2GHz MIMO channel model from experimental outdoor data analysis in UMTS

The key objective of this work was to obtain a MIMO model for a line of sight (LOS) channel component as well as the covariance matrix for a non-LOS deployment. A maximum likelihood criteria is applied to obtain a LOS spatial signature vector and a NLOS covariance matrix derived from channel measurements taken in the 2 GHz UMTS spectrum for an urban deployment in Bristol (UK). Different user equipment deployments were considered to represent both LOS and NLOS, as well as static and dynamic (motion) situations. The parameters of interest were estimated from these data and the fitness model was satisfactorily evaluated in all cases. Further, the Kronecker product between transmitter and receiver matrices was evaluated in order to simplify the model, for both, LOS and NLOS cases, including polarization diversity cases.The key objective of this work was to obtain a MIMO model for a line of sight (LOS) channel component as well as the covariance matrix for a non-LOS deployment. A maximum likelihood criteria is applied to obtain a LOS spatial signature vector and a NLOS covariance matrix derived from channel measurements taken in the 2 GHz UMTS spectrum for an urban deployment in Bristol (UK). Different user equipment deployments were considered to represent both LOS and NLOS, as well as static and dynamic (motion) situations. The parameters of interest were estimated from these data and the fitness model was satisfactorily evaluated in all cases. Further, the Kronecker product between transmitter and receiver matrices was evaluated in order to simplify the model, for both, LOS and NLOS cases, including polarization diversity cases

Predation on invasive cane toads (Rhinella marina) by native Australian rodents

© 2014, Springer-Verlag Berlin Heidelberg. The success of an invasive species can be reduced by biotic resistance from the native fauna. For example, an invader that is eaten by native predators is less likely to thrive than one that is invulnerable. The ability of invasive cane toads (Rhinella marina) to spread through Australia has been attributed to the toad’s potent defensive chemicals that can be fatal if ingested by native snakes, lizards, marsupials and crocodiles. However, several taxa of native insects and birds are resistant to cane toad toxins. If native rodents are also capable of eating toads (as suggested by anecdotal reports), these large, abundant and voracious predators might reduce toad numbers. Our field observations and laboratory trials confirm that native rodents (Melomys burtoni, Rattus colletti and Rattus tunneyi) readily kill and consume cane toads (especially small toads), and are not overtly affected by toad toxins. Captive rodents did not decrease their consumption of toads over successive trials, and ate toads even when alternative food types were available. In combination with anecdotal reports, our data suggest that rodents (both native and invasive) are predators of cane toads in Australia. Despite concerns about the decline of rodents following the invasion of toads, our data suggest that the species we studied are not threatened by toads as toxic prey, and no specific conservation actions are required to ensure their persistence

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact

Enzymatic degradation ofRNAcauses widespread protein aggregation in cell and tissue lysates

Most proteins in cell and tissue lysates are soluble. We show here that in lysate from human neurons, more than 1,300 proteins are maintained in a soluble and functional state by association with endogenous RNA, as degradation of RNA invariably leads to protein aggregation. The majority of these proteins lack conventional RNA‐binding domains. Using synthetic oligonucleotides, we identify the importance of nucleic acid structure, with single‐stranded pyrimidine‐rich bulges or loops surrounded by double‐stranded regions being particularly efficient in the maintenance of protein solubility. These experiments also identify an apparent one‐to‐one protein‐nucleic acid stoichiometry. Furthermore, we show that protein aggregates isolated from brain tissue from Amyotrophic Lateral Sclerosis patients can be rendered soluble after refolding by both RNA and synthetic oligonucleotides. Together, these findings open new avenues for understanding the mechanism behind protein aggregation and shed light on how certain proteins remain soluble

Design Patterns for Efficient Solutions to NP-Complete Problems in Membrane Computing

Many variants of P systems have the ability to generate an exponential number of membranes in linear time. This feature has been exploited to elaborate (theoretical) efficient solutions to NP-complete, or even harder, problems. A thorough review of the existent solutions shows the utilization of common techniques and procedures. The abstraction of the latter into design patterns can serve to ease and accelerate the construction of efficient solutions to new hard problems.Ministerio de Economía y Competitividad TIN2017-89842-

Counting Membrane Systems

A decision problem is one that has a yes/no answer, while a counting problem asks how many possible solutions exist associated with each instance. Every decision problem X has associated a counting problem, denoted by #X, in a natural way by replacing the question “is there a solution?” with “how many solutions are there?”. Counting problems are very attractive from a computational complexity point of view: if X is an NP-complete problem then the counting version #X is NP-hard, but the counting version of some problems in class P can also be NP-hard. In this paper, a new class of membrane systems is presented in order to provide a natural framework to solve counting problems. The class is inspired by a special kind of non-deterministic Turing machines, called counting Turing machines, introduced by L. Valiant. A polynomial-time and uniform solution to the counting version of the SAT problem (a well-known #P-complete problem) is also provided, by using a family of counting polarizationless P systems with active membranes, without dissolution rules and division rules for non-elementary membranes but where only very restrictive cooperation (minimal cooperation and minimal production) in object evolution rules is allowed

