Exploration of highly selective fluorogenic ‘on–off' chemosensor for H2PO4− ions: ICT‐based sensing and ATPase activity profiling

Abstract In this study, the recognition contour of Chemosensor 1 was investigated using semiaqueous methanol (X_H, mole fraction = 0.31) for a range of anions and bioactive species. Host–receptor signalling based on the internal charge transfer mechanism for Chemosensor 1 was explored and reported. Structure of Chemosensor 1 and its plausible anion coordination based on hydrogen bonding is complemented with density functional theory. Consequently, we investigated the applicability of the synthesized probe in blood plasma, urine, tap water samples, and for monitoring of ATP in lysosomes by apyrase enzyme.     

Three‐dimensional ring‐oven washing technique for a paper‐based immunodevice

Washing is a standard step for enzyme‐linked immunosorbent assays (ELISA) performed on a paper‐based chip, in which nonspecific‐binding antibodies and antigens should be removed completely from the paper surface. In this study, a novel three‐dimensional (3D) washing strategy using a heating ring‐oven was carried out on a paper‐based chip. Compared with a plane washing mode by a ring‐oven, this 3D washing strategy obtained a lower background, as gravity played an important role in the washing step. The paper‐based chip was placed on a 3D plastic holder and the waste area was connected to a heating ring. Use of a heating waste area meant that the nonspecific‐binding protein was continuously carried to the waste area through gravity and capillary action. The angle between the plastic holder and the ring plane was carefully selected. The effect of washing on different parts of the detection area was investigated by upconversion fluorescence and chemiluminescence (CL). This novel 3D washing strategy was performed for carcinoembryonic antigen detection through CL and a lower detection limit of 2 pg ml^?1 was obtained. This approach provides an effective washing strategy to remove nonspecific‐binding antibody from a paper‐based immunodevice.     

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning

IntroductionOur markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-gated treatment due to inaccuracies and a high radiation dose. We developed a deep neural network (DNN) to generate 4DCT from 3DCT data.MethodsWe used 2420 thoracic 4DCT datasets from 436 patients to train a DNN, designed to export 9 deformation vector fields (each field representing one-ninth of the respiratory cycle) from each CT dataset based on a 3D convolutional autoencoder with shortcut connections using deformable image registration. Then 3DCT data at exhale were transformed using the predicted deformation vector fields to obtain simulated 4DCT data. We compared markerless tracking accuracy between original and simulated 4DCT datasets for 20 patients. Our tracking algorithm used a machine learning approach with patient-specific model parameters. For the training stage, a pair of digitally reconstructed radiography images was generated using 4DCT for each patient. For the prediction stage, the tracking algorithm calculated tumor position using incoming fluoroscopic image data.ResultsDiaphragmatic displacement averaged over 40 cases for the original 4DCT were slightly higher (<1.3 mm) than those for the simulated 4DCT. Tracking positional errors (95th percentile of the absolute value of displacement, “simulated 4DCT” minus “original 4DCT”) averaged over the 20 cases were 0.56 mm, 0.65 mm, and 0.96 mm in the X, Y and Z directions, respectively.ConclusionsWe developed a DNN to generate simulated 4DCT data that are useful for markerless tumor tracking when original 4DCT is not available. Using this DNN would accelerate markerless tumor tracking and increase treatment accuracy in thoracoabdominal treatment.     

1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals

PurposeCardiovascular disease (CVD) is a leading cause of death globally. Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for the diagnosis of CVD. The automated and robust detection of CVD from ECG signals plays a significant role for early and accurate clinical diagnosis. The purpose of this study is to provide automated detection of coronary artery disease (CAD) from ECG signals using capsule networks (CapsNet).MethodsDeep learning-based approaches have become increasingly popular in computer aided diagnosis systems. Capsule networks are one of the new promising approaches in the field of deep learning. In this study, we used 1D version of CapsNet for the automated detection of coronary artery disease (CAD) on two second (95,300) and five second-long (38,120) ECG segments. These segments are obtained from 40 normal and 7 CAD subjects. In the experimental studies, 5-fold cross validation technique is employed to evaluate performance of the model.ResultsThe proposed model, which is named as 1D-CADCapsNet, yielded a promising 5-fold diagnosis accuracy of 99.44% and 98.62% for two- and five-second ECG signal groups, respectively. We have obtained the highest performance results using 2 s ECG segment than the state-of-art studies reported in the literature.Conclusions1D-CADCapsNet model automatically learns the pertinent representations from raw ECG data without using any hand-crafted technique and can be used as a fast and accurate diagnostic tool to help cardiologists.     

