A Simple Apparatus for Measuring Cell Settling Velocity

Accurate cell settling velocity determination is critical for perfusion culture using a gravity settler for cell retention. We have developed a simple apparatus (a \u27settling column\u27) for measuring settling velocity and have validated the procedure with 15-μm polystyrene particles with known physical properties. The measured settling velocity of the polystyrene particles is within 4% of the value obtained using the traditional Stokes\u27 law approach. The settling velocities of three hybridoma cell lines were measured, resulting in up to twofold variation among cell lines, and the values decreased as the cell culture aged. The settling velocities of the nonviable cells were 33-50% less than the corresponding viable cells. The significant variation of settling velocities among cell populations and growth phases confirms the necessity of routine measurement of this property during long-term perfusion culture

A Euclidean Distance Matrix Model for Convex Clustering

Clustering has been one of the most basic and essential problems in unsupervised learning due to various applications in many critical fields. The recently proposed sum-of-nums (SON) model by Pelckmans et al. (2005), Lindsten et al. (2011) and Hocking et al. (2011) has received a lot of attention. The advantage of the SON model is the theoretical guarantee in terms of perfect recovery, established by Sun et al. (2018). It also provides great opportunities for designing efficient algorithms for solving the SON model. The semismooth Newton based augmented Lagrangian method by Sun et al. (2018) has demonstrated its superior performance over the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA). In this paper, we propose a Euclidean distance matrix model based on the SON model. An efficient majorization penalty algorithm is proposed to solve the resulting model. Extensive numerical experiments are conducted to demonstrate the efficiency of the proposed model and the majorization penalty algorithm.Comment: 32 pages, 3 figures, 3 table

Global dynamics of a class of HIV-1 infection models with latently infected cells

In this paper, the global dynamics of a class of HIV-1 infection models with different infection rates and latently infected cells are investigated. We first modify the basic virus infection model and propose two models with bilinear infection rate and saturation infection rate, respectively, which take HIV-1 latency into consideration, and then study a model with a general nonlinear infection rate. By using proper Lyapunov functions and LaSalle's invariance principle, it is proved that in the first two models, if the basic reproduction ratio is less than unity, each of the infection-free equilibria is globally asymptotically stable; if the basic reproduction ratio is greater than unity, each of the chronic-infection equilibria is globally asymptotically stable. For the last model with general nonlinear infection rate, we obtain sufficient conditions for the global stability of both the infection-free and chronic-infection equilibria of the model

KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection

Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities. However, their potential to generate misinformation, often called the hallucination problem, poses a significant risk to their deployment. A common approach to address this issue is to retrieve relevant knowledge and fine-tune the LLM with the knowledge in its input. Unfortunately, this method incurs high training costs and may cause catastrophic forgetting for multi-tasking models. To overcome these limitations, we propose a knowledge-constrained decoding method called KCTS (Knowledge-Constrained Tree Search), which guides a frozen LM to generate text aligned with the reference knowledge at each decoding step using a knowledge classifier score and MCTS (Monte-Carlo Tree Search). To adapt the sequence-level knowledge classifier to token-level guidance, we also propose a novel token-level hallucination detection method called RIPA (Reward Inflection Point Approximation). Our empirical results on knowledge-grounded dialogue and abstractive summarization demonstrate the strength of KCTS as a plug-and-play, model-agnostic decoding method that can effectively reduce hallucinations in natural language generation.Comment: Accepted at EMNLP 2023 Main Conferenc

Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly. As a result, various automatic methods have been proposed to construct CSKG with larger semantic coverage. However, these unsupervised approaches introduce spurious noise that can lower the quality of the resulting CSKG, which cannot be tackled easily by existing denoising algorithms due to the unique characteristics of nodes and structures in CSKGs. To address this issue, we propose Gold (Global and Local-aware Denoising), a denoising framework for CSKGs that incorporates entity semantic information, global rules, and local structural information from the CSKG. Experiment results demonstrate that Gold outperforms all baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks. Furthermore, we show that denoising a real-world CSKG is effective and even benefits the downstream zero-shot commonsense question-answering task.Comment: Accepted to EMNLP findings 202

Tumor elimination by clustered microRNAs miR-306 and miR-79 via noncanonical activation of JNK signaling

JNK signaling plays a critical role in both tumor promotion and tumor suppression. Here, we identified clustered microRNAs (miRNAs) miR-306 and miR-79 as novel tumor-suppressor miRNAs that specifically eliminate JNK-activated tumors in Drosophila. While showing only a slight effect on normal tissue growth, miR-306 and miR-79 strongly suppressed growth of multiple tumor models, including malignant tumors caused by Ras activation and cell polarity defects. Mechanistically, these miRNAs commonly target the mRNA of an E3 ubiquitin ligase ring finger protein 146 (RNF146). We found that RNF146 promotes degradation of tankyrase (Tnks), an ADP-ribose polymerase that promotes JNK activation in a noncanonical manner. Thus, downregulation of RNF146 by miR-306 and miR-79 leads to hyper-enhancement of JNK activation. Our data show that, while JNK activity is essential for tumor growth, elevation of miR-306 or miR-79 overactivate JNK signaling to the lethal level via noncanonical JNK pathway and thus eliminate tumors, providing a new miRNA-based strategy against cancer

Evaluation of an Inclined Gravity Settler for Microalgae Harvesting

BACKGROUND Given the small size of microalgae, and the low biomass concentrations obtained in light-limited cultures, the cost of algae harvesting is a significant barrier to commercial-scale production of biofuel from algae. A downward- flow inclined gravity settler was evaluated for its effectiveness in dewatering the microalgae Scenedesmus dimorphus and Chlorella vulgaris. RESULTS Experimental results showed that S. dimorphus can be concentrated up to 8-fold using a single settler stage, with a biomass recovery of 80%. Separation efficiency was independent of biomass concentration between 1 and 5 gdw L-1, suggesting that a two-stage sequential system of settlers may maximize biomass recovery and concentration effectively. Efficiency of separation of C. vulgaris was slightly lower than that of S. dimorphus, most likely due to the fact that S. dimorphus exist in aggregates of four or more cells and thus may settle more easily. CONCLUSION The downward-flow inclined gravity settler demonstrated consistent results with 72% efficiency in biomass recovery and low operating costs. This separation system warrants further investigation at the industrial scale, for the harvesting of algae from dilute cell suspensions, with applications to biofuels. (c) 2013 Society of Chemical Industr