Eganboye7269

Protein detection plays an important role in biological and biomedical sciences. The immunoassay based on fluorescence labeling has good specificity but a high labeling cost. Herein, on the basis of G-triplex molecular beacon (G3MB) and thioflavin T (ThT), we developed a simple and label-free biosensor for protein detection. The biotin and streptavidin were used as model enzymes. In the presence of target streptavidin (SA), the streptavidin hybridized with G3MB-b (biotin-linked-G-triplex molecular beacon) perfectly and formed larger steric hindrance, which hindered the hydrolysis of probes by exonuclease III (Exo III). In the absence of target streptavidin, the exonuclease III successively cleaved the stem of G3MB-b and released the G-rich sequences which self-assembled into a G-triplex and subsequently activated the fluorescence signal of thioflavin T. Compared with the traditional G-quadruplex molecular beacon (G4MB), the G3MB only needed a lower dosage of exonuclease III and a shorter reaction time to reach the optimal detection performance, because the concise sequence of G-triplex was good for the molecular beacon design. Moreover, fluorescence experiment results exhibited that the G3MB-b had good sensitivity and specificity for streptavidin detection. The developed label-free biosensor provides a valuable and general platform for protein detection.A parallel fish school tracking based on multiple-feature fish detection has been proposed in this paper to obtain accurate movement trajectories of a large number of zebrafish. Zebrafish are widely adapted in many fields as an excellent model organism. Due to the non-rigid body, similar appearance, rapid transition, and frequent occlusions, vision-based behavioral monitoring is still a challenge. A multiple appearance feature based fish detection scheme was developed by examining the fish head and center of the fish body based on shape index features. The proposed fish detection has the advantage of locating individual fishes from occlusions and estimating their motion states, which could ensure the stability of tracking multiple fishes. Moreover, a parallel tracking scheme was developed based on the SORT framework by fusing multiple features of individual fish and motion states. The proposed method was evaluated in seven video clips taken under different conditions. These videos contained various scales of fishes, different arena sizes, different frame rates, and various image resolutions. The maximal number of tracking targets reached 100 individuals. The correct tracking ratio was 98.60% to 99.86%, and the correct identification ratio ranged from 97.73% to 100%. The experimental results demonstrate that the proposed method is superior to advanced deep learning-based methods. Nevertheless, this method has real-time tracking ability, which can acquire online trajectory data without high-cost hardware configuration.(1) Pulmonary vein stenosis (PVS) can be a severe, progressive disease with lung involvement. We aimed to characterize findings by computed tomography (CT) and identify factors associated with death; (2) Veins and lung segments were classified into five locations right upper, middle, and lower; and left upper and lower. Severity of vein stenosis (0-4 = no disease-atresia) and lung segments (0-3 = unaffected-severe) were scored. Androgen Receptor Antagonists high throughput screening A PVS severity score (sum of all veins + 2 if bilateral disease; maximum = 22) and a total lung severity score (sum of all lung segments; maximum = 15) were reported; (3) Of 43 CT examinations (median age 21 months), 63% had bilateral disease. There was 30% mortality by 4 years after CT. Individual-vein PVS severity was associated with its corresponding lung segment severity (p 6 had higher hazard of death; and perihilar induration had lower hazard of death; (4) Multiple CT-derived variables of PVS severity and lung disease have prognostic significance. PVS severity correlates with lung disease severity.Aging has been implicated in the alteration of taste acuity. Diet can affect taste sensitivity. We aimed to investigate the types of tastes altered in elderly Korean people and factors associated with taste alteration in relation to dietary intake and other factors. Elderly participants (≥65 years) and young adults were assessed to determine their recognition thresholds (RT) for sweet, salty, bitter, sour, and umami tastes. Elderly participants were further surveyed for dietary intake and non-nutritional factors. Five taste RTs were correlated with age, but only four taste RTs, except sweetness, differed between the elderly participants and young adults. Inadequate intake of iron, thiamin, folic acid, zinc, and phosphorus among the elderly participants was related to elevated taste RT levels, except for bitter taste. In both correlation and regression analyses, only salty and sour RTs were associated with energy, iron, thiamin, fiber, vitamin C, and riboflavin levels in the elderly participants. The elderly participants' taste RTs exhibited strong associations with quality of life (QOL) but showed partial relationships with physical activity, number of medicine intakes, social gatherings, and education. Taste sensitivity may decrease with age, which is further influenced by insufficient dietary intake, especially iron and thiamin, and QOL.Weakly supervised instance segmentation (WSIS) provides a promising way to address instance segmentation in the absence of sufficient labeled data for training. Previous attempts on WSIS usually follow a proposal-based paradigm, critical to which is the proposal scoring strategy. These works mostly rely on certain heuristic strategies for proposal scoring, which largely hampers the sustainable advances concerning WSIS. Towards this end, this paper introduces a novel framework for weakly supervised instance segmentation, called Weakly Supervised R-CNN (WS-RCNN). The basic idea is to deploy a deep network to learn to score proposals, under the special setting of weak supervision. To tackle the key issue of acquiring proposal-level pseudo labels for model training, we propose a so-called Attention-Guided Pseudo Labeling (AGPL) strategy, which leverages the local maximal (peaks) in image-level attention maps and the spatial relationship among peaks and proposals to infer pseudo labels. We also suggest a novel training loss, called Entropic OpenSet Loss, to handle background proposals more effectively so as to further improve the robustness.