Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning

The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in New York, the expected rise in the EV population requires additional charging stations to maintain a sufficient level of efficiency across the charging infrastructure. A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations given the predicted charging demand and current charging locations. The most important factors that influence charging demand prediction include the conterminous traffic density, EV registrations, and proximity to certain types of public buildings. The proposed RL framework can be refined and applied to cities across the world to optimize charging station placement.Comment: 25 pages with 12 figures. Shankar Padmanabhan and Aidan Petratos provided equal contributio

Directional Microwave Emission from Femtosecond-laser Illuminated Linear Arrays of Superconducting Rings

We examine the electromagnetic emission from two photo-illuminated linear arrays composed of inductively charged superconducting ring elements. The arrays are illuminated by an ultrafast infrared laser that triggers microwave broadband emission detected in the 1–26 GHz range. Based on constructive interference from the arrays a narrowing of the forward radiation lobe is observed with increasing element count and frequency demonstrating directed GHz emission. Results suggest that higher frequencies and a larger number of elements are achievable leading to a unique pulsed array emitter concept that can span frequencies from the microwave to the terahertz (THz) regime

Improving 10-year cardiovascular risk prediction in apparently healthy people : flexible addition of risk modifiers on top of SCORE2

AIMS: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics. METHODS AND RESULTS: Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)]. CONCLUSION: The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers

C2 and CFB Genes in Age-Related Maculopathy and Joint Action with CFH and LOC387715 Genes

Background: Age-related maculopathy (ARM) is a common cause of visual impairment in the elderly populations of industrialized countries and significantly affects the quality of life of those suffering from the disease. Variants within two genes, the complement factor H (CFH) and the poorly characterized LOC387715 (ARMS2), are widely recognized as ARM risk factors. CFH is important in regulation of the alternative complement pathway suggesting this pathway is involved in ARM pathogenesis. Two other complement pathway genes, the closely linked complement component receptor (C2) and complement factor B (CFB), were recently shown to harbor variants associated with ARM. Methods/Principal Findings: We investigated two SNPs in C2 and two in CFB in independent case-control and family cohorts of white subjects and found rs547154, an intronic SNP in C2, to be significantly associated with ARM in both our case-control (P-value 0.00007) and family data (P-value 0.00001). Logistic regression analysis suggested that accounting for the effect at this locus significantly (P-value 0.002) improves the fit of a genetic risk model of CFH and LOC387715 effects only. Modeling with the generalized multifactor dimensionality reduction method showed that adding C2 to the two-factor model of CFH and LOC387715 increases the sensitivity (from 63% to 73%). However, the balanced accuracy increases only from 71% to 72%, and the specificity decreases from 80% to 72%. Conclusions/Significance: C2/CFB significantly influences AMD susceptibility and although accounting for effects at this locus does not dramatically increase the overall accuracy of the genetic risk model, the improvement over the CFH-LOC387715 model is statistically significant. © 2008 Jakobsdottir et al

Atypical Haemolytic Uraemic Syndrome Associated with a Hybrid Complement Gene

BACKGROUND: Sequence analysis of the regulators of complement activation (RCA) cluster of genes at chromosome position 1q32 shows evidence of several large genomic duplications. These duplications have resulted in a high degree of sequence identity between the gene for factor H (CFH) and the genes for the five factor H-related proteins (CFHL1–5; aliases CFHR1–5). CFH mutations have been described in association with atypical haemolytic uraemic syndrome (aHUS). The majority of the mutations are missense changes that cluster in the C-terminal region and impair the ability of factor H to regulate surface-bound C3b. Some have arisen as a result of gene conversion between CFH and CFHL1. In this study we tested the hypothesis that nonallelic homologous recombination between low-copy repeats in the RCA cluster could result in the formation of a hybrid CFH/CFHL1 gene that predisposes to the development of aHUS. METHODS AND FINDINGS: In a family with many cases of aHUS that segregate with the RCA cluster we used cDNA analysis, gene sequencing, and Southern blotting to show that affected individuals carry a heterozygous CFH/CFHL1 hybrid gene in which exons 1–21 are derived from CFH and exons 22/23 from CFHL1. This hybrid encodes a protein product identical to a functionally significant CFH mutant (c.3572C>T, S1191L and c.3590T>C, V1197A) that has been previously described in association with aHUS. CONCLUSIONS: CFH mutation screening is recommended in all aHUS patients prior to renal transplantation because of the high risk of disease recurrence post-transplant in those known to have a CFH mutation. Because of our finding it will be necessary to implement additional screening strategies that will detect a hybrid CFH/CFHL1 gene

