Preliminary analyses of cultured Symbiodinium isolated from sand in the oceanic Ogasawara Islands, Japan

Abstract The dinoflagellate genus Symbiodinium is generally found in many tropical and subtropical marine invertebrates. Recently, reports have focused on free-living types. We examined free-living Symbiodinium from the Ogasawara (Bonin) Islands, a group of oceanic islands south of Japan. Examining sand samples, seven of eight initial isolates were successfully cultured. Genetic analyses of 18S, 28S and internal transcribed spacer (ITS) ribosomal DNA regions reveal that one isolate cultured with only IMK was identical to clade A isolated from coral reef sand in Okinawa, and four additional isolates cultured with only IMK comprised a new clade A lineage. Additionally, two isolates cultured with IMK and soil extract were closely related to a little-known divergent lineage within clade D. Our results demonstrate some free-living Symbiodinium types may have very wide distributions, and that utilizing different culturing techniques will further discovery of unique Symbiodinium lineages from environmental samples

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly.Comment: Accepted in WACV 202

Two dinoflagellate taxa, Durinskia (Peridiniales, Dinophyceae) and Goniodoma (Gonyaulacales, Dinophyceae) from Okinawa, Japan

琉球大学21世紀COEプログラム「サンゴ礁島嶼系の生物多様性の総合解析」平成20年度成果発表会（平成21年3月14日開催）　講演・特別講演会場：理系複号棟102号室，ポスター発表会場：琉球大学50周年記念館1

Astaxanthin-Producing Green Microalga Haematococcus pluvialis: From Single Cell to High Value Commercial Products

Many species of microalgae have been used as source of nutrient rich food, feed and health promoting compounds. Among the commercially important microalgae, Haematococcus pluvialis is the richest source of natural astaxanthin which is considered as super anti-oxidant. Natural astaxanthin produced by H. pluvialis has significantly greater antioxidant capacity than the synthetic one. Astaxanthin has important applications in the nutraceuticals, cosmetics, food, and aquaculture industries. Thanks to many researches it is now evident, that astaxanthin can significantly reduce free radicals and oxidative stress and help human body maintain a healthy state. With extraordinary potency and increase in demand, astaxanthin is one of the high-value microalgal products of the future. Thus, this comprehensive review summarizes the most important aspects of the biology, biochemical composition, biosynthesis and astaxanthin accumulation in the cells of H. pluvialis and its wide range of applications for humans and animals. In this paper, important and recent developments ranging from cultivation, harvest and postharvest bio-processing technologies to metabolic control and genetic engineering are reviewed in detail, focusing on biomass and astaxanthin production from this biotechnologically important microalga. Simultaneously, critical bottlenecks and major challenges in commercial scale production; current and prospective global market of H. pluvialis derived astaxanthin are also presented in a critical manner. A new biorefinery concept for H. pluvialis has been also suggested to guide towards economically sustainable approach for microalgae cultivation and processing. This report could serve as a useful guide to present current status of knowledge in the field and highlight key areas for future development of H. pluvialis astaxanthin technology and its large scale commercial implementation

Seasonal variations of phytoplanktonic community structure and production in relation to environmental factors of the southwest coastal waters of Bangladesh

The present study was carried out to monitor the plankton community structure and productivity, its diurnal and seasonal variations and the influence of physico-chemical factors in the Shibsha River of the southwest coast of Bangladesh from July 2004 to June 2005. A total of 31 phytoplankton species were identified; 17 belong to Bacillariophyceae, seven to Cyanophyceae, five to Chlorophyceae and two to Dinophyceae. Bacillariophyceae appeared to be the dominant group in terms of total species and cell numbers during the period studied. The over all phytoplankton production was significantly (p< 0.05) higher in June (175.8x103 cells L-1) and lower in September (12.0x103 cells L-1) attributed to low temperature. Phytoplankton diversity declined to the lowest level in winter and there was a positive correlation with water temperature. The number of phytoplankton species was high (26) in June and quite low (11) in December. Nutrient concentrations including nitrate and phosphate were significantly (p< 0.05) higher in summer, while lower values recorded in winter. The lowest and highest concentration of Nitrate-Nitrogen (NO3-N) and Phosphate-Phosphorus (PO4-P) were 0.7, 0.3, 1.9 and 0.9 mg-1, respectively

Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer\u27s Disease: A Literature Review from a Machine Learning Perspective

There is a growing interest in the application of machine learning (ML) in Alzheimer\u27s disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS