Kinase profiling of liposarcomas using RNAi and drug screening assays identified druggable targets.

BackgroundLiposarcoma, the most common soft tissue tumor, is understudied cancer, and limited progress has been made in the treatment of metastatic disease. The Achilles heel of cancer often is their kinases that are excellent therapeutic targets. However, very limited knowledge exists of therapeutic critical kinase targets in liposarcoma that could be potentially used in disease management.MethodsLarge RNAi and small-molecule tyrosine kinase inhibitor screens were performed against the proliferative capacity of liposarcoma cell lines of different subtypes. Each small molecule inhibitor was either FDA approved or in a clinical trial.ResultsScreening assays identified several previously unrecognized targets including PTK2 and KIT in liposarcoma. We also observed that ponatinib, multi-targeted tyrosine kinase inhibitor, was the most effective drug with anti-growth effects against all cell lines. In vitro assays showed that ponatinib inhibited the clonogenic proliferation of liposarcoma, and this anti-growth effect was associated with apoptosis and cell cycle arrest at the G0/G1 phase as well as a decrease in the KIT signaling pathway. In addition, ponatinib inhibited in vivo growth of liposarcoma in a xenograft model.ConclusionsTwo large-scale kinase screenings identified novel liposarcoma targets and a FDA-approved inhibitor, ponatinib with clear anti-liposarcoma activity highlighting its potential therapy for treatment of this deadly tumor

Aberrant splicing of U12-type introns is the hallmark of ZRSR2 mutant myelodysplastic syndrome.

Somatic mutations in the spliceosome gene ZRSR2-located on the X chromosome-are associated with myelodysplastic syndrome (MDS). ZRSR2 is involved in the recognition of 3'-splice site during the early stages of spliceosome assembly; however, its precise role in RNA splicing has remained unclear. Here we characterize ZRSR2 as an essential component of the minor spliceosome (U12 dependent) assembly. shRNA-mediated knockdown of ZRSR2 leads to impaired splicing of the U12-type introns and RNA-sequencing of MDS bone marrow reveals that loss of ZRSR2 activity causes increased mis-splicing. These splicing defects involve retention of the U12-type introns, while splicing of the U2-type introns remain mostly unaffected. ZRSR2-deficient cells also exhibit reduced proliferation potential and distinct alterations in myeloid and erythroid differentiation in vitro. These data identify a specific role for ZRSR2 in RNA splicing and highlight dysregulated splicing of U12-type introns as a characteristic feature of ZRSR2 mutations in MDS

DIAGNOSIS OF ADHD SYNDROME BY COMPARITIVE ANALYSIS OF EEG SIGNALS OF BRAIN

One of the incurable mental disorder, which is estimated to occur in about of all the children (5-15 years, approximately 6.4 million), is Attention Deficit Hyper Active Disorder (ADHD). Electroencephalogram (EEG) is a best method for monitoring, recording and measuring spontaneous voltage fluctuations of the brain that caused due to the ionic current associated with the neurons. Due to having many advantage of using EEG over MRI, PET and MEG in the detection and diagnosis of ADHD, we presented the comparative analysis and distance measure techniques for detection and classification of this disorder in the childhood. In comparative analysis, we compare different parameters of EEG signals of ADHD affected children with normal children. An algorithm is developed to classify the children effectively as normal or affected

Molecular investigation between four Himalayan pines of India through random amplified polymorphic DNA markers

Studies were undertaken to identify genetic relationship in four different species of Pinus L. through randomly amplified polymorphic DNA (RAPD) markers. A total of 500 DNA fragments ranging from 234 to 1353 bp were amplified using 5 selected primers. The number of amplification products produced by a primer ranged from as low as 4 to a maximum of 13, with an average of 8 bands per primer. The cluster analysis revealed one major cluster and one outlier. In the major cluster, Pinus roxburghii from Malithi, Pinus wallichiana from Malithi, P. wallichiana from Taradevi H.P, Pinus kesiya from Taradevi H.P, Pinus gerardiana from Chamba and P. roxburghii from Chamba falls into subcluster 1 and P. kesiya from Jubbal (east) and P. kesiya from Jubbal (west) falls into subcluster 2. The similarity coefficient value varied from 0.54 to 0.88. The highest similarity coefficient (0.88) was detected between samples collected from P. wallichiana (Malithi) and P. roxburghii (Malithi) as well as between P. roxburghii (Malithi) & P. wallichiana (Taradevi, H.P) and the lowest (0.54) was detected between the P. gerardiana (Raspa) and P. kesiya (South Vietnam). The level of polymorphism in our study was not so much which showed that samples used for the analysis could have close relationship.Key words: Randomly amplified polymorphic DNA (RAPD), similarity coefficient, polymorphism, Pinus, primer

Transfer Learning based Automated Essay Summarization

The human evaluation of essays has become a very time-consuming process as the number of schools and universities has grown. The available software entities are unable to assess the sentiment associated with essays. Thus, we propose a model using Natural Language Processing to assess the essay based on both grammar and sentiment associated with the essay by using linear regression and ULMFiT (Universal Language Model Fine-tuning for Text Classification) models.  Evaluation of essay is done in two parts. Part one is on essay grading with respect to grammar with maximum 12 and minimum 0 grade points and in part two score of 0/1 for sentiment analysis with 0 being negative and 1 being positive. The model can be used to score the essay and discard any essay with a score less than a specified value or specified sentiment score

Intelligent diagnosis system based on artificial intelligence models for predicting freezing of gait in Parkinson’s disease

IntroductionFreezing of gait (FoG) is a significant issue for those with Parkinson’s disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life.MethodsThis research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson’s disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration.ResultsThe decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG