Assistive Domotics for Physically Disabled

Assistive technology is aimed at providing automated aid to people with disabilities. It promotes greater independency by enabling disabled people to perform tasks they are unable to accomplish themselves. The present system developed would facilitate to ease by changing interacting automatically. It works based on mere voice commands and uses a low-power ZigBee wireless communication modules along with arduino. It recognizes the voice commands and send the data wirelessly using a zigbee module and thus corresponding switching operation is performed. The system is intended to control all essential electrical appliances for instance lights, fan, etc using voice commands

A Load Harmonizing Standard Based On Cloud Segregating For The Public Cloud

Concept of Harmonizing Load in cloud computing has an important effect on the performance. A cloudcomputing system which does not use load Harmonizing has numerous drawbacks. Now-a-days the usage of internet and related resources has increased widely. Due to this there is tremendous increase in workload. So there is uneven distribution of this workload which results in server overloading and may crash. In such systems the resources are not optimally used. Due to this the performance degrades and efficiency reduces. Cloud computing is made more efficient by better load Harmonizing methods. User satisfaction also improves. This paper introduces a better load Harmonizing Standard for the public cloud based on the cloud Segregating concept. A switch mechanism is introduced here to choose different strategies for different situations. The public cloud is divided into cloud partitions and different strategies are applied to balance the load on clouds. This paper introduces a system which has main controller, balancers and servers. The main controller selects the appropriate balancer for a particular job. The balancer further selects the server having minimum load. Hence, this system will help dynamically allocate jobs (data) to the least loaded server which will result in an efficiently balanced cloud system.

Stock price prognosticator using machine learning techniques.

Stock market price prediction is one of the favourite research topics under consideration for professionals from various fields like mathematics, statistics, history, finance, computer science engineering etc., as it requires a set of skills to predict variation of price of shares in a very volatile and challenging share market scenario. Share market trading is mostly dependent on sentiments of investors and other factors like economic policies, political changes, natural disasters etc., Many theories were forwarded, mathematical and statistical applications in conjunction with probability, to simplify the complex process. After the advent of computers, it got further simplified but still challenging due to various external influential factors ruling the volatility of the market prices. Thus, AI and ML algorithms were being developed, but for only for next day using Linear Regression procedures.Our project aims to predict the prices of shares more precisely and accurately using special algorithms using RNN by improvising the back propagation, feedback routines to overcome the short-term memory loss involved in RNN thus providing efficiency in LSTM applications.Our project emphasizes how the LSTM applications perform with datasets of extreme, larger and minimal fluctuating data

Increased serum tumor necrosis factor α levels in patients with lenalidomide-induced hypothyroidism

As the use of lenalidomide expands, the poorly understood phenomenon of lenalidomide-induced thyroid abnormalities will increase. In this study we compared rates of therapy-induced hypothyroidism in 329 patients with DLBCL treated with conventional chemotherapy (DLBCL-c) or conventional chemotherapy plus lenalidomide (DLBCL-len). We measured serum levels of tumor necrosis factor alpha (TNF-α), interferon gamma (IFN-γ), interleukin-6 (IL-6), interleukin-12 (IL-12), and interleukin-15 (IL-15) before and after treatment. We found a significantly higher rate of therapy-induced hypothyroidism in the DLBCL-len group (25.8% vs 1.3%), and we found a statistically significant increase in serum TNF-α in patients with lenalidomide-induced hypothyroidism

Genomic Profiling of T-Cell Neoplasms Reveals Frequent JAK1 and JAK3 Mutations With Clonal Evasion From Targeted Therapies

Purpose: The promise of precision oncology is that identification of genomic alterations will direct the rational use of molecularly targeted therapy. This approach is particularly applicable to neoplasms that are resistant to standard cytotoxic chemotherapy, like T-cell leukemias and lymphomas. In this study, we tested the feasibility of targeted next-generation sequencing in profiles of diverse T-cell neoplasms and focused on the therapeutic utility of targeting activated JAK1 and JAK3 in an index case. Patients and Methods: Using Foundation One and Foundation One Heme assays, we performed genomic profiling on 91 consecutive T-cell neoplasms for alterations in 405 genes. The samples were sequenced to high uniform coverage with an Illumina HiSeq and averaged a coverage depth of greater than 500× for DNA and more than 8M total pairs for RNA. An index case of T-cell prolymphocytic leukemia (T-PLL), which was analyzed by targeted next-generation sequencing, is presented. T-PLL cells were analyzed by RNA-seq, in vitro drug testing, mass cytometry, and phospho-flow. Results: One third of the samples had genomic aberrations in the JAK-STAT pathway, most often composed of JAK1 and JAK3 gain-of-function mutations. We present an index case of a patient with T-PLL with a clonal JAK1 V658F mutation that responded to ruxolitinib therapy. After relapse developed, an expanded clone that harbored mutant JAK3 M511I and downregulation of the phosphatase, CD45, was identified. We demonstrate that the JAK missense mutations were activating, caused pathway hyperactivation, and conferred cytokine hypersensitivity. Conclusion: These results underscore the utility of profiling occurrences of resistance to standard regimens and support JAK enzymes as rational therapeutic targets for T-cell leukemias and lymphomas

Genomic Profiling of T-Cell Neoplasms Reveals Frequent

Purpose: The promise of precision oncology is that identification of genomic alterations will direct the rational use of molecularly targeted therapy. This approach is particularly applicable to neoplasms that are resistant to standard cytotoxic chemotherapy, like T-cell leukemias and lymphomas. In this study, we tested the feasibility of targeted next-generation sequencing in profiles of diverse T-cell neoplasms and focused on the therapeutic utility of targeting activated JAK1 and JAK3 in an index case. Patients and Methods: Using Foundation One and Foundation One Heme assays, we performed genomic profiling on 91 consecutive T-cell neoplasms for alterations in 405 genes. The samples were sequenced to high uniform coverage with an Illumina HiSeq and averaged a coverage depth of greater than 500× for DNA and more than 8M total pairs for RNA. An index case of T-cell prolymphocytic leukemia (T-PLL), which was analyzed by targeted next-generation sequencing, is presented. T-PLL cells were analyzed by RNA-seq, in vitro drug testing, mass cytometry, and phospho-flow. Results: One third of the samples had genomic aberrations in the JAK-STAT pathway, most often composed of Conclusion: These results underscore the utility of profiling occurrences of resistance to standard regimens and support JAK enzymes as rational therapeutic targets for T-cell leukemias and lymphomas

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/133605/1/bjh14046.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/133605/2/bjh14046_am.pd