Improved Learning Scheme for Cognitive Radio using Artificial Neural Networks

The future of wireless system is facing the problem of spectrum scarcity. Number of users is increasing rapidly but available spectrum is limited. The Cognitive Radio (CR) network technology can enable the unlicensed users to share the frequency spectrum with the licensed users on a dynamic basis without creating any interference to primary user. Whenever secondary user finds that primary user is not transmitting and channel is free then it uses channel opportunistically. In this paper cognitive radio with predictive capability using artificial neural network has been proposed. The advantage of such cognitive user is saving of time and energy for spectrum sensing. Proposed radio will sense only that channel which is predicted to be free and channel is selected on the basis of maximum vacant time. Performance has been evaluated in the term of mean square error. The results show that this learning capability can be embedded in secondary users for better performance of future wireless technologies. 

Area efficient parallel lfsr for cyclic redundancy check

Cyclic Redundancy Check (CRC), code for error detection finds many applications in the field of digital communication, data storage, control system and data compression. CRC encoding operation is carried out by using a Linear Feedback Shift Register (LFSR). Serial implementation of CRC requires more clock cycles which is equal to data message length plus generator polynomial degree but in parallel implementation of CRC one clock cycle is required if a whole data message is applied at a time. In previous work related to parallel LFSR, hardware complexity of the architecture reduced using a technique named state space transformation. This paper presents detailed explaination of search algorithm implementation and technique to find number of XOR gates required for different CRC algorithms. This paper presents a searching algorithm and new technique to find the number of XOR gates required for different CRC algorithms. The comparison between proposed and previous architectures shows that the number of XOR gates are reduced for CRC algorithms which improve the hardware efficiency. Searching algorithm and all the matrix computations have been performed using MATLAB simulations

Technique for Predicting Data Rate for Cognitive Radio Using Neural Network

Abstract Significant problems confronting wireless communications, are scarcity and deployment difficulty. The deployment problem is a process problem i.e., frequency allocation is fixed and is done so by complex collaboration and coordination between countries and systems, respectively. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically called cognitive radio technology. In this paper we have proposed a learning technique using neural network to be used in cognitive radio so that it can predict the data rate. Performance of learning scheme has been observed using Matlab simulations and found that Focused Time Delay Neural Networks are good candidate for cognitive radios

Congenital myasthenic syndrome with mild intellectual disability caused by a recurrent SLC25A1 variant

Abstract: Congenital myasthenic syndromes (CMS) are a clinically and genetically heterogeneous group of disorders caused by mutations which lead to impaired neuromuscular transmission. SLC25A1 encodes a mitochondrial citrate carrier, associated mainly with the severe neurometabolic disease combined D-2- and L-2-hydroxyglutaric aciduria (D/L-2-HGA). We previously reported a single family with a homozygous missense variant in SLC25A1 with a phenotype restricted to relatively mild CMS with intellectual disability, but to date no additional cases of this CMS subtype had been reported. Here, we performed whole exome sequencing (WES) in three additional and unrelated families presenting with CMS and mild intellectual disability to identify the underlying causative gene. The WES analysis revealed the presence of a homozygous c.740G>A; p.(Arg247Gln) missense SLC25A1 variant, the same SLC25A1 variant as identified in the original family with this phenotype. Electron microscopy of muscle from two cases revealed enlarged and accumulated mitochondria. Haplotype analysis performed in two unrelated families suggested that this variant is a result of recurrent mutation and not a founder effect. This suggests that p.(Arg247Gln) is associated with a relatively mild CMS phenotype with subtle mitochondrial abnormalities, while other variants in this gene cause more severe neurometabolic disease. In conclusion, the p.(Arg247Gln) SLC25A1 variant should be considered in patients presenting with a presynaptic CMS phenotype, particularly with accompanying intellectual disability

Biological Insights into the Expression of Translation Initiation Factors from Recombinant CHOK1SV Cell Lines and their Relationship to Enhanced Productivity

Translation initiation is on the critical pathway for the production of monoclonal antibodies (mAb) by mammalian cells. Formation of a closed loop structure comprised of mRNA, a number of eukaryotic initiation factors and ribosomal proteins has been proposed to aid re-initiation of translation and therefore increase global translational efficiency. We have determined mRNA and protein levels of the key components of the closed loop; eukaryotic initiation factors (eIF3a, eIF3b, eIF3c, eIF3h, eIF3i and eIF4G1), poly(A) binding protein (PABP) 1 and PABP interacting protein 1 (PAIP1) across a panel of 30 recombinant mAb-producing GS-CHOK1SV cell lines with a broad range of growth characteristics and production levels of a model recombinant mAb. We have used a multi-level statistical approach to investigate the relationship between key performance indicators (cell growth and recombinant antibody productivity) and the intracellular amounts of target translation initiation factor proteins and the mRNAs encoding them. We show that high-producing cell lines maintain amounts of the translation initiation factors involved in the formation of the closed loop mRNA, maintaining these proteins at appropriate levels to deliver enhanced recombinant protein production. We then utilise knowledge of the amounts of these factors to build predictive models for, and use cluster analysis to identify, high-producing cell lines. This study therefore defines the translation initiation factor amounts that are associated with highly productive recombinant GS-CHOK1SV cell lines that may be targets for screening highly productive cell lines or to engineer new host cell lines with the potential for enhanced recombinant antibody productivity

Innate and adaptive immunity in the development of depression: : An update on current knowledge and technological advances

The inflammation theory of depression, proposed over 20years ago, was influenced by early studies on T cell responses and since then has been a stimulus for numerous research projects aimed at understanding the relationship between immune function and depression. Observational studies have shown that indicators of immunity, especially C reactive protein and proinflammatory cytokines, such as interleukin 6, are associated with an increased risk of depressive disorders, although the evidence from randomized trials remains limited and only few studies have assessed the interplay between innate and adaptive immunity in depression. In this paper, we review current knowledge on the interactions between central and peripheral innate and adaptive immune molecules and the potential role of immune-related activation of microglia, inflammasomes and indoleamine-2,3-dioxygenase in the development of depressive symptoms. We highlight how combining basic immune methods with more advanced 'omics' technologies would help us to make progress in unravelling the complex associations between altered immune function and depressive disorders, in the identification of depression-specific biomarkers and in developing immunotherapeutic treatment strategies that take individual variability into account.Peer reviewe

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.Peer reviewe

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels. Methods We applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level. Findings In 2019, there were 12·2 million (95% UI 11·0–13·6) incident cases of stroke, 101 million (93·2–111) prevalent cases of stroke, 143 million (133–153) DALYs due to stroke, and 6·55 million (6·00–7·02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11·6% [10·8–12·2] of total deaths) and the third-leading cause of death and disability combined (5·7% [5·1–6·2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70·0% (67·0–73·0), prevalent strokes increased by 85·0% (83·0–88·0), deaths from stroke increased by 43·0% (31·0–55·0), and DALYs due to stroke increased by 32·0% (22·0–42·0). During the same period, age-standardised rates of stroke incidence decreased by 17·0% (15·0–18·0), mortality decreased by 36·0% (31·0–42·0), prevalence decreased by 6·0% (5·0–7·0), and DALYs decreased by 36·0% (31·0–42·0). However, among people younger than 70 years, prevalence rates increased by 22·0% (21·0–24·0) and incidence rates increased by 15·0% (12·0–18·0). In 2019, the age-standardised stroke-related mortality rate was 3·6 (3·5–3·8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3·7 (3·5–3·9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62·4% of all incident strokes in 2019 (7·63 million [6·57–8·96]), while intracerebral haemorrhage constituted 27·9% (3·41 million [2·97–3·91]) and subarachnoid haemorrhage constituted 9·7% (1·18 million [1·01–1·39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79·6 million [67·7–90·8] DALYs or 55·5% [48·2–62·0] of total stroke DALYs), high body-mass index (34·9 million [22·3–48·6] DALYs or 24·3% [15·7–33·2]), high fasting plasma glucose (28·9 million [19·8–41·5] DALYs or 20·2% [13·8–29·1]), ambient particulate matter pollution (28·7 million [23·4–33·4] DALYs or 20·1% [16·6–23·0]), and smoking (25·3 million [22·6–28·2] DALYs or 17·6% [16·4–19·0]). Interpretation The annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.publishedVersio