Cluster analysis of flow cytometric list mode data on a personal computer

A cluster analysis algorithm, dedicated to analysis of flow cytometric data is described. The algorithm is written in Pascal and implemented on an MS-DOS personal computer. It uses k-means, initialized with a large number of seed points, followed by a modified nearest neighbor technique to reduce the large number of subclusters. Thus we combine the advantage of the k-means (speed) with that of the nearest neighbor technique (accuracy). In order to achieve a rapid analysis, no complex data transformations such as principal components analysis were used. \ud Results of the cluster analysis on both real and artificial flow cytometric data are presented and discussed. The results show that it is possible to get very good cluster analysis partitions, which compare favorably with manually gated analysis in both time and in reliability, using a personal computer

A new principle of cell sorting by using selective electroporation in a modified flow cytometer

When a strong electric field pulse of a few microseconds is applied to biological cells, small pores are formed in the cell membranes; this process is called electroporation. At high field strengths and/or long pulse durations the membranes will be damaged permanently. This eventually leads to cell kill. \ud We have developed a modified flow cytometer in which one can electroporate individual cells selected by optical analysis. The first experiments with this flow cytometer were designed to use it as a damaging sorter; we used electric pulses of 10 s and resulting field strengths of 2.0 and 3.2 X 106 V/m to kill K562 cells and lymphocytes respectively. The hydrodynamically focused cells are first optically analyzed in the usual way in a square flow channel. At the end of this channel the cells are forced to flow through a small Coulter orifice, into a wider region. If optical analysis indicates that a cell is unwanted, the cell is killed by applying a strong electric field across the Coulter orifice. The wanted living cells can be subsequently separated from the dead cells and cell fragments by a method suitable for the particular application (e.g., centrifugation, cell growth, density gradient, etc.). \ud The results of these first experiments demonstrate that by using very simple equipment, sorting by selective killing with electric fields is possible at rates of 1,000 cells/s with a purity of the sorted fraction of 99.9%

A flow cytometric study of the membrane potential of natural killer and k562 cells during the cytotoxic process

This study demonstrates that it is possible to investigate the membrane potential of interacting cells during the cytotoxic process using flow cytometry. Changes in the membrane potential of NK and K562 cells, involved in a cell-mediated cytotoxic process, were studied by standard and slit-scan flow cytometry, using the membrane potential sensitive fluorescent probe DiBAC4(3). The NK cells were labeled with a membrane marker (TR-18 or DiI) prior to incubation with K562 cells and the conjugates that were formed could be identified on the basis of the membrane marker fluorescence and light scattering signals. With a slit-scan technique we measured the membrane potential of each cell in a conjugate separately. The results show that depolarization of the K562 cell occurs as a consequence of the cytotoxic activity of the NK cell. This depolarization appears to be an early sign of cell damage because the cell membrane still remains impermeable to propidium iodide. Our data also indicate that depolarization of the NK cell occurs as a result of its cytotoxic activity

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011–2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49–0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors

Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging the hidden knowledge encoded within these highly performant AI systems. Yet, this knowledge is often hard to extract, and may be hard to understand or learn from. Here, we show that this is possible by proposing a new method that allows us to extract new chess concepts in AlphaZero, an AI system that mastered the game of chess via self-play without human supervision. Our analysis indicates that AlphaZero may encode knowledge that extends beyond the existing human knowledge, but knowledge that is ultimately not beyond human grasp, and can be successfully learned from. In a human study, we show that these concepts are learnable by top human experts, as four top chess grandmasters show improvements in solving the presented concept prototype positions. This marks an important first milestone in advancing the frontier of human knowledge by leveraging AI; a development that could bear profound implications and help us shape how we interact with AI systems across many AI applications.Comment: 61 pages, 29 figure

Diversifying AI: Towards Creative Chess with AlphaZero

In recent years, Artificial Intelligence (AI) systems have surpassed human intelligence in a variety of computational tasks. However, AI systems, like humans, make mistakes, have blind spots, hallucinate, and struggle to generalize to new situations. This work explores whether AI can benefit from creative decision-making mechanisms when pushed to the limits of its computational rationality. In particular, we investigate whether a team of diverse AI systems can outperform a single AI in challenging tasks by generating more ideas as a group and then selecting the best ones. We study this question in the game of chess, the so-called drosophila of AI. We build on AlphaZero (AZ) and extend it to represent a league of agents via a latent-conditioned architecture, which we call AZ_db. We train AZ_db to generate a wider range of ideas using behavioral diversity techniques and select the most promising ones with sub-additive planning. Our experiments suggest that AZ_db plays chess in diverse ways, solves more puzzles as a group and outperforms a more homogeneous team. Notably, AZ_db solves twice as many challenging puzzles as AZ, including the challenging Penrose positions. When playing chess from different openings, we notice that players in AZ_db specialize in different openings, and that selecting a player for each opening using sub-additive planning results in a 50 Elo improvement over AZ. Our findings suggest that diversity bonuses emerge in teams of AI agents, just as they do in teams of humans and that diversity is a valuable asset in solving computationally hard problems

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

Background Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins. Objective To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue. Materials and methods Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients. Results A PCA-LDA tissue classification model ‘tumor’ versus ‘non-tumorous tissue’ (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model ‘tumor’ versus ‘non-tumorous tissue’ showed an accuracy of 91%

Raman spectroscopy to discriminate laryngeal squamous cell carcinoma from non-cancerous surrounding tissue

As for many solid cancers, laryngeal cancer is treated surgically, and adequate resection margins are critical for survival. Raman spectroscopy has the capacity to accurately differentiate between cancer and non-cancerous tissue based on their molecular composition, which has been proven in previous work. The aim of this study is to investigate whether Raman spectroscopy can be used to discriminate laryngeal cancer from surrounding non-cancerous tissue. Patients surgically treated for laryngeal cancer were included. Raman mapping experiments were performed ex vivo on resection specimens and correlated to histopathology. Water concentration analysis and CH-stretching region analysis were performed in the high wavenumber range of 2500–4000 cm−1. Thirty-four mapping experiments on 22 resection specimens were used for analysis. Both laryngeal cancer and all non-cancerous tissue structures showed high water concentrations of around 75%. Discriminative information was only found to be present in the CH-stretching region of the Raman spectra of the larynx (discriminative power of 0.87). High wavenumber region Raman spectroscopy can discriminate laryngeal cancer from non-cancerous tissue structures. Contrary to the findings for oral cavity cancer, water concentration is not a discriminating factor for laryngeal cancer.</p