BMI) and healthcare costs in relation to obesity‐related comorbidity and depression. A population‐based cohort study was undertaken in the UK Clinical Practice Research Datalink (CPRD). A stratified random sample was taken of participants registered with general practices in England in 2008 and 2013. Person time was classified by BMI category and morbidity status using first diagnosis of diabetes (T2DM), coronary heart disease (CHD), stroke or malignant neoplasms. Participants were classified annually as depressed or not depressed. Costs of healthcare utilization were calculated from primary care records with linked hospital episode statistics. A two‐part model estimated predicted mean annual costs by age, gender and morbidity status. Linear regression was used to estimate the effects of BMI category, comorbidity and depression on healthcare costs. The analysis included 873 809 person‐years  from 250 046 participants. Annual healthcare costs increased with BMI, to a mean of £456 (95% CI 344–568) higher for BMI ≥40 kg m−2 than for normal weight based on a general linear model. After adjusting for BMI, the additional cost of comorbidity was £1366 (£1269–£1463) and depression £1044 (£973–£1115). There was evidence of interaction so that as the BMI category increased, additional costs of comorbidity  or depression  were greater. High healthcare costs in obesity may be driven by the presence of comorbidity and depression. Prioritizing primary prevention of cardiovascular disease and diabetes in the obese population may contribute to reducing obesity‐related healthcare costs.The objective of this study was to evaluate the association between body mass index ( Obesity is associated with higher healthcare costs.It is less clear whether obesity, or obesity‐associated comorbidities and depression, are the main driver of healthcare costs.Few studies have been based on nationally representative data sources.This study reports data for a large English cohort, with 873 809 person‐years of follow‐up.Healthcare costs are greater as the BMI category increases, but comorbidity and depression are the greatest drivers of healthcare costs in obesity.Prioritizing primary prevention of cardiovascular disease and diabetes, as well as improving depression management in obesity, may contribute to reducing obesity‐related healthcare costs.Obesity is a growing global health concern, accounting for substantial national healthcare expenditures, with healthcare costs predicted to be higher by around a third in obese people compared to those of normal weight The aim of this paper is to investigate the association between body mass index (BMI) category and healthcare costs, focusing on the issue of whether BMI category, obesity‐related comorbidity and/or depression most strongly determines costs related to obesity. Answering this question may enable better informed efforts to increase the effectiveness and efficiency of the management of obese individuals. This analysis advances the literature on obesity‐related healthcare costs in three important ways. First, it utilizes data from a large and nationally representative database. Previous studies have relied on smaller https://www.cprd.com). The CPRD collects primary care electronic health records for 7% of the UK population and is considered representative of the UK population in terms of patient demographic characteristics and the size and composition of the general practices of reporting data We undertook a population‐based cohort study using the UK Clinical Practice Research Datalink (CPRD) . This left 250 046 participants for the analysis.A cohort of participants was drawn from the CPRD. Participants were eligible if they were registered with a CPRD general practice between 1 January 2008 and 31 December 2013. We evaluated eligible participants’ BMI records using the categories 18.5–24.9; 25.0–29.9; 30.0–34.9; 35.0–39.9; 40.0–44.9 and 45.0 kg m−2 into a single group to represent morbid obesity. Comorbidity status was evaluated using medical diagnoses coded into electronic health records during general practice consultations. Person time was classified according to morbidity status using the first diagnosis of type 2 diabetes (T2DM), coronary heart disease (CHD), stroke or cancer. Depression was re‐evaluated in each year of follow‐up, and patients were considered to have depression if they were diagnosed with depression during the year or if they were prescribed antidepressants in year and were ever diagnosed with depression. Morbidities, including T2DM Electronic health records were analysed from the latest of the date the practice joined CPRD or the patient's first registration to the earliest of the end of patient registration or date of death. Person time for men and women was allocated to a BMI category according to the most recent BMI value, combining BMI records >40 Kg