag-recovery study completed historically during 1974–1977 with a contemporary (2017–2019) acoustic telemetry study. Mark-recapture models revealed comparably low but detectable annual transitions between basins from historical (0.3–1.2%) and contemporary datasets (7–8.5%). Historically, fish > 300 mm more frequently moved in a south to north direction. Contemporary estimates suggest similar length-based directionality in that fish > 350 mm were always more likely to move in a south-north direction. Contemporary annual survival derived from mark-recapture models ranged between 27 and 45% and 64.3% when derived from catch curve analysis, while independently derived annual historical survival estimates ranged between 50 and 69% and 45.5% from catch curve analysis. Using the contemporary dataset, we also observed seasonal variation in movement and survival between basins, with the greatest movement across the lake occurring during the fall. Our results demonstrate a persisting pattern of low but measurable movement, suggesting between basin movement is not unusual for Lake Winnipeg. Further, low walleye survival rates reported here for the two time periods studied, support recent management actions to reduce fishing pressure across the lake.     

How Mixing and Light Heterogeneity Impact the Overall Growth Rate in Photobioreactors

The microalgae growth rate in photobioreactors responds with inertia to light stimuli. Here, light variations experienced by the algae are accessed through a coupling of an irradiance field calculation and a Lagrangian particle tracking. The response of algae to fluctuating light is then described by a relaxation model involving a single time constant, the value of which is identified from published data. The overall growth rate is calculated as the sum of individual growth rates of all particles. Instantaneous adaptation and full integration asymptotic behaviors are recovered whilst a finite time constant reveals that the overall growth rate is dependent both on mixing and light distribution. This methodology thus quantitatively relates the design parameters to the photobioreactor performance.     

Advances in the in vitro digestion and fermentation of polysaccharides

In vitro digestion models are widely used to study the structural changes, digestion and release of food components under simulated gastrointestinal conditions. As compared to the in vivo digestion tests, the in vitro digestion reflects the digestion and utilisation of food after ingestion and has the advantages of being time consuming, inexpensive, reproducible and free from moral and ethical restrictions. This study reviewed the current research studies on the in vitro simulated digestion of polysaccharides conducted in the last 5 years and focused on the oral, gastric and intestinal digestion models, with the aim of providing a basis for the further testing of changes in the content, structure and active ingredients of polysaccharides before and after digestion.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed-integer nonlinear optimization

Large-scale strongly nonlinear and nonconvex mixed-integer nonlinear programming (MINLP) models frequently appear in optimization-based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to much lower computational effort required for the same locally optimal solution.