Infering Air Quality from Traffic Data using Transferable Neural Network Models

This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The fallacy of enrolling only high-risk subjects in cancer prevention trials: Is there a "free lunch"?

BACKGROUND: There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions. METHODS: We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects. RESULTS: Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population. CONCLUSION: Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population

Non-Abelian statistics and topological quantum information processing in 1D wire networks

Topological quantum computation provides an elegant way around decoherence, as one encodes quantum information in a non-local fashion that the environment finds difficult to corrupt. Here we establish that one of the key operations---braiding of non-Abelian anyons---can be implemented in one-dimensional semiconductor wire networks. Previous work [Lutchyn et al., arXiv:1002.4033 and Oreg et al., arXiv:1003.1145] provided a recipe for driving semiconducting wires into a topological phase supporting long-sought particles known as Majorana fermions that can store topologically protected quantum information. Majorana fermions in this setting can be transported, created, and fused by applying locally tunable gates to the wire. More importantly, we show that networks of such wires allow braiding of Majorana fermions and that they exhibit non-Abelian statistics like vortices in a p+ip superconductor. We propose experimental setups that enable the Majorana fusion rules to be probed, along with networks that allow for efficient exchange of arbitrary numbers of Majorana fermions. This work paves a new path forward in topological quantum computation that benefits from physical transparency and experimental realism.Comment: 6 pages + 17 pages of Supp. Mat.; 10 figures. Supp. Mat. has doubled in size to establish results more rigorously; many other improvements as wel

Safety, tumor trafficking and immunogenicity of chimeric antigen receptor (CAR)-T cells specific for TAG-72 in colorectal cancer.

BackgroundT cells engineered to express chimeric antigen receptors (CARs) have established efficacy in the treatment of B-cell malignancies, but their relevance in solid tumors remains undefined. Here we report results of the first human trials of CAR-T cells in the treatment of solid tumors performed in the 1990s.MethodsPatients with metastatic colorectal cancer (CRC) were treated in two phase 1 trials with first-generation retroviral transduced CAR-T cells targeting tumor-associated glycoprotein (TAG)-72 and including a CD3-zeta intracellular signaling domain (CART72 cells). In trial C-9701 and C-9702, CART72 cells were administered in escalating doses up to 1010 total cells; in trial C-9701 CART72 cells were administered by intravenous infusion. In trial C-9702, CART72 cells were administered via direct hepatic artery infusion in patients with colorectal liver metastases. In both trials, a brief course of interferon-alpha (IFN-α) was given with each CART72 infusion to upregulate expression of TAG-72.ResultsFourteen patients were enrolled in C-9701 and nine in C-9702. CART72 manufacturing success rate was 100% with an average transduction efficiency of 38%. Ten patients were treated in CC-9701 and 6 in CC-9702. Symptoms consistent with low-grade, cytokine release syndrome were observed in both trials without clear evidence of on target/off tumor toxicity. Detectable, but mostly short-term (≤14 weeks), persistence of CART72 cells was observed in blood; one patient had CART72 cells detectable at 48 weeks. Trafficking to tumor tissues was confirmed in a tumor biopsy from one of three patients. A subset of patients had 111Indium-labeled CART72 cells injected, and trafficking could be detected to liver, but T cells appeared largely excluded from large metastatic deposits. Tumor biomarkers carcinoembryonic antigen (CEA) and TAG-72 were measured in serum; there was a precipitous decline of TAG-72, but not CEA, in some patients due to induction of an interfering antibody to the TAG-72 binding domain of humanized CC49, reflecting an anti-CAR immune response. No radiologic tumor responses were observed.ConclusionThese findings demonstrate the relative safety of CART72 cells. The limited persistence supports the incorporation of co-stimulatory domains in the CAR design and the use of fully human CAR constructs to mitigate immunogenicity

Alcohol Production as an Adaptive Livelihood Strategy for Women Farmers in Tanzania and Its Potential for Unintended Consequences on Women's Reproductive Health.

Although women occupy a central position in agriculture in many developing countries, they face numerous constraints to achieving their full potential including unequal access to assets and limited decision-making authority. We explore the intersection of agricultural livelihoods, food and economic security, and women's sexual and reproductive health in Iringa Region, Tanzania. Our goal was to understand whether the benefits of supporting women in the agricultural sector might also extend to more distal outcomes, including sexual and reproductive health. Using the Sustainable Livelihoods Framework to guide data collection, we conducted 13 focus group discussions (FGD) with female (n = 11) and male farmers (n = 2) and 20 in-depth interviews with agricultural extension officers (n = 10) and village agro-dealers (n = 10). Despite providing the majority of agricultural labor, women have limited control over land and earned income and have little bargaining power. In response to these constraints, women adopt adaptive livelihood strategies, such as alcohol production, that allow them to retain control over income and support their households. However, women's central role in alcohol production, in concert with the ubiquitous nature of alcohol consumption, places them at risk by enhancing their vulnerability to unsafe or transactional sex. This represents a dangerous confluence of risk for female farmers, in which alcohol plays an important role in income generation and also facilitates high-risk sexual behavior. Alcohol production and consumption has the potential to both directly and indirectly place women at risk for undesirable sexual and reproductive health outcomes. Group formation, better access to finance, and engaging with agricultural extension officers were identified as potential interventions for supporting women farmers and challenging harmful gender norms. In addition, joint, multi-sectoral approaches from health and agriculture and alternative income-generating strategies for women might better address the complexities of achieving safe and sustainable livelihoods for women in this context

Concurrent validity of self-rating scale of self-directed learning and self-directed learning instrument among Italian nursing students

BACKGROUND: Self-Directed Learning develops when students take the initiative for their learning, recognising needs, formulating goals, identifying resources, implementing appropriate strategies and evaluating learning outcomes. This should be seen as a collaborative process between the nurse educator and the learner. At the international level, various instruments have been used to measure Self-Directed Learning abilities (SDL), both in original and in culturally-adapted versions. However, few instruments have been subjected to full validation, and no gold standard reference has been established to date. In addition, few researchers have adopted the established tools to assess the concurrent validity of the emerging new tools. Therefore, the aim of this study was to measure the concurrent validity between the Self-Rating Scale of Self-Directed Learning (SRSSDL_Ita) - Italian version and the Self-Directed Learning Instruments (SDLI) in undergraduate nursing students. METHODS: A concurrent validity study design was conducted in a Bachelor level nursing degree programme located in Italy. All nursing students attending the first, second or third year (n=428) were the target sample. The SRSSDL_Ita, and the SDLI were used. The Pearson correlation was used to determine the concurrent validity between the instruments; the confidence of intervals (CI 95%) bias-corrected and accelerated bootstrap (BCa), were also calculated. RESULTS: The majority of participants were students attending their first year (47.9%), and were predominately female (78.5%). Their average age was 22.5\ub14.1. The SDL abilities scores, as measured with the SRSSDL_Ita (min 40, max 200), were, on average, 160.79 (95% CI 159.10-162.57; median 160); while with the SDLI (min 20, max 100), they were on average 82.57 (95% CI 81.79-83.38; median 83). The Pearson correlation between the SRSSDL_Ita and SDLI instruments was 0.815 (CI BCa 95% 0.774-0.848), (p=0.000). CONCLUSIONS: The findings confirm the concurrent validity of the SRSSDL_Ita with the SDLI. The SRSSDL_Ita instrument can be useful in the process of identifying Self-Directed Learning abilities, which are essential for students to achieve the expected learning goals and become lifelong learners

A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait

The occurrence of invasive cancers following a diagnosis of breast carcinoma in situ

Approximately 1 in every 600 women attending breast-screening programmes in the United Kingdom is diagnosed with breast carcinoma in situ (BCIS). However, there is little information on the occurrence of subsequent cancers (other than second breast cancers) in these women. We investigated the occurrence of invasive cancers in 12 836 women diagnosed with BCIS in southeast England between 1971 and 2003, using data from the Thames Cancer Registry. A greater than expected number of subsequent cancers was found for two sites: breast (standardised incidence ratio (SIR) 1.96; 95% confidence interval (CI) 1.79–2.14) and corpus uteri (SIR 1.42; 95% CI 1.11–1.78). For subsequent ipsilateral breast cancer in those treated with breast conservation, the excess was independent of the time since diagnosis of BCIS, whereas for subsequent contralateral breast cancer, there was a steady decline in excess over time. For subsequent uterine cancer, the excess became statistically significant only at >5 years after BCIS diagnosis, consistent with a treatment effect. This was further supported by Cox regression anaysis: the risk of subsequent uterine cancer was significantly increased in women receiving hormonal therapy compared with those not receiving it, with a hazard ratio of 2.97 (95% CI 1.84–4.80)