mHealth care utilization was estimated from participants’ electronic health records with linked hospital episode statistics (HES) data. Primary and secondary care utilization was evaluated, including primary care consultations at the practice, by telephone, at home, emergency and out‐of‐hours. Secondary care utilization included admissions to hospital, out‐patient, day case and emergency visits. All drug prescriptions issued by the practice were evaluated. Utilization rates were calculated using person time at risk. Age‐standardized rates were estimated using direct standardization and the European Standard Population (ESP) for reference. The costs of healthcare utilization were evaluated for participants by morbidity and depression status within BMI categories for the period 2008–2013. Initially, the mean cost per person per year was calculated for each combination of BMI category and comorbidity status. The analysis was undertaken from a health service provider perspective  and did not consider indirect costs such as loss of productivity or patient and carer time.Unit costs were applied to the categories of healthcare utilization to estimate healthcare costs. The unit costs were all taken from reference sources based on UK£ 2013 price estimates. The Personal Social Service Research Unit (PSSRU) publication ‘Unit Costs of Health and Social Care (2013) was used as the reference for all healthcare costs A two‐part model The research represents part of a study approved by the Independent Scientific Advisory Committee (ISAC) of the Medicines and Healthcare products Regulatory Agency (MHRA) (ISAC Protocol No. 13–089).The cohort person‐years are presented in Table The two‐part model used to estimate healthcare costs is presented in Table The predicted annual healthcare costs in UK£ by BMI category, gender, comorbidity and depression status are presented in Table Table This study investigated healthcare costs in relation to BMI and may be the first study to isolate depression as a driver of costs separate from other common obesity‐related morbidities. Previous studies investigating healthcare costs relating to obesity using patient cohorts have attempted to estimate the marginal effect of obesity on costs There is a wide variation in cost estimates for obesity based on population sample studies, ranging from an additional €160 per year for severe obesity, compared to normal weight We found that having a comorbidity was the greatest predictor of increased healthcare costs at £1366 per year followed by clinical depression at £1044 per year. Of the four obesity‐related morbidities we assessed, stroke was found to be associated with the highest absolute costs, which is likely to result from acute secondary care required after a stroke. This finding is supported by a previous paper that identified cardiovascular diseases and cardiovascular agents as the biggest drivers of healthcare claims costs per unit of BMI We found that depression was the second greatest driver of costs, and its presence increased costs in all conditions, consistent with a previous investigation conducted into depression and healthcare utilization In T2DM, body weight did not appear to be associated with healthcare costs. This phenomenon has been reported elsewhere, and the possibility of greater morbidity in diabetics who are of normal weight was suggested as an explanatory factor This study has a number of strengths that make it a valuable addition to the literature on health care‐related costs of obesity. Firstly, it is based on a large, longitudinal, nationally representative dataset and uses costs associated directly with provision of care rather than based on insurance claims, which are an indirect measure of morbidity and may be less accurate. It is also one of few papers to use clinically measured height and weight values in the calculation of BMI rather than self‐reported values that are prone to reporting bias The economics literature has moved towards using an instrumental variables approach to examine the causal effect of obesity on medical costs by using the weight of a biological relative as an instrument The findings of this analysis emphasize that healthcare costs for obesity are largely driven by a few key obesity‐related morbidities and not by high BMI alone. Further research is now needed to elucidate the costly elements of these patients’ care and to develop clinical pathways to manage them more efficiently. Persistently high obesity rates and evidence from the literature www.icmje.org/coi_disclosure.pdf and declare no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work.All authors have completed the ICMJE uniform disclosure form at MG and ACR designed the study. JC and MG conducted the analysis. ACR and MG interpreted the results. MG, ACR and HB drafted the paper. All authors commented on and approved the manuscript.


It is important to study the neural connectivities and functions in primates. For this purpose, it is critical to be able to transfer genes to certain neurons in the primate brain so that we can image the neuronal signals and analyze the function of the transferred gene. Toward this end, our team has been developing gene transfer systems using viral vectors. In this review, we summarize our current achievements as follows. 1) We compared the features of gene transfer using five different AAV serotypes in combination with three different promoters, namely, CMV, mouse CaMKII (CaMKII), and human synapsin 1 (hSyn1), in the marmoset cortex with those in the mouse and macaque cortices. 2) We used target‐specific double‐infection techniques in combination with TET‐ON and TET‐OFF using lentiviral retrograde vectors for enhanced visualization of neural connections. 3) We used an AAV‐mediated gene transfer method to study the transcriptional control for amplifying fluorescent signals using the TET/TRE system in the primate neocortex. We also established systems for shRNA mediated gene targeting in a neocortical region where a gene is significantly expressed and for expressing the gene using the CMV promoter for an unexpressed neocortical area in the primate cortex using AAV vectors to understand the regulation of downstream genes. Our findings have demonstrated the feasibility of using viral vector mediated gene transfer systems for the study of primate cortical circuits using the marmoset as an animal model. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 354–372, 2017 The brain size is approximately proportional to the surface area of the neocortex with the same order of difference  to the killer whale on the order of 10The recent availability of genetically engineered primates means that methods involving viral vector mediated gene transfer have increasingly become important because they can be used in combination with other methods using genetically engineered transgene lines, which mutually enhances markedly their advantages , NeuN (neuron), and Hoechst33342 (nuclear staining) 3 and 6 weeks after the AAV injection. Cells infected CMV‐GFP showed a low signal intensity of GFP and almost no NeuN expression 6 weeks after infection and a high signal intensity of Hoechst33342, which suggest the degeneration of neurons and infiltration of reactive glia cells. No AAV toxicity was observed when the CaMKII and hSyn1 promoters were used. We, thus, recommend the use of these neuron‐specific promoters for long‐term experiments.Another important aspect to consider when selecting promoters is the cell‐type specificity of transgene expression. Consistent with the previous reports by Nathanson et al.  and ScheThere have been many studies on rodents and nonhuman primates in which different AAV serotypes have been tested  in primates. One pioneering work on GECI application to the macaque cortex provided images for months through a chronically implanted window , a derivative of tetracycline. After applying Dox for 5 days (Marmoset A) and 7 days (Marmoset B), a marked decrease of fluorescence signals was observed in 10 and 15 days, respectively. Controlling the expression level is thought to be important to prevent the adverse effects of GECIs on expression  at depths of 150, 275, and 400 μm. In an experiment, we identified 445 putative neuronal somata, 81 of which were spontaneously active .We applied the Thy1‐tTA and TRE‐GECI systems by double AAV infections in collaboration with Dr. Matsuzaki's group , which was highly expressed in the primary visual cortex  (mouse homolog of OCC1) is a Na‐K‐ATPase and controls afferent‐dependent synaptic transmission of sensory inputs to motor neurons  are low  are high  of the selective binding of a particular protein to the promoter region by PCR amplification analysis using a specific pair of primers after precipitating the complex with a specific antibody revealed the specific binding of MBD4 to the promoter regions of PNMA5, RBP4, and SLIT1. To determine whether MBD4 plays a critical role in controlling the expression of association‐area‐selective genes, we used a human neuroblastoma cell line (SH‐SY5Y). In SH‐SY5Y cells, the PNMA5 gene is highly methylated while MBD4 is poorly expressed. Demethylation by 5‐azacytidine diminished the binding of MBDs including MBD4, while. MBD4 overexpression enhanced MBD4 binding, as determined by ChIP assay, and increased PNMA5 expression levels, as determined by PCR assay. These assays using cultured neuroblastoma cells revealed that MBD4 does indeed control PNMA5 gene expression by binding to the methylated site of the PNMA5 promoter.Although the molecular features of genes of each group show no apparent correlation, their expression patterns are very similar. This observation raised the possibility that there may be a common mechanism that controls the expression of these genes. To confirm this possibility, we examined the methylation of CpG islands in the promoter region of the area‐selective genes  RNA against the For the application of the AAV vector to the marmoset cortex, we compared AAV serotypes in combination with one of three different promoters of CMV, CaMKII, and hSyn1 for the gene transfer experiments on