Induction of plasminogen activator inhibitor type-1 (PAI-1) by hypoxia and irradiation in human head and neck carcinoma cell lines

Contains fulltext : 53187.pdf ( ) (Open Access)BACKGROUND: Squamous cell carcinoma of the head and neck (SCCHN) often contain highly radioresistant hypoxic regions, nonetheless, radiotherapy is a common treatment modality for these tumours. Reoxygenation during fractionated radiotherapy is desired to render these hypoxic tumour regions more radiosensitive. Hypoxia additionally leads to up-regulation of PAI-1, a protein involved in tumour progression and an established prognostic marker for poor outcome. However, the impact of reoxygenation and radiation on PAI-1 levels is not yet clear. Therefore, we investigated the kinetics of PAI-1 expression and secretion after hypoxia and reoxygenation, and determined the influence of ionizing radiation on PAI-1 levels in the two human SCCHN cell lines, BHY and FaDu. METHODS: HIF-1alpha immunoblot was used to visualize the degree of hypoxia in the two cell lines. Cellular PAI-1 expression was investigated by immunofluorescence microscopy. ELISA was used to quantify relative changes in PAI-1 expression (cell lysates) and secretion (cell culture supernatants) in response to various lengths (2-4 h) of hypoxic exposure (< 0.66% O2), reoxygenation (24 h, 20% O2), and radiation (0, 2, 5 and 10 Gy). RESULTS: HIF-1alpha expression was induced between 2 and 24 h of hypoxic exposure. Intracellular PAI-1 expression was significantly increased in BHY and FaDu cells as early as 4 h after hypoxic exposure. A significant induction in secreted PAI-1 was seen after 12 to 24 h (BHY) and 8 to 24 h (FaDu) hypoxia, as compared to the normoxic control. A 24 h reoxygenation period caused significantly less PAI-1 secretion than a 24 h hypoxia period in FaDu cells. Irradiation led to an up-regulation of PAI-1 expression and secretion in both, BHY and FaDu cells. CONCLUSION: Our data suggest that both, short-term (approximately 4-8 h) and long-term (approximately 20-24 h) hypoxic exposure could increase PAI-1 levels in SCCHN in vivo. Importantly, radiation itself could lead to PAI-1 up-regulation in head and neck tumours, whereas reoxygenation of hypoxic tumour cells during fractionated radiotherapy could counteract the increased PAI-1 levels

Using genetic variation and environmental risk factor data to identify individuals at high risk for age-related macular degeneration

A major goal of personalized medicine is to pre-symptomatically identify individuals at high risk for disease using knowledge of each individual's particular genetic profile and constellation of environmental risk factors. With the identification of several well-replicated risk factors for age-related macular degeneration (AMD), the leading cause of legal blindness in older adults, this previously unreachable goal is beginning to seem less elusive. However, recently developed algorithms have either been much less accurate than expected, given the strong effects of the identified risk factors, or have not been applied to independent datasets, leaving unknown how well they would perform in the population at large. We sought to increase accuracy by using novel modeling strategies, including multifactor dimensionality reduction (MDR) and grammatical evolution of neural networks (GENN), in addition to the traditional logistic regression approach. Furthermore, we rigorously designed and tested our models in three distinct datasets: a Vanderbilt-Miami (VM) clinic-based case-control dataset, a VM family dataset, and the population-based Age-related Maculopathy Ancillary (ARMA) Study cohort. Using a consensus approach to combine the results from logistic regression and GENN models, our algorithm was successful in differentiating between high- and low-risk groups (sensitivity 77.0%, specificity 74.1%). In the ARMA cohort, the positive and negative predictive values were 63.3% and 70.7%, respectively. We expect that future efforts to refine this algorithm by increasing the sample size available for model building, including novel susceptibility factors as they are discovered, and by calibrating the model for diverse populations will improve accuracy

