Motor deficits were assessed using a rotarod and cylinder test. The corpus striatum was harvested, processed, and stained using H&E and Nissl stains. Cellular density was analyzed using Image J software 1.8.0. Results Motor deficit was observed in -D2R animals administered chlorpromazine with less improvement in WG compared to control (p less then 0.05) in both rotarod and cylinder test. Ascorbic acid (SVCT2R stimulation) significantly (p less then 0.001) improved the latency of fall and climbing attempts observed in -D2R animals. The density of basophilic trigoid bodies was significantly (p less then 0.001) restored in -D2R+SVCT2R group, suggesting recovery of neural activity in the corpus striatum. Moreover, the hallmarks of neuronal degeneration were less expressed in the ascorbic acid treatment groups. Conclusions Ascorbic acid putatively ameliorates extrapyramidal symptoms observed in D2R blockage by chlorpromazine in Wistar rats.[This corrects the article DOI 10.2196/14081.].In genomics, a wide range of machine learning methodologies have been investigated to annotate biological sequences for positions of interest such as transcription start sites, translation initiation sites, methylation sites, splice sites and promoter start sites. In recent years, this area has been dominated by convolutional neural networks, which typically outperform previously-designed methods as a result of automated scanning for influential sequence motifs. However, those architectures do not allow for the efficient processing of the full genomic sequence. As an improvement, we introduce transformer architectures for whole genome sequence labeling tasks. We show that these architectures, recently introduced for natural language processing, are better suited for processing and annotating long DNA sequences. We apply existing networks and introduce an optimized method for the calculation of attention from input nucleotides. To demonstrate this, we evaluate our architecture on several sequence labeling tasks, and find it to achieve state-of-the-art performances when comparing it to specialized models for the annotation of transcription start sites, translation initiation sites and 4mC methylation in E. coli.Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequencederived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model’s good generalization ability.It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification need to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance.Postural stability is an important indicator of balance and is commonly evaluated in neurorehabilitation. We proposed a system based on a virtual reality (HTC Vive) system with a tracker at the lumbar area. The position data of the tracker were obtained through detection of the sensors on the tracker by the VR system. The reliability and validity of these sway parameters to measure postural stability were evaluated. Twenty healthy adults had their postural sway measured with this system and a force platform system under four stance conditions, with wide- or narrow-stance and eyes open or closed. The path data from both systems were computed to obtain the following parameters the mean distance and the mean velocity in the medial-lateral and anterior-posterior directions and the 95% confidence ellipse area. The reliability of the Vive-based sway measures was tested with intraclass correlation coefficients (ICCs). The convergent validity was tested against the center of pressure (COP) parameters from the force platform system. Finally, the discriminative validity was tested for the above four conditions. The results indicated that the Vive-based sway parameters had moderate to high reliability (ICCs 0.56 ~ 0.90) across four conditions and correlated moderately to very highly with the COP parameters ( r = 0.420 ∼ 0.959 ). Bland-Altman plotting showed generally good agreement, with negative offset for the Vive-based sway parameters. The sway parameters obtained by the Vive-based system also discriminated well among the tasks. In conclusion, the results support this system as a simple and easy-to-use tool to evaluate postural stability with acceptable reliability and validity.Gait asymmetry in lower-limb amputees can lead to several secondary conditions that can decrease general health and quality of life. Including augmented sensory feedback in rehabilitation programs can effectively mitigate spatiotemporal gait irregularities. Such benefits can be obtained with non-invasive haptic systems representing an advantageous choice for usability in overground training and every-day life. In this study, we tested a wearable tactile feedback device delivering short-lasting (100ms) vibrations around the waist syncronized to gait events, to improve the temporal gait symmetry of lower-limb amputees. Three above-knee amputees participated in the study. The device provided bilateral stimulations during a training program that involved ground-level gait training. After three training sessions, participants showed higher temporal symmetry when walking with the haptic feedback in comparison to their natural walking (resulting symmetry index increases of +2.8% for Subject IDA, +12.7% for Subject IDB and +2.9% for Subject IDC). One subject retained improved symmetry (Subject IDB,+14.9%) even when walking without the device. Gait analyses revealed that higher temporal symmetry may lead to concurrent compensation strategies in the trunk and pelvis. Overall, the results of this pilot study confirm the potential utility of sensory feedback devices to positively influence gait parameters when used in supervised settings. Future studies shall clarify more precisely the training modalities and the targets of rehabilitation programs with such devices.We present P6, a declarative language for building high performance visual analytics systems through its support for specifying and integrating machine learning and interactive visualization methods. As data analysis methods based on machine learning and artificial intelligence continue to advance, a visual analytics solution can leverage these methods for better exploiting large and complex data. However, integrating machine learning methods with interactive visual analysis is challenging. Existing declarative programming libraries and toolkits for visualization lack support for coupling machine learning methods. By providing a declarative language for visual analytics, P6 can empower more developers to create visual analytics applications that combine machine learning and visualization methods for data analysis and problem solving. Through a variety of example applications, we demonstrate P6’s capabilities and show the benefits of using declarative specifications to build visual analytics systems. We also identify and discuss the research opportunities and challenges for declarative visual analytics.In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For comparing data streams involving multiple items (e.g., words in texts, actors or action types in action sequences, visited places in itineraries, etc.), we propose Co-Bridges, a visual design involving connection and comparison techniques that reveal similarities and differences between two streams. Co-Bridges use river and bridge metaphors, where two sides of a river represent data streams, and bridges connect temporally or sequentially aligned segments of streams. Commonalities and differences between these segments in terms of involvement of various items are shown on the bridges. Interactive query tools support the selection of particular stream subsets for focused exploration. The visualization supports both qualitative (common and distinct items) and quantitative (stream volume, amount of item involvement) comparisons. We further propose Comparison-of-Comparisons, in which two or more Co-Bridges corresponding to different selections are juxtaposed. We test the applicability of the Co-Bridges in different domains, including social media text streams and sports event sequences. We perform an evaluation of the users’ capability to understand and use Co-Bridges. The results confirm that Co-Bridges is effective for supporting pair-wise visual comparisons in a wide range of applications.Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration.