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Abstract Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

The fairness & bias of narrow coverage of AI becomes another challenge for AI researchers. If a commercial AI trains with a biased dataset, there will be severe gender or racial fairness and bias issues. Since the researchers use primary language datasets to train AI, the broad audience cannot be satisfied if a novel LLM (Large Language Model) AI shows a knowledge or creativity limitation on their specific spoken language. Narrow coverage of the LLMs can lead the audience to misinterpretation and confusion if the service involves STT (Speech-To-Text). In this paper, to overcome this issue of data diversity, we propose the idea that the embedded, extracted features have captured semantic proximity information that can be useful to mitigate diversity issues. This project focused on the Korean language food dataset for STT services, where a narrow-trained A.I. is prone to show its limitations, such as lifestyle-related elements. To present our proof of concept, we trained a baseline model, GPT2, with the Korean Wikipedia dataset in 2022. Then, we employed DistilBERT and KoBERT for comparison. The extracted hidden_state_output features from each model were utilized to build feature-extraction-based text search engines. We used the same idea of Local Sensitive Hashing (LSH) but effectively located a similar hash by applying transposed weights. We also present conventional classification benchmarks for performance comparison using top-k measurements, times for training and memory & disc consumptions. In the discussion, we proposed that our idea can mitigate the diversity problem without re-training the model and tokenizer.

Glassy carbon (GC) microelectrodes have been successfully used for the detection of electroactive neurotransmitters such as dopamine and serotonin through voltammetry. However, non-electroactive neurotransmitters such as glutamate, lactate, and gamma-aminobutyric acid (GABA) are inherently unsuitable for detection through voltammetry techniques without functionalizing the surface of the microelectrodes. To this end, we present here the immobilization of the L-glutamate oxidase (GluOx) enzyme on the surface of GC microelectrodes to enable the catalysis of a chemical reaction between L-glutamate, oxygen, and water to produce H2O2, an electroactive byproduct that is readily detectable through voltammetry. This immobilization of GluOx on the surface of bare GC microelectrodes and the subsequent catalytic reduction in H2O2 through fast-scan cyclic voltammetry (FSCV) helped demonstrate the indirect in vitro detection of glutamate, a non-electroactive molecule, at concentrations as low as 10 nM. The functionalized microelectrodes formed part of a four-channel array of microelectrodes (30 μm × 60 μm) on a 1.6 cm long neural probe that was supported on a flexible polymer, with potential for in vivo applications. The types and strengths of the bond between the GC microelectrode surface and its functional groups, on one hand, and glutamate and the immobilized functionalization matrix, on the other hand, were investigated through molecular dynamic (MD) modeling and Fourier transform infrared spectroscopy (FTIR). Both MD modeling and FTIR demonstrated the presence of several covalent bonds in the form of C-O (carbon–oxygen polar covalent bond), C=O (carbonyl), C-H (alkenyl), N-H (hydrogen bond), C-N (carbon–nitrogen single bond), and C≡N (triple carbon–nitrogen bond). Further, penetration tests on an agarose hydrogel model confirmed that the probes are mechanically robust, with their penetrating forces being much lower than the fracture force of the probe material.

With the increasing use of unmanned aerial vehicles (UAVs) in various fields, achieving the precise localization of UAV images is crucial for enhancing their utility. Photogrammetry-based techniques, particularly bundle adjustment, serve as foundational methods for accurately determining the spatial coordinates of UAV images. The effectiveness of bundle adjustment is significantly influenced by the selection of input data, particularly the composition of overlapping image sets. The selection process of overlapping images significantly impacts both the accuracy of spatial coordinate determination and the computational efficiency of UAV image localization. Therefore, a strategic approach to this selection is crucial for optimizing the performance of bundle adjustment in UAV image processing. In this context, we propose an efficient methodology for determining overlapping image sets. The proposed method selects overlapping images based on image similarity, leveraging the complementary strengths of the bag of visual words and number of matches techniques. Essentially, our method achieves both high accuracy and high speed by utilizing a Bag of Visual Words for candidate selection and the number of matches for additional similarity assessment for overlapping image-set determination. We compared the performance of our proposed methodology with the conventional number of matches and bag-of-visual word-based methods for overlapping image-set determination. In the comparative evaluation, the proposed method demonstrated an average precision of 96%, comparable to that of the number of matches-based approach, while surpassing the 62% precision achieved by both bag-of-visual-word methods. Moreover, the processing time decreased by approximately 0.11 times compared with the number of matches-based methods, demonstrating relatively high efficiency. Furthermore, in the bundle adjustment results using image sets, the proposed method, along with the number of matches-based methods, showed reprojection error values of less than 1, indicating relatively high accuracy and contributing to the improvement in accuracy in estimating image positions.

Background: According to international pediatric urinary tract infection (UTI) guidelines, selecting ampicillin/ sulbactam or amoxicillin/clavulanate is recommended as the first-line treatment for pediatric UTI. In Korea, elevated resistance to ampicillin and ampicillin/sulbactam has resulted in the widespread use of third-generation cephalosporins for treating pediatric UTIs. This study aims to compare the efficacy of piperacillin-tazobactam (TZP) and cefotaxime (CTX) as first-line treatments in hospitalized children with UTIs. Materials and Methods: The study, conducted at Jeju National University Hospital, retrospectively analyzed medical records of children hospitalized for febrile UTIs between 2014 and 2017. UTI diagnosis included unexplained fever, abnormal urinalysis, and the presence of significant uropathogens. Treatment responses, recurrence, and antimicrobial susceptibility were assessed. Results: Out of 323 patients, 220 met the inclusion criteria. Demographics and clinical characteristics were similar between TZP and CTX groups. For children aged ≥3 months, no significant differences were found in treatment responses and recurrence. Extended-spectrum beta-lactamase (ESBL)-positive strains were associated with recurrence in those <3 months. Conclusion: In Korea, escalating resistance to empirical antibiotics has led to the adoption of broad-spectrum empirical treatment. TZP emerged as a viable alternative to CTX for hospitalized children aged ≥3 months with UTIs. Consideration of ESBL-positive strains and individualized approaches for those <3 months are crucial. [ABSTRACT FROM AUTHOR]