Colorectal Cancer Classification from Protein Sequences Using Several RNN Pre-Trained Models

Bioinformatics is one field that can integrate health information with data mining applications in order to predict or analyze the patient’s information. One among the several diseases is colorectal cancer,which is horrible and rated the third leading disease in the area of cancer which leads to death in both men and women.In general, there are several intelligent methods to identify several kinds of problems in gene selection for predicting cancer,but there is no method to give a solution for patients who are diagnosed in the advanced stage.This motivated me to design theproposed approach in which we try to identify all the homo protein sequences and then train these sequences corresponding to one which causes colorectal cancer.InBioinformatics,a protein acts as one of the main agent or source to perform a biological function by interacting with molecules like Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA). The function of a protein determines the healthy or diseased states of an organism. Protein interaction with other proteins can be visualized through a network called Protein-Protein Interaction Network (PPIN).In general classification of protein sequences is a very complex task. Deep learning techniques like CNN and RNN can be used to solve the problem.In computational bioinformatics,the classification of protein sequence plays an important role in determining accuracy.To improve the accuracy of our current model, the suggested method incorporates GRU, LSTM, RNN, and Customized LSTM into an RNN based architecture by optimizing the parameters in a two-way direction.Here we try to test all the models on sample protein sequences that are collected from TCGA and then determine the correctness of testing data and training data

Integer Compositions Using Simulated Annealing

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryAFOSR 88-0181U.S. Army Research Offic

A Characterization of the Smallest Eigenvalue of a Graph

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryAir Force Office of Scientific Research / AFOSR 88-018

Deep Reinforcement Learning Based Secure Transmission for UAV-Assisted Mobile Edge Computing

The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility

Parasitic Strip Loaded Dual Band Notch Circular Monopole Antenna with Defected Ground Structure

In this article a parasitic strip loaded monopole antennas are designed to notch dual and triple bands. The designed models are constructed on one side of the substrate material and on the other end defected ground structures are implemented. The basic antenna comprises a tuning stub and a ground plane with tapered shape slot as DGS. Another model is constructed with circular monopole radiating element on front side and similar kind of ground structure used in the basic rectangular tuning stub antenna. To create notched bands with tuning stubs, two symmetrical parasitic slits are placed inside the slot of the ground plane. The basic model is of the rectangular stub notching triple band and the circular tuning stub antenna notching dual band. Dual band notched circular tuning stub antenna is prototyped on FR4 substrate and measured results from vector network analyzer are compared with simulation results of HFSS for validation

Instruments of RT-2 Experiment onboard CORONAS-PHOTON and their test and evaluation V: Onboard software, Data Structure, Telemetry and Telecommand

The onboard software and data communication in the RT-2 Experiment onboard the Coronas-Photon satellite is organized in a hierarchical way to effectively handle and communicate asynchronous data generated by the X-ray detectors. A flexible data handling system is organized in the X-ray detector packages themselves and the processing electronic device, namely RT-2/E, has the necessary intelligence to communicate with the 3 scientific payloads by issuing commands and receiving data. It has direct interfacing with the Satellite systems and issues commands to the detectors and processes the detector data before sending to the satellite systems. The onboard software is configured with several novel features like a) device independent communication scheme, b) loss-less data compression and c) Digital Signal Processor. Functionality of the onboard software along with the data structure, command structure, complex processing scheme etc. are discussed in this paper.Comment: 24 pages, 2 figures, Accepted for publication in Experimental Astronomy (in press

Instruments of RT-2 Experiment onboard CORONAS-PHOTON and their test and evaluation II: RT-2/CZT payload

Cadmium Zinc Telluride (CZT) detectors are high sensitivity and high resolution devices for hard X-ray imaging and spectroscopic studies. The new series of CZT detector modules (OMS40G256) manufactured by Orbotech Medical Solutions (OMS), Israel, are used in the RT-2/CZT payload onboard the CORONAS-PHOTON satellite. The CZT detectors, sensitive in the energy range of 20 keV to 150 keV, are used to image solar flares in hard X-rays. Since these modules are essentially manufactured for commercial applications, we have carried out a series of comprehensive tests on these modules so that they can be confidently used in space-borne systems. These tests lead us to select the best three pieces of the 'Gold' modules for the RT-2/CZT payload. This paper presents the characterization of CZT modules and the criteria followed for selecting the ones for the RT-2/CZT payload. The RT-2/CZT payload carries, along with three CZT modules, a high spatial resolution CMOS detector for high resolution imaging of transient X-ray events. Therefore, we discuss the characterization of the CMOS detector as well.Comment: 26 pages, 19 figures, Accepted for publication in Experimental Astronomy (in press

Improvement of two traditional Basmati rice varieties for bacterial blight resistance and plant stature through morphological and marker-assisted selection

Bacterial blight (BB) is a major production threat to Basmati, the aromatic rice prized for its unique quality. In order to improve the BB resistance of two elite, traditional BB-susceptible Basmati varieties (Taraori Basmati and Basmati 386), we utilized the strategy of limited marker-assisted backcrossing for introgression of two major BB resistance genes, Xa21 and xa13, coupled with phenotype-based selection for improvement of their plant type and yield. Improved Samba Mahsuri, an elite high-yielding, fine-grain-type BB-resistant rice variety served as donor for BB resistance. Backcross-derived improved Basmati lines at BC1F5 possessing a single resistance gene (i.e. either Xa21 or xa13) displayed moderate resistance to BB, while lines possessing both Xa21 and xa13 showed significantly higher levels of resistance. Two-gene pyramid lines (Xa21 + xa13) possessing good grain and cooking quality similar to their respective traditional Basmati parents, short plant stature (<110 cm plant height) and higher grain yield than the recurrent parent(s) were identified and advanced. This work demonstrates the successful application of marker-assisted selection in conjunction with phenotype-based selection for targeted introgression of multiple resistance genes into traditional Basmati varieties along with improvement of their plant stature and yield

Pectin induced transcriptome of a Rhizoctonia solani strain causing sheath blight disease in rice reveals insights on key genes and RNAi machinery for development of pathogen derived resistance

Key message RNAi mediated silencing of pectin degrading enzyme of R. solani gives a high level of resistance against sheath blight disease of rice. Abstract Rice sheath blight disease caused by Rhizoctonia solani Kuhn (telemorph; Thanatephorus cucumeris) is one of the most devastating fungal diseases which cause severe loss to rice grain production. In the absence of resistant cultivars, the disease is currently managed through fungicides which add to environmental pollution. To explore the potential of utilizing RNA interference (RNAi)-mediated resistance against sheath blight disease, we identified genes encoding proteins and enzymes involved in the RNAi pathway in this fungal pathogen. The RNAi target genes were deciphered by RNAseq analysis of a highly virulent strain of the R. solani grown in pectin medium. Additionally, pectin metabolism associated genes of R. solani were analyzed through transcriptome sequencing of infected rice tissues obtained from six diverse rice cultivars. One of the key candidate gene AG1IA_04727 encoding polygalacturonase (PG), which was observed to be significantly upregulated during infection, was targeted through RNAi to develop disease resistance. Stable expression of PG-RNAi construct in rice showed efficient silencing of AG1IA_04727 and suppression of sheath blight disease. This study highlights important information about the existence of RNAi machinery and key genes of R. solani which can be targeted through RNAi to develop pathogen-derived resistance, thus opening an alternative strategy for developing sheath blight-resistant rice cultivars

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill &amp; Melinda Gates Foundation