The effect of incorrect scanning distance on boundary detection errors and macular thickness measurements by spectral domain optical coherence tomography: a cross sectional study

BACKGROUND: To investigate the influence of scan distance on retinal boundary detection errors (RBDEs) and retinal thickness measurements by spectral domain optical coherence tomography (SD-OCT). METHODS: 10 eyes of healthy subjects, 10 eyes with diabetic macular edema (DME) and 10 eyes with neovascular age-related macular degeneration (AMD) were examined with RTVue SD-OCT. The MM5 protocol was used in two consecutive sessions to scan the macula. For the first session, the device was set 3.5 cm from the eye in order to obtain detectable signal with low fundus image quality (suboptimal setting) while in the second session a distance of 2.5 cm was set with a good quality fundus image. The signal strength (SSI) value was recorded. The score for retinal boundary detection errors (RBDE) was calculated for ten scans of each examination. RBDE scores were recorded for the whole scan and also for the peripheral 1.0 mm region. RBDE scores, regional retinal thickness values and SSI values between the two sessions were compared. The correlation between SSI and the number of RBDEs was also examined. RESULTS: The SSI was significantly lower with suboptimal settings compared to optimal settings (63.9+/-12.0 vs. 68.3+/-12.2, respectively, p = 0.001) and the number of RBDEs was significantly higher with suboptimal settings in the "all-eyes" group along with the group of healthy subjects and eyes with DME (9.1+/-6.5 vs. 6.8+/-6.3, p = 0.007; 4.4+/-2.6 vs. 2.5+/-1.6, p = 0.035 and 9.7+/-3.3 vs. 5.1+/-3.7, p = 0.008, respectively). For these groups, significant negative correlation was found between the SSI and the number of RBDEs. In the AMD group, the number of RBDEs was markedly higher compared to the other groups and there was no difference in RBDEs between optimal and suboptimal settings with the errors being independent of the SSI. There were significantly less peripheral RBDEs with optimal settings in the "all-eyes" group and the DME subgroup (2.7+/-2.6 vs. 4.2+/-2.8, p = 0.001 and 1.4+/-1.7 vs. 4.1+/-2.2, p = 0.007, respectively). Retinal thickness in the two settings was significantly different only in the outer-superior region in DME. CONCLUSIONS: Optimal distance settings improve SD-OCT SSI with a decrease in RBDEs while retinal thickness measurements are independent of scanning distance

miR-21 Promotes Fibrogenesis in Peritoneal Dialysis.

Peritoneal dialysis (PD) is a life-saving form of renal replacement therapy for those with end-stage kidney disease. Mesothelial cells (MCs) line the peritoneal cavity and help define peritoneal response to treatment-associated injury, a major reason for treatment failure. miRNAs are important regulators, but their roles in peritoneal fibrosis are largely unknown. In this study, miR-21 was one of the most abundant miRNAs in primary MCs, and was up-regulated by the profibrotic cytokine transforming growth factor-β1 and in PD effluent-derived MCs exhibiting mesenchymal phenotypic change. Increased miR-21 was found in peritoneal membrane biopsy specimens from PD patients compared to healthy controls (PD biocompatible, 5.86×, P = 0.0001; PD conventional, 7.09×, P < 0.0001, n = 11 per group). In PD effluent from a cohort of 230 patients, miR-21 was higher in those receiving the therapy long-term compared to new starters (n = 230, miR-21 3.26×, P = 0.001) and associated with icodextrin use (R = 0.52; 95% CI, 0.20-0.84), peritonitis count (R = 0.16; 95% CI, 0.03-0.29), and dialysate cytokines. miR-21 down-regulated programmed cell death 4 and programmed cell death 4 protein was decreased in peritoneal membrane biopsy specimens from PD patients compared to healthy controls. New miR-21 targets were identified that may be important during PD fibrogenesis. These data identify miR-21 as an important effector of fibrosis in the peritoneal membrane, and a promising biomarker in the dialysis effluent for membrane change in patients receiving PD

Over 1000 genetic loci influencing blood pressure with multiple systems and tissues implicated.

High blood pressure (BP) remains the major heritable and modifiable risk factor for cardiovascular disease. Persistent high BP, or hypertension, is a complex trait with both genetic and environmental interactions. Despite swift advances in genomics, translating new discoveries to further our understanding of the underlying molecular mechanisms remains a challenge. More than 500 loci implicated in the regulation of BP have been revealed by genome-wide association studies (GWAS) in 2018 alone, taking the total number of BP genetic loci to over 1000. Even with the large number of loci now associated to BP, the genetic variance explained by all loci together remains low (~5.7%). These genetic associations have elucidated mechanisms and pathways regulating BP, highlighting potential new therapeutic and drug repurposing targets. A large proportion of the BP loci were discovered and reported simultaneously by multiple research groups, creating a knowledge gap, where the reported loci to date have not been investigated in a harmonious way. Here, we review the BP-associated genetic variants reported across GWAS studies and investigate their potential impact on the biological systems using in silico enrichment analyses for pathways, tissues, gene ontology and genetic pleiotropy