the cortices of mice, marmosets, and macaque monkeys. Each combination gives different results; therefore, the best combination should be chosen depending on the purpose of the experiment.For the visualization of neuronal connections, we have used target‐specific double infection techniques in combination with TET‐ON/TET‐OFF systems. This set of systems works well in mice. Although the application of these techniques to the marmoset brain are still limited, they can be used for visualizing the primate neuron connections when combined with infection using TVA‐expressing systems (Callaway and Luo, We were able to observe the subcellular morphology and activity in the marmoset brain by two‐photon microscopy using the Thy1S‐tTA and Tre‐GFP/Tre‐GCaMP6f systems. Promoter activity, sensitivity of Ca indicator sensitivity, and two‐photon microscopy resolution are critically important factors and should be continuously improved in future studies.We also established an AAV‐mediated gene transfer method as a tool for the study of gene regulation in the primate neocortex by targeting the expression of a particular gene using either a loss‐ or gain‐of‐function strategy. This is particularly important as models of germline‐transmitted gene manipulation become available in primates (Sasaki et al., We have reviewed our recent studies focusing on the application of viral vectors to the study of neuronal connections and gene functions in the marmoset cortex. Toward this goal, we have developed and modified several techniques so that we can use the viral vectors for the study of neuronal circuits in the marmoset brain, which includes the following methods that we described in this review.The major concern is the size limitation for insertion in AAV vectors. The size of the gene to be inserted is limited to approximately 4.8 kb (Dong et al., The above problem that DNAs larger than 4.8 kb cannot be integrated also causes the related problem of promoter specificity. As in the case of the three different promoters of CMV, CAMKII, and hSyn, none of them showed a complete specificity for a cell‐type although each promoter tended to show variability in specificity. As discussed above, it is not easy to overcome this size limitation. However, there are a few examples of studies using shorter promoter regions that maintain at least reasonable specificity (Kawashima et al., AAV vectors are less toxic than other viral vectors (Nassi et al., Our studies clearly demonstrated the feasibility and usefulness of viral vector‐mediated gene transfer and manipulation systems. We have used AAV vectors, which seem to be the most appropriate except for retrograde tracing. However, there are still some concerns with using AAV vectors, which need to be improved in future studies as follows.The authors thank all the collaborators cited in the text.


Eudyptes chrysolophus), and numerically assessed the relative importance of bottom‐up and top‐down drivers acting through different demographic rates. The population trajectory was considerably more sensitive to changes in top‐down control of survival compared to bottom‐up control of survival or productivity. This study integrates a unique set of demographic and covariate data and highlights the benefits of using a single estimation framework to examine the links between covariates, demographic rates and population dynamics.In the open ocean ecosystem, climate and anthropogenic changes have driven biological change at both ends of the food chain. Understanding how the population dynamics of pelagic predators are simultaneously influenced by nutrient‐driven processes acting from the “bottom‐up” and predator‐driven processes acting from the “top‐down” is therefore considered an urgent task. Using a state‐space demographic model, we evaluated the population trajectory of an oceanic predator, the Macaroni Penguin ( The predator‐driven or “top‐down” view of population control appears to be widely accepted by researchers considering terrestrial , supporting an assemblage of apex predators  colonies on South Georgia had a net decline of approximately 70% during this time . Abundance and productivity were monitored annually between 1985 and 2012 Table . Survivaavailable online).6 iterations and retaining every 100th step in order to increase the effective MCMC sample size for the same amount of computer memory. The first 5,000 MCMC draws were removed as burn‐in. Each chain was initialized at different points in parameter space, and mixing and convergence of the MCMC was examined for each parameter and state. The amount of mixing between the three chains indicated the ability of the model to reach a steady state distribution. Convergence of the chains was confirmed using the Brooks‐Gelman‐Rubin diagnostic tool in the OpenBUGS software . The final model structure was validated by removing the demographic data and simulating the population time series using the influential covariates and the marginal posterior distributions for the respective parameters. This procedure demonstrated whether the observed population time series could be recreated from the functional structure, the parameter estimates and the covariate data, and by not including the implicit covariance embedded in the MCMC