Search

The article Mycorrhizal colonization and phosphorus uptake in presence of PGPRs along with nematode infection, written by Ishwar Prakash Sharma and A. K. Sharma, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 20 September 2018 with open access. [ABSTRACT FROM AUTHOR]

Quantifying the number of people in various spaces of a commercial building is important for saving energy, optimizing space usage, and assisting with public safety. To accomplish these goals requires obtaining accurate, fine-grained counts in real time. However, existing methodologies are ineffective for covering large areas with high occupancy. We propose an occupancy-sensing system that uses multiple overhead fisheye cameras and state-of-the-art deep-learning algorithms to cover large spaces with high counting accuracy. Tested in a 2,000 ft2 space, our system shows 54% to 83% reduction in commonly-used error metrics compared to recent people-counting methods proposed for large-space scenarios. Our system is scalable to arbitrarily-large spaces; additional cameras can be integrated with minimal commissioning. We also introduce two new performance metrics for assessing counting accuracy that, unlike common metrics used to date, are independent of occupancy level and can be easily compared across different occupancy scenarios. [ABSTRACT FROM AUTHOR]

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 ( β  =   .002, t 17,700  = 7.64, P  < .001) and moderate ( β  =   .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 ( β  =   .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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Liu, Cordella, Ishwar, Betke and Kiran.)

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.

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., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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 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.

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.