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We conducted a series of experiments in a flume with a cavity that provided refuge from the current to assess the feeding behavior of the stone moroko . Feeding behavior on commercial food pellets was quantified by assigning fish activity to one of the components of their predation cycle: pause, moving, pursuit (i.e., approach, chase, and attack), coasting, and resting within the cavity. Swimming speed was highest during attack, followed by that during chase, moving, approach, and coasting. As flow velocity increased, the approach and moving distances decreased while swimming speed increased. The body angle of a fish in pursuit relative to the flow generally increased in the latter stages of approach, chase, and attack and markedly decreased as flow increased. When flow velocity increased, the percentage of lateral captures decreased and fish rested in the cavity longer. Thus, the average energy cost of a feeding fish did not increase in higher flow velocities as much as was expected from estimations made at low flow velocity. The processes of approach, chase, and attack were defined as the unsteady swimming motions of linear or centripetal acceleration. Energy expenditure in the unsteady swimming mode was estimated using the theory of hydrodynamics. The main energy expenditure of the feeding cycle shifted from moving in slow flow to chase in fast flow because search time was shorter in fast flow. The net energy gain from feeding and swimming implied that the fish used in this experiment tended to express specific behaviors based on the trade-off between food uptake and energy expenditure up to a flow velocity of 7 cm/s. However, in faster flows, fish seemingly avoided a large drag force from the flowing water even if the net energy gain was positive. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is increasingly entering and transforming not only medical research but also clinical practice. In the last 10 years, new AI methods have enabled computers to perform visual tasks, reaching high performance and thereby potentially supporting and even outperforming human experts. This is in particular relevant for colorectal cancer (CRC), which is the 3rd most common cancer type in general, as along the CRC patient journey many complex visual tasks need to be performed: from endoscopy over imaging to histopathology; the screening, diagnosis, and treatment of CRC involve visual image analysis tasks., Summary: In all these clinical areas, AI models have shown promising results by supporting physicians, improving accuracy, and providing new biological insights and biomarkers. By predicting prognostic and predictive biomarkers from routine images/slides, AI models could lead to an improved patient stratification for precision oncology approaches in the near future. Moreover, it is conceivable that AI models, in particular together with innovative techniques such as single-cell or spatial profiling, could help identify novel clinically as well as biologically meaningful biomarkers that could pave the way to new therapeutic approaches., Key Messages: Here, we give a comprehensive overview of AI in colorectal cancer, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Background: Familial adenomatous polyposis (FAP) is a rare inherited syndrome that predisposes the patient to cancer. Treatment of FAP-related ampullary lesions is challenging and the role of endoscopic papillectomy has not been elucidated. We retrospectively analyzed the outcomes of endoscopic papillectomy in matched cohorts of FAP-related and sporadic ampullary lesions (SALs)., Methods: This retrospective multicenter study included 1422 endoscopic papillectomy procedures. Propensity score matching including age, sex, comorbidity, histologic subtype, and size was performed. Main outcomes were complete resection (R0), technical success, complications, and recurrence., Results: Propensity score matching identified 202 patients (101 FAP, 101 SAL) with comparable baseline characteristics. FAP patients were mainly asymptomatic (79.2 % [95 %CI 71.2-87.3] vs. 46.5 % [95 %CI 36.6-56.4]); P  < 0.001). The initial R0 rate was significantly lower in FAP patients (63.4 % [95 %CI 53.8-72.9] vs. 83.2 % [95 %CI 75.8-90.6]; P  = 0.001). After repeated interventions (mean 1.30 per patient), R0 was comparable (FAP 93.1 % [95 %CI 88.0-98.1] vs. SAL 97.0 % [95 %CI 93.7-100]; P  = 0.19). Adverse events occurred in 28.7 %. Pancreatitis and bleeding were the most common adverse events in both groups. Severe adverse events were rare (3.5 %). Overall, 21 FAP patients (20.8 % [95 %CI 12.7-28.8]) and 16 SAL patients (15.8 % [95 %CI 8.6-23.1]; P  = 0.36) had recurrence. Recurrences occurred later in FAP patients (25 [95 %CI 18.3-31.7] vs. 2 [95 %CI CI 0.06-3.9] months)., Conclusions: Endoscopic papillectomy was safe and effective in FAP-related ampullary lesions. Criteria for endoscopic resection of ampullary lesions can be extended to FAP patients. FAP patients have a lifetime risk of relapse even after complete resection, and require long-time surveillance., Competing Interests: M. Hollenbach has received honoraria for lectures and expert panel from FUJIFILM. E. Wedi and A. Schmidt has received honoraria for lectures and research support from Ovesco AG., (Thieme. All rights reserved.)

Obesity and diabetes are associated with increased mortality and morbidity. After failure of conservative therapy such as pharmacotherapy and lifestyle management, bariatric treatment is indicated for selected patients. Endoscopic techniques for weight reduction are well-established treatment for obese population who do not qualify for or are not willing to undergo bariatric surgery. This article is an overview of currently applied endoscopic techniques and summarized current published data., Competing Interests: Dr. Kien Vu Trung gibt an Honorare für FUJIFILM erhalten zu haben., (Thieme. All rights reserved.)

We propose a mobility-assisted on-demand routing algorithm for mobile ad hoc networks in the presence of location errors. Location awareness enables mobile nodes to predict their mobility and enhances routing performance by estimating link duration and selecting reliable routes. However, measured locations intrinsically include errors in measurement. Such errors degrade mobility prediction and have been ignored in previous work. To mitigate the impact of location errors on routing, we propose an on-demand routing algorithm taking into account location errors. To that end, we adopt the Kalman filter to estimate accurate locations and consider route confidence in discovering routes. Via simulations, we compare our algorithm and previous algorithms in various environments. Our proposed mobility prediction is robust to the location errors.