Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time

The South African Bone Marrow Registry (SABMR) and allogeneic bone marrow transplantation

In 1939 Osgood et al. reported infusing bone marrow into patients with severe aplastic anaemia without any clinical benefit. Rekers and Coulter attempted to reconstitute marrow function in irradiated dogs by marrow infusions. Both failed because of insufficient irradiation to produce immunosuppression necessary for engraftment. In 1957 Donnall Thomas and co-workers showed that marrow can be collected, stored in significant quantities and safely administered. Marrow transplants remained unsuccessful, however, although patients with refractory leukaemia given supralethal irradiation recovered after marrow infusion from identical twins. Allogeneic marrow transplants usually resulted in failed engraftment, or engraftment followed by lethal graft-versus-host disease (GVHD). Further discoveries were the HLA complex and transplantation antigens, and that successful allogeneic engrafting depends upon donor/recipient histocompatibility

Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?

Recent breakthroughs in pre-trained code models, such as CodeBERT and Codex, have shown their superior performance in various downstream tasks. The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically. Recent studies reveal that even state-of-the-art pre-trained code models struggle with suggesting the correct APIs during code generation. However, the reasons for such poor API usage performance are barely investigated. To address this challenge, we propose using knowledge probing as a means of interpreting code models, which uses cloze-style tests to measure the knowledge stored in models. Our comprehensive study examines a code model's capability of understanding API fully qualified names from two different perspectives: API call and API import. Specifically, we reveal that current code models struggle with understanding API names, with pre-training strategies significantly affecting the quality of API name learning. We demonstrate that natural language context can assist code models in locating Python API names and generalize Python API name knowledge to unseen data. Our findings provide insights into the limitations and capabilities of current pre-trained code models, and suggest that incorporating API structure into the pre-training process can improve automated API usage and code representations. This work provides significance for advancing code intelligence practices and direction for future studies. All experiment results, data and source code used in this work are available at \url{https://doi.org/10.5281/zenodo.7902072}

HARDWARE DESIGNING OF A SMART ROBOTIC PET

poster abstractHuman have dreamed to have robot since ancient time. Aristotle wrote of the idea in 322 BCE as a perfect measure to bring equality to civilization by removing the need for labor. Modern robots have permeated the very way of life in all aspects of human activity, particularly after the creation of the microprocessor. In this poster, the process of developing the hardware of an autonomously movable robotic pet will be introduced. Four steps were followed when designing the hardware for robotic pet – 1) identify the need, 2) research the need, 3) develop possible solution and select best solution, 4) test and evaluate the solution, and 4) optimize the design. In particular, three major factors need to be taken in consideration—structure of the base, driving system, and power required. To ensure an efficient and economic design, all possible solutions for driving system and structure are compared by matrix chart. The power and torque needed is calculated based on the weight and speed of the robotic pet. After identify the driving system and chassis, CAD software is used to sketch the blueprint for the hardware. In addition, we need to do face recognition using the camera mounted on the robot. However, the motion of the robot may severely degrade the image and signal quality. To mitigate noise effect, special effort is made such as using special type of wheel to decrease shock of the robot

Endoscopic Vision Augmentation Using Multiscale Bilateral-Weighted Retinex for Robotic Surgery

医疗机器人手术视觉是微创外科手术成功与否的关键所在。由于手术器械医学电子内镜自身内在的局限性，导致了手术视野不清晰、光照不均、多烟雾等诸多问题，使得外科医生无法准确快速感知与识别人体内部器官中的神经血管以及病灶位置等结构信息，这无疑增加了手术风险和手术时间。针对这些手术视觉问题，本论文提出了一种基于双边滤波权重分析的多尺度Retinex模型方法，对达芬奇医疗机器人手术过程中所采集到的病患视频进行处理与分析。经过外科医生对实验结果的主观评价，一致认为该方法能够大幅度地增强手术视野质量；同时客观评价实验结果表明本论文所提出方法优于目前计算机视觉领域内的图像增强与恢复方法。 厦门大学信息科学与技术学院计算机科学系罗雄彪教授为本文第一作者。【Abstract】Endoscopic vision plays a significant role in minimally invasive surgical procedures. The visibility and maintenance of such direct in-situ vision is paramount not only for safety by preventing inadvertent injury, but also to improve precision and reduce operating time. Unfortunately, endoscopic vision is unavoidably degraded due to illumination variations during surgery. This work aims to restore or augment such degraded visualization and quantitatively evaluate it during robotic surgery. A multiscale bilateral-weighted retinex method is proposed to remove non-uniform and highly directional illumination and enhance surgical vision, while an objective noreference image visibility assessment method is defined in terms of sharpness, naturalness, and contrast, to quantitatively and objectively evaluate endoscopic visualization on surgical video sequences. The methods were validated on surgical data, with the experimental results showing that our method outperforms existent retinex approaches. In particular, the combined visibility was improved from 0.81 to 1.06, while three surgeons generally agreed that the results were restored with much better visibility.The authors thank the assistance of Dr. Stephen Pautler for facilitating the data acquisition, Dr. A. Jonathan McLeod and Dr.Uditha Jayarathne for helpful discussions

Arx is required for normal enteroendocrine cell development in mice and humans

AbstractEnteroendocrine cells of the gastrointestinal (GI) tract play a central role in metabolism, digestion, satiety and lipid absorption, yet their development remains poorly understood. Here we show that Arx, a homeodomain-containing transcription factor, is required for the normal development of mouse and human enteroendocrine cells. Arx expression is detected in a subset of Neurogenin3 (Ngn3)-positive endocrine progenitors and is also found in a subset of hormone-producing cells. In mice, removal of Arx from the developing endoderm results in a decrease of enteroendocrine cell types including gastrin-, glucagon/GLP-1-, CCK-, secretin-producing cell populations and an increase of somatostatin-expressing cells. This phenotype is also observed in mice with endocrine-progenitor-specific Arx ablation suggesting that Arx is required in the progenitor for enteroendocrine cell development. In addition, depletion of human ARX in developing human intestinal tissue results in a profound deficit in expression of the enteroendocrine cell markers CCK, secretin and glucagon while expression of a pan-intestinal epithelial marker, CDX2, and other non-endocrine markers remained unchanged. Taken together, our findings uncover a novel and conserved role of Arx in mammalian endocrine cell development and provide a potential cause for the chronic diarrhea seen in both humans and mice carrying Arx mutations

Anti-nausea effects and pharmacokinetics of ondansetron, maropitant and metoclopramide in a low-dose cisplatin model of nausea and vomiting in the dog: a blinded crossover study

Nausea is a subjective sensation which is difficult to measure in non-verbal species. The aims of this study were to determine the efficacy of three classes of antiemetic drugs in a novel low dose cisplatin model of nausea and vomiting and measure change in potential nausea biomarkers arginine vasopressin (AVP) and cortisol. A four period cross-over blinded study was conducted in eight healthy beagle dogs of both genders. Dogs were administered 18 mg/m2 cisplatin intravenously, followed 45 min later by a 15 min infusion of either placebo (saline) or antiemetic treatment with ondansetron (0.5 mg/kg; 5-HT3 antagonist), maropitant (1 mg/kg; NK1 antagonist) or metoclopramide (0.5 mg/kg; D2 antagonist). The number of vomits and nausea associated behaviours, scored on a visual analogue scale, were recorded every 15 min for 8 h following cisplatin administration. Plasma samples were collected to measure AVP, cortisol and antiemetic drug concentrations

Improving Evidence-Based Grouping of Transitional Care Strategies in Hospital Implementation Using Statistical Tools and Expert Review

BACKGROUND: As health systems transition to value-based care, improving transitional care (TC) remains a priority. Hospitals implementing evidence-based TC models often adapt them to local contexts. However, limited research has evaluated which groups of TC strategies, or transitional care activities, commonly implemented by hospitals correspond with improved patient outcomes. In order to identify TC strategy groups for evaluation, we applied a data-driven approach informed by literature review and expert opinion. METHODS: Based on a review of evidence-based TC models and the literature, focus groups with patients and family caregivers identifying what matters most to them during care transitions, and expert review, the Project ACHIEVE team identified 22 TC strategies to evaluate. Patient exposure to TC strategies was measured through a hospital survey (N = 42) and prospective survey of patients discharged from those hospitals (N = 8080). To define groups of TC strategies for evaluation, we performed a multistep process including: using ACHIEVE\u27S prior retrospective analysis; performing exploratory factor analysis, latent class analysis, and finite mixture model analysis on hospital and patient survey data; and confirming results through expert review. Machine learning (e.g., random forest) was performed using patient claims data to explore the predictive influence of individual strategies, strategy groups, and key covariates on 30-day hospital readmissions. RESULTS: The methodological approach identified five groups of TC strategies that were commonly delivered as a bundle by hospitals: 1) Patient Communication and Care Management, 2) Hospital-Based Trust, Plain Language, and Coordination, 3) Home-Based Trust, Plain language, and Coordination, 4) Patient/Family Caregiver Assessment and Information Exchange Among Providers, and 5) Assessment and Teach Back. Each TC strategy group comprises three to six, non-mutually exclusive TC strategies (i.e., some strategies are in multiple TC strategy groups). Results from random forest analyses revealed that TC strategies patients reported receiving were more important in predicting readmissions than TC strategies that hospitals reported delivering, and that other key co-variates, such as patient comorbidities, were the most important variables. CONCLUSION: Sophisticated statistical tools can help identify underlying patterns of hospitals\u27 TC efforts. Using such tools, this study identified five groups of TC strategies that have potential to improve patient outcomes