The NEI/NCBI dbGAP database: Genotypes and haplotypes that may specifically predispose to risk of neovascular age-related macular degeneration

<p>Abstract</p> <p>Background</p> <p>To examine if the significantly associated SNPs derived from the genome wide allelic association study on the AREDS cohort at the NEI (dbGAP) specifically confer risk for neovascular age-related macular degeneration (AMD). We ascertained 134 unrelated patients with AMD who had one sibling with an AREDS classification 1 or less and was past the age at which the affected sibling was diagnosed (268 subjects). Genotyping was performed by both direct sequencing and Sequenom iPLEX system technology. Single SNP analyses were conducted with McNemar's Test (both 2 × 2 and 3 × 3 tests) and likelihood ratio tests (LRT). Conditional logistic regression was used to determine significant gene-gene interactions. LRT was used to determine the best fit for each genotypic model tested (additive, dominant or recessive).</p> <p>Results</p> <p>Before release of individual data, <it>p</it>-value information was obtained directly from the AREDS dbGAP website. Of the 35 variants with <it>P </it>< 10^-6examined, 23 significantly modified risk of neovascular AMD. Many variants located in tandem on 1q32-q22 including those in <it>CFH</it>, <it>CFHR4</it>, <it>CFHR2</it>, <it>CFHR5</it>, <it>F13B</it>, <it>ASPM </it>and <it>ZBTB </it>were significantly associated with AMD risk. Of these variants, single SNP analysis revealed that <it>CFH </it>rs572515 was the most significantly associated with AMD risk (P < 10^-6). Haplotype analysis supported our findings of single SNP association, demonstrating that the most significant haplotype, GATAGTTCTC, spanning <it>CFH</it>, <it>CFHR4</it>, and <it>CFHR2 </it>was associated with the greatest risk of developing neovascular AMD (<it>P </it>< 10^-6). Other than variants on 1q32-q22, only two SNPs, rs9288410 (<it>MAP2</it>) on 2q34-q35 and rs2014307 (<it>PLEKHA1</it>/<it>HTRA1</it>) on 10q26 were significantly associated with AMD status (<it>P </it>= .03 and <it>P </it>< 10^-6respectively). After controlling for smoking history, gender and age, the most significant gene-gene interaction appears to be between rs10801575 (<it>CFH</it>) and rs2014307 (<it>PLEKHA1</it>/<it>HTRA1</it>) (<it>P </it>< 10^-11). The best genotypic fit for rs10801575 and rs2014307 was an additive model based on LRT. After applying a Bonferonni correction, no other significant interactions were identified between any other SNPs.</p> <p>Conclusion</p> <p>This is the first replication study on the NEI dbGAP SNPs, demonstrating that alleles on 1q, 2q and 10q may predispose an individual to AMD.</p

Polymorphisms in PTK2 are associated with skeletal muscle specific force: an independent replication study

Purpose The aim of the study was to investigate two single nucleotide polymorphisms (SNP) in PTK2 for associations with human muscle strength phenotypes in healthy men. Methods Measurement of maximal isometric voluntary knee extension (MVCKE) torque, net MVCKE torque and vastus lateralis (VL) specific force, using established techniques, was completed on 120 Caucasian men (age = 20.6 ± 2.3 year; height = 1.79 ± 0.06 m; mass = 75.0 ± 10.0 kg; mean ± SD). All participants provided either a blood (n = 96) or buccal cell sample, from which DNA was isolated and genotyped for the PTK2 rs7843014 A/C and rs7460 A/T SNPs using real-time polymerase chain reaction. Results Genotype frequencies for both SNPs were in Hardy–Weinberg equilibrium (X 2 ≤ 1.661, P ≥ 0.436). VL specific force was 8.3% higher in rs7843014 AA homozygotes than C-allele carriers (P = 0.017) and 5.4% higher in rs7460 AA homozygotes than T-allele carriers (P = 0.029). No associations between either SNP and net MVCKE torque (P ≥ 0.094) or peak MVCKE torque (P ≥ 0.107) were observed. Conclusions These findings identify a genetic contribution to the inter-individual variability within muscle specific force and provides the first independent replication, in a larger Caucasian cohort, of an association between these PTK2 SNPs and muscle specific force, thus extending our understanding of the influence of genetic variation on the intrinsic strength of muscle.Published versio