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images

PurposeDeep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the limited number of imaging studies available at a single facility.This study aimed to find a simple and low-cost method to increase the accuracy of deep learning semantic segmentation for radiation therapy of prostate cancer.MethodsIn total, 556 cases with non-contrast CT images for prostate cancer radiation therapy were examined using a two-dimensional U-Net. Initially, all slices were used for the input data. Then, we removed slices of the cranial portions, which were beyond the margins of the bladder and rectum. Finally, the ground truth labels for the bladder and rectum were added as channels to the input for the prostate training dataset.ResultsThe highest mean dice similarity coefficients (DSCs) for each organ in the test dataset of 56 cases were 0.85 ± 0.05, 0.94 ± 0.04 and 0.85 ± 0.07 for the prostate, bladder and rectum, respectively. Removal of the cranial slices from the original images significantly increased the DSC of the rectum from 0.83 ± 0.09 to 0.85 ± 0.07 (p < 0.05). Adding bladder and rectum information to prostate training without removing the slices significantly increased the DSC of the prostate from 0.79 ± 0.05 to 0.85 ± 0.05 (p < 0.05).ConclusionsThese cost-free approaches may be useful for new applications, which may include updated models and datasets. They may be applicable to other organs at risk (OARs) and clinical targets such as elective nodal irradiation.     

Could 2021–2030 be the decade to couple new human values with ecological restoration? Valuable insights and actions are emerging from the Colombian Amazon

Looking ahead to the United Nations' 2021–2030 Decade of Ecosystem Restoration, we would like to ponder and discuss two fundamental goals to improve, mainstream, and scale up ecological restoration. The first is to cultivate alternative visions of the human dimension in relation to ecological restoration and other restorative activities. The second is to develop shared protocols for planning, revamping, and monitoring the progress of social goals related to ecological restoration within the social construction theoretical framework, based on three interrelated dimensions: stakeholder‐based problem definition, social representations, and legitimation. We draw on ongoing work in Caquetá (Colombian Amazonia) to consider how these dimensions may be incorporated into tangible restoration practices. Caquetá is facing the highest deforestation rates in the Amazonian region due to a highly volatile sociopolitical context and recent armed conflicts that have claimed thousands of victims to date. We conclude that the work in Caquetá demonstrates a process of social construction that effectively couples new human values with ecological restoration. Our work also provides evidence that the human dimension of restoration is a central issue in the restoration of human, social, and ecosystem health and must be integrated into the framework of the coming Decade of Ecosystem Restoration.     

Colorimetric paper bioassay by horseradish peroxidase for the detection of catechol and resorcinol in aqueous samples

Abstract

Phenolic compounds such as catechol and resorcinol are toxic and persistent pollutants in the aqueous environment. Detection procedures such as chromatographic and spectrophotometric methods are time-consuming and require sophisticated instruments with skilled manpower. Development of a simple, cost effective, portable and disposable paper based biosensor could be a better alternative to the conventional methods. The present study attempted to develop a paper based biosensor by immobilizing horseradish peroxidase enzyme to detect catechol and resorcinol in aqueous samples. Horseradish peroxidase catalyzes the oxidation of phenolic compounds to semiquinones, which on reaction with a chromogen, 3-methyl 2-benzothiazolinone hydrazine (MBTH) gives faint pink to red color depending on the compound and its concentration in the sample is the basis for biosensing application. Different methods of enzyme immobilization on filter paper like physical adsorption, covalent coupling, and polysaccharide entrapment were executed. The performance of the various enzyme immobilization methods was evaluated by analyzing the developed color intensity using ImageJ software. Entrapment technique is the most effective method of immobilizing enzyme on the filter paper that produces the highest color intensity with better stability. The visible limit of detection (LoD) was observed as 0.45?mM (50?mg/L) for catechol and 0.09?mM (10?mg/L) for resorcinol in aqueous samples.     

Spatial synchrony in the response of a long range migratory species (Salmo salar) to climate change in the North Atlantic Ocean

A major challenge in understanding the response of populations to climate change is to separate the effects of local drivers acting independently on specific populations, from the effects of global drivers that impact multiple populations simultaneously and thereby synchronize their dynamics. We investigated the environmental drivers and the demographic mechanisms of the widespread decline in marine survival rates of Atlantic salmon (Salmo salar) over the last four decades. We developed a hierarchical Bayesian life cycle model to quantify the spatial synchrony in the marine survival of 13 large groups of populations (called stock units, SU) from two continental stock groups (CSG) in North America (NA) and Southern Europe (SE) over the period 1971–2014. We found strong coherence in the temporal variation in postsmolt marine survival among the 13 SU of NA and SE. A common North Atlantic trend explains 37% of the temporal variability of the survivals for the 13 SU and declines by a factor of 1.8 over the 1971–2014 time series. Synchrony in survival trends is stronger between SU within each CSG. The common trends at the scale of NA and SE capture 60% and 42% of the total variance of temporal variations, respectively. Temporal variations of the postsmolt survival are best explained by the temporal variations of sea surface temperature (SST, negative correlation) and net primary production indices (PP, positive correlation) encountered by salmon in common domains during their marine migration. Specifically, in the Labrador Sea/Grand Banks for populations from NA, 26% and 24% of variance is captured by SST and PP, respectively and in the Norwegian Sea for populations from SE, 21% and 12% of variance is captured by SST and PP, respectively. The findings support the hypothesis of a response of salmon populations to large climate‐induced changes in the North Atlantic simultaneously impacting populations from distant continental habitats.