Consistent long-term practice leads to consistent improvement: Benefits of self-managed therapy for language and cognitive deficits using a digital therapeutic.

Background: Although speech-language therapy (SLT) is proven to be beneficial to recovery of post-stroke aphasia, delivering sufficiently high amounts of dosage remains a problem in real-world clinical practice. Self-managed SLT was introduced to solve the problem. Previous research showed in a 10-week period, increased dosage frequency could lead to better performance, however, it is uncertain if dosage still affects performance over a longer period of practice time and whether gains can be seen following practice over several months. Objective: This study aims to evaluate data from a health app (Constant Therapy) to investigate the relationship between dosage amount and improvements following a 30-week treatment period. Two cohorts of users were analyzed. One was comprised of patients with a consistent average weekly dosage amount and the other cohort was comprised of users whose practice had higher variability. Methods: We conducted two analyses with two cohorts of post-stroke patients who used Constant Therapy. The first cohort contains 537 "consistent" users, while the second cohort contains 2,159. The 30-week practice period was split into three consecutive 10-week practice windows to calculate average dosage amount. In each 10-week practice period, patients were grouped by their average dosage into low (0-15 min/week), medium (15-40 min/week) and moderate dosage (greater than 40 min/week) groups. Linear mixed-effects models were employed to evaluate if dosage amount was a significant factor affecting performance. Pairwise comparison was also applied to evaluate the slope difference between groups. Results: For the consistent cohort, medium (ß = .002, t 17,700 = 7.64, P < .001) and moderate (ß = .003, t 9,297 = 7.94, P < .001) dosage groups showed significant improvement compared to the low dosage group. The moderate group also showed greater improvement compared to the medium group. For the variable cohort in analysis 2, the same trend was shown in the first two 10-week windows, however, in weeks 21-30, the difference was insignificant between low and medium groups (ß = .001, t = 1.76, P = .078). Conclusions: This study showed a higher dosage amount is related to greater therapy outcomes in over 6 months of digital self-managed therapy. It also showed that regardless of the exact pattern of practice, self-managed SLT leads to significant and sustained performance gains.

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia.

BACKGROUND: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. METHODS: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. RESULTS: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68-0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). CONCLUSIONS: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.

Anti-Cancer and Anti-Inflammatory Potential of Furanocoumarins from Ammi majus L.

Secondary metabolites have potential benefits to human being. They are used in the food, agricultural and pharmaceutical industries. The secondary metabolite of furanocoumarins from different plant sources is essential in various skin-related ailments. Biologically, these chemicals are isolated from different plants in the Apiaceae, Fabaceae, Rutaceae and Moraceae families. Ammi majus L. is one of the most common plants in the family of Apiaceae with a large quantity of derivatives. The furanocoumarin derivatives defend the plant by fighting external enemies by Systemic Acquired Resistance (SAR). Via suppressing or retarding microbial growth in infected parts, these derivatives, along with SAR, help to alleviate inflammation in the human body. Latest evidence of these compounds has been established in the treatment of cancer, but the mechanism that needs to be elaborated is not yet understood. Recent studies have shown that furanocoumarin derivatives bind to DNA base pairs and block DNA replication. This may be a potential pathway that helps to regulate the growth of cancerous cells. This article reflects on the pharmaceutical data of furanocoumarins and their different mechanisms in these cases.

Elucidating stress proteins in rice (Oryza sativa L.) genotype under elevated temperature: a proteomic approach to understand heat stress response.

Rice is one of the widely consumed staple foods among the world's human population. Its production is adversely affected by high temperature and is more pronounced at flowering stage. Elucidating elevated temperature stress-related proteins as well as associated mechanisms is inevitable for improving heat tolerance in rice. In the present study, a proteomic analysis of heat-sensitive rice genotype, IET 21405 was conducted. Two-dimensional electrophoresis (2-DE) and MALDI-TOF/MS-based proteomics approaches revealed a total of 73 protein spots in rice leaf. The protein profiles clearly indicated variations in protein expression between the control and heat treated rice genotypes. Functional assessment of 73 expressed proteins revealed several mechanisms thought to be involved in high temperature including their putative role in metabolism, energy, protein synthesis, protein transport/storage, etc. Besides these, some proteins are expected to involve in photosynthesis, tricarboxylic acid (TCA) cycle, glycolysis and other proteins for energy production. The proteins identified in the present study provide a strong basis to elucidate gene function of these proteins and to explain further the molecular mechanisms underlying the adaptation of rice to high temperature stress.

