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A DNAzyme-driven haphazard biped Genetics jogging nanomachine for vulnerable recognition associated with uracil-DNA glycosylase action.
We propose a network representation learning method based on matrix factorisation to learn low-dimensional vector representations of drug and protein nodes. On the basis of these low-dimensional node representations, a GBDT-based prediction model was constructed and it obtains the association scores through establishing multiple decision trees for a drug-protein pairs. NGDTP is an ensemble learning model that fully utilises all the negative samples to effectively alleviate the problem of class imbalance. NGDTP achieves superior prediction performance when it is compared with several state-of-the-art methods. The experimental results indicate that NGDTP also retrieves more actual drug-protein interactions in the top part of prediction result, which drew significant attention from the biologists. In addition, case studies on 10 drugs further confirmed the ability of the NGDTP to identify potential candidate proteins for drugs.At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression iden-tification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depres-sion.The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational approaches for determining functionally-relevant structures. The majority of structures generated in silico are not relevant. Discriminating relevant/native protein structures from non-native ones is an outstanding challenge in computational structural biology. Inherently, this is a recognition problem that can be addressed under the umbrella of machine learning. In this paper, based on the premise that near-native structures are effectively anomalies, we build on the concept of anomaly detection in machine learning. We propose methods that automatically select relevant subsets, as well as methods that select a single structure to offer as prediction. Evaluations are carried out on benchmark datasets and demonstrate that the proposed methods advance the state of the art. The presented results motivate further building on and adapting concepts and techniques from machine learning to improve recognition of near-native structures in protein structure prediction.To assist drug development, many computational methods have been proposed to identify potential drug-disease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods calculate the similarities of drugs and diseases by using various chemical or biological features. However, since these features may be unknown or hard to collect, such methods will not work in the face of incomplete data. Besides, due to the lack of validated negative samples in the drug-disease associations data, most methods have no choice but to simply select some unlabeled samples as negative ones, which may introduce noises and decrease the reliability of prediction. Herein, we propose a new method (TS-SVD) which only uses those known drug-protein, disease-protein and drug-disease interactions to predict the potential drug-disease associations. In a constructed drug-proteindisease heterogeneous network, assuming that drugs/diseases relating to some common proteins or diseases/drugs may be similar, we get the common neighbors count matrix of drugs/diseases, then convert it to a topological similarity matrix. After that, we get low dimensional embedding representations of drugdisease pairs by using topological features and singular value decomposition. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the k-step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods. Case studies have further shown the practicality of our method in discovering novel drug-disease associations.Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes. We achieve this by defining the significance of the compartment, constructing edge-weighted PPINs, finding protein complexes with a non-negative matrix factorization approach, generating disease-specific networks based on the known disease genes, prioritizing disease candidate genes with a WDC method. As a case study, we investigate the breast cancer but the techniques described here are applicable to other disorders. For the top genes scored by BCCGD approach, we utilize the literature retrieving method to test the correlations of them with the breast cancer. The results show that BCCGD discover some novel breast cancer candidate genes which are valuable references for the biomedical scientists.Understanding the use of haptic assistance to facilitate motor learning is a critical issue, especially in the context of tasks requiring control of motor variability. However, the question of how haptic assistance should be designed in tasks with redundancy, where multiple solutions are available, is currently unknown. Here we examined the effect of haptic assistance that either allowed or restricted the use of redundant solutions on the learning of a bimanual steering task. 60 college-aged participants practiced steering a single cursor placed in between their hands along a smooth W-shaped track of a certain width as quickly as possible. Haptic assistance was either applied at (i) the 'task' level using a force channel that only constrained the cursor to the track, allowing for the use of different hand trajectories, or (ii) the 'individual effector' level using a force channel that constrained each hand to a specific trajectory. In addition, we also examined the effect of simply 'fading' assistance in a linear fashion- i.e., decreasing force gains with practice to reduce dependence on haptic assistance. Results showed all groups improved with practice - however, groups with haptic assistance at the individual effector level performed worse than those at the task level. Besides, we did not find sufficient evidence for the benefits of linearly fading assistance in our task. Overall, the results suggest that haptic assistance is not effective for motor learning when it restricts the use of redundant solutions.Walking can be simplified as an inverted pendulum motion where both legs generate linear impulses to redirect the center of mass (COM) into every step. In this work, we describe a system to assist walking in a simpler way than exoskeletons by providing linear impulses directly at the COM instead of providing torques at the joints. We developed a novel waist endeffector and high-level controller for an existing cable-robot. The controller allows for the application of cyclic horizontal force profiles with desired magnitudes, timings, and durations based on detection of the step timing. By selecting a lightweight rubber series elastic element with optimal stiffness and carefully tuning the gains of the closed-loop proportional-integral-derivative (PID) controller in a number of single-subject experiments, we were able to reduce the within-step root mean square error between desired and actual forces up to 1.21% of body weight. This level of error is similar or lower compared to the performance of other robotic tethers designed to provide variable or constant forces at the COM. The system can produce force profiles with peaks of up to 15 ± 2% of body weight within a root mean square error (RMSE) of 2.5% body weight. This system could be used to assist patient populations that require levels of assistance that are greater than current exoskeletons and in a way that does not make the user rely on vertical support.The effectiveness of haptic feedback devices highly depends on the perception of tactile stimuli, which differs across body parts and can be affected by movement. In this study, a novel wearable sensory feedback apparatus made of a pair of pressure-sensitive insoles and a belt equipped with vibrotactile units is presented; the device provides time-discrete vibrations around the waist, synchronized with biomechanically-relevant gait events during walking. Experiments with fifteen healthy volunteers were carried out to investigate users' tactile perception on the waist. Stimuli of different intensities were provided at twelve locations, each time synchronously with one pre-defined gait event (i.e. heel strike, flat foot or toe off), following a pseudo-random stimulation sequence. Reaction time, detection rate and localization accuracy were analyzed as functions of the stimulation level and site and the effect of gait events on perception was investigated. Results revealed that above-threshold stimuli (i.e. vibrations characterized by acceleration amplitudes of 1.92g and 2.13g and frequencies of 100 Hz and 150 Hz, respectively) can be effectively perceived in all the sites and successfully localized when the intertactor spacing is set to 10 cm. check details Moreover, it was found that perception of time-discrete vibrations was not affected by phase-related gating mechanisms, suggesting that the waist could be considered as a preferred body region for delivering haptic feedback during walking.Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis.
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