Lossless Compression of Cytometric Data.

Nowadays, most cytometrists apply lossless compression by storing their FCS files in ZIP archives. Unfortunately, ZIP only achieves modest space savings in cytometric data, due to DEFLATE being used as the underlying lossless compression algorithm (LCA). Presumably, other modern LCA can outperform DEFLATE, especially in terms of space savings. Twenty-one codecs (programs implementing LCA) were evaluated in 167,131 publicly available FCS files. Within floating-point data, as produced by modern instruments, most favorable compression ratios (CRs) were achieved by ZPAQ (median 0.469), BCM (median 0.523), and LZMA (median 0.545). In comparison, the DEFLATE-based codecs only achieved median CR of 0.728 under the most optimal conditions. By default, ZIP offers nine compression level (CL) settings, where lower ZIP-CL optimizes for time efficiency, while higher ZIP-CL optimizes for space efficiency. Interestingly, the third ZIP-CL already resulted in near optimal CR in 90% of the files with floating-point data, as produced by digital cytometers. LZMA is well established, widely supported, and actively maintained (in sharp contrast to ZPAQ and BCM) and therefore arguably the most attractive alternative for ZIP. Within floating-point data, by shifting from ZIP (under optimal conditions) to LZMA (at default settings), the median CR can be improved by 25%. Based on our results, cytometrists can benefit from state-of-the-art compression by choosing the appropriate codec for their situation. Our results are likely to speed-up the adaptation of modern codecs, as CR around 0.5 were beyond all expectations, and such space savings will benefit the field of cytometry. © 2019 International Society for Advancement of Cytometry.

Improving the performance of 3D image model compression based on optimized DEFLATE algorithm.

This study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm's exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.

A Quick and Easy Way to Estimate Entropy and Mutual Information for Neuroscience.

Calculations of entropy of a signal or mutual information between two variables are valuable analytical tools in the field of neuroscience. They can be applied to all types of data, capture non-linear interactions and are model independent. Yet the limited size and number of recordings one can collect in a series of experiments makes their calculation highly prone to sampling bias. Mathematical methods to overcome this so-called "sampling disaster" exist, but require significant expertise, great time and computational costs. As such, there is a need for a simple, unbiased and computationally efficient tool for estimating the level of entropy and mutual information. In this article, we propose that application of entropy-encoding compression algorithms widely used in text and image compression fulfill these requirements. By simply saving the signal in PNG picture format and measuring the size of the file on the hard drive, we can estimate entropy changes through different conditions. Furthermore, with some simple modifications of the PNG file, we can also estimate the evolution of mutual information between a stimulus and the observed responses through different conditions. We first demonstrate the applicability of this method using white-noise-like signals. Then, while this method can be used in all kind of experimental conditions, we provide examples of its application in patch-clamp recordings, detection of place cells and histological data. Although this method does not give an absolute value of entropy or mutual information, it is mathematically correct, and its simplicity and broad use make it a powerful tool for their estimation through experiments.

"Selective bronchus-blowing" method for effective inflate-deflate line identification in lung segmentectomy.

BACKGROUND: We examined two methods for the intraoperative selective bronchial delivery of air, and compared their effectiveness. METHODS: We recruited patients undergoing lung resection with the selective bronchus-blowing method in pulmonary segmentectomy for lung tumors. We assessed two types of bronchial ventilation methods: high-frequency jet ventilation (HFJV) and the bronchus-blowing method, which deliver air to target bronchi using HFJV or a 20G cannula inserted directly into the bronchi, respectively. The inflate-deflate line was classified as clear, slightly clear, and unclear. We examined the relationships between clinicopathological findings and the inflate-deflate line classification, as well as group differences in surgical-related factors. RESULTS: Among the 86 patients enrolled, 45 received HFJV ventilation and 41 received the bronchus-blowing method of ventilation. There was a significantly higher incidence of complex-type segmentectomies among patients in the bronchus-blowing group than in the HFJV group. The inflate-deflate line was classified as clear, slightly clear, and unclear in 16/7/11 and 25/3/3 patients in the HFJV and bronchus-blowing groups, respectively, according to the inflate-deflate criteria. The inflate-deflate line was identifiable in more cases in the bronchus-blowing group than in HFJV group (P=0.02). Complete resection was significantly less frequent in the HFJV group (73.5%) than in the bronchus-blowing group (90.3%). The rate of unsuccessful surgery was significantly higher among patients with severe emphysema, interstitial pneumonia, and anthracosis. CONCLUSIONS: Intraoperative selective bronchial air supply was attempted for the safe identification of target lung segments. The bronchus-blowing method easily enabled effective visualization of the segmental area within the operative field.