Bryophytes: Hoard of remedies, an ethno-medicinal review.

Bryophytes are the second largest group of land plants after angiosperms. There is very less knowledge available about medicinal properties of these plants. Bryophytes are popular remedy among the tribal people of different parts of the world. Tribal people use these plants to cure various ailments in their daily lives. Bryophytes are used to cure hepatic disorders, skin diseases, cardiovascular diseases, used as antipyretic, antimicrobial, wound healing and many more other ailments by different tribal communities of Africa, America, Europe, Poland, Argentina, Australia, New Zealand, Turkey, Japan, Taiwan, Pakistan, China, Nepal and different parts of South, North and Eastern India. Apart from ethno-medicinal uses some bryophytes possesses antitumor activities against different cancer cell lines and this property of bryophytes needs to be more focused in the future. Compile information about medicinal properties and anticancer properties of bryophytes is lacking till date. In the present review, the authors tried to compile all the ethno-medicinal and other related information of bryophytes and fill the knowledge lacuna in this particular field. Some published reviews are available but the information is segregated. This manuscript will help people doing research in the bryophytes.

Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security.

In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

A novel video dataset for change detection benchmarking.

Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website and will be updated with feedback from academia and industry in the future.

Learning-based, automatic 2D-to-3D image and video conversion.

Despite a significant growth in the last few years, the availability of 3D content is still dwarfed by that of its 2D counterpart. To close this gap, many 2D-to-3D image and video conversion methods have been proposed. Methods involving human operators have been most successful but also time-consuming and costly. Automatic methods, which typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality for they rely on assumptions that are often violated in practice. In this paper, we propose a new class of methods that are based on the radically different approach of learning the 2D-to-3D conversion from examples. We develop two types of methods. The first is based on learning a point mapping from local image/video attributes, such as color, spatial position, and, in the case of video, motion at each pixel, to scene-depth at that pixel using a regression type idea. The second method is based on globally estimating the entire depth map of a query image directly from a repository of 3D images ( image+depth pairs or stereopairs) using a nearest-neighbor regression type idea. We demonstrate both the efficacy and the computational efficiency of our methods on numerous 2D images and discuss their drawbacks and benefits. Although far from perfect, our results demonstrate that repositories of 3D content can be used for effective 2D-to-3D image conversion. An extension to video is immediate by enforcing temporal continuity of computed depth maps.

Action recognition from video using feature covariance matrices.

We propose a general framework for fast and accurate recognition of actions in video using empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are computed from video to provide a localized description of the action, and subsequently aggregated in an empirical covariance matrix to compactly represent the action. Two supervised learning methods for action recognition are developed using feature covariance matrices. Common to both methods is the transformation of the classification problem in the closed convex cone of covariance matrices into an equivalent problem in the vector space of symmetric matrices via the matrix logarithm. The first method applies nearest-neighbor classification using a suitable Riemannian metric for covariance matrices. The second method approximates the logarithm of a query covariance matrix by a sparse linear combination of the logarithms of training covariance matrices. The action label is then determined from the sparse coefficients. Both methods achieve state-of-the-art classification performance on several datasets, and are robust to action variability, viewpoint changes, and low object resolution. The proposed framework is conceptually simple and has low storage and computational requirements making it attractive for real-time implementation.

Video condensation by ribbon carving.

Efficient browsing of long video sequences is a key tool in visual surveillance, e.g., for postevent video forensics, but can also be used for fast review of motion pictures and home videos. While frame skipping (fixed or adaptive) is straightforward to implement, its performance is quite limited. Although more efficient techniques have been developed, such as video summarization and video montage, they lose either the temporal or semantic context of events. A recently proposed method called video synopsis deals with some of these issues but involves multiple processing stages and is fairly complex. Video condensation, that we propose here, is novel in the way information is removed from the space-time video volume, is conceptually simple and relatively easy to implement. We introduce the concept of a video ribbon inspired by that of a seam recently proposed for image resizing. We recursively carve ribbons out by minimizing an activity-aware cost function using dynamic programming. The ribbon model we develop is flexible and permits an easy adjustment of the compromise between temporal condensation ratio and anachronism of events. We also propose sliding-window ribbon carving to handle streaming video and demonstrate the method's efficiency on motor and pedestrian traffic data.