samples was considered a stringent method for testing the descriptive power of the variables  coefficients describing the relationship between different covariates and demographic processes between 1985 and 2012 Appendix , (2) BerAppendix .The demographic function of survival included covariates that were demonstrated to be influential at the population level in the analysis of deviance study reported by Horswill et al. ; i.e., pMacronectes halli) and Southern (M. giganteus) Giant Petrel chicks successfully reared to fledging. Northern and Southern Giant Petrels are large birds that are predators and scavengers. On Bird Island, these species breed sympatrically at densities that, at the time of publication, were among the highest in the world. Nesting pairs of Giant Petrels in three study areas close to the penguin study colony were visited weekly during incubation and chick‐rearing to determine the local productivity. This measure represents the number of adult Giant Petrels that will be foraging under highly constrained central‐place‐constraint for the whole of the penguin breeding season, including when the Macaroni Penguin chicks fledge. Penguins form a major component of Giant Petrel diet during the breeding season , as well as an adult Macaroni Penguin being killed by a Giant Petrel in waters close to the study colony . Therefore, the predation pressure index is taken to reflect predation by both Giant Petrels and sub‐adult Antarctic fur seals.For the index of predation pressure we used the number of Northern . A study of Macaroni Penguins breeding in the Indian Ocean did not identify a significant relationship between arrival mass and productivity, however, a positive correlation has been reported in other crested penguins available online),available online).SSTa are considered to have a major influence on prey biomass within the South Georgia continental shelf zone  and five state variables: one fledgling , three sub‐adult , and one adult stage : The deterministic transition matrix was based on breeding success  and bottom‐up control from SSTa : The logit functions for adult and fledgling survival rates are shown in Eqs. f and a denote birds in the fledging year and birds that are older than 1 yr, respectively. Age dependence was determined by using different parameters for the baseline survival of each age class . Following Horswill et al. (1) and SSTa terms (α3) replicated the effect of these covariates across both age classes, and an extra coefficient (α2) was included in the function for fledgling survival to allow for an additional component of predation on fledglings , environmental forces , inter‐ (D) and intraspecific competition (C), and predation pressure (P): The productivity function Eq.  includedC.The number of breeding females estimated by the process model was fed back into the productivity function as the auto‐covariate s2) was calculated using the mean variance of the annual counts  and the mean number of counts (n¯ = 4.7 repeated counts): Observation error was stochastically assigned in each time step to account for annual variation associated with detection and unspecified covariates. For survival, the maximum range of uncertainty was set using the 95% confidence intervals (CIs) from the mark–recapture analysis in order to encompass both process variability and observation error Fig. B‐C. The As with simpler regression analyses, the covariate data will also contain some observation error. Ignoring these uncertainties in a typical regression framework that uses model selection  can lead to spurious correlations. It was not computationally feasible to propagate these sources of uncertainty through the state‐space model, however, the embedded model selection approach essentially operates as a Bayesian equivalent to model averaging .The imputed segment of the predation pressure time series was slightly higher and more variable , however variability was consistently low , and values were lower during the period of population decline  per year from 1985 until ca. 2000, the trend thereafter could not be reliably resolved; declining at 1.2%   decreased (Williams and Rodwell unpublished data). Therefore, the predation pressure index may represent mechanisms acting at the community level, and further work is needed to examine the interaction between bottom‐up and top‐down forces during adverse climatic conditions  could rapidly destabilize the penguin population. More broadly, this study highlights the importance of considering how pelagic predators are influenced by multiple drivers when examining their population dynamics, and assessing options for conservation management. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Plasmonically enhanced metal-insulator-metal (MIM) type structures are popular among perfect absorbers and photodetectors in which the field enhancement (for increased absorption) mechanism is directly coupled with collection (photocurrent) processes. In this work we propose a device structure that decouples absorption and collection parts for independent optimization. Double-stacked MIM (i.e. MIMIM) photodetectors operating in the near-infrared (NIR) spectrum up to 1200 nm wavelength are demonstrated. In the absorbing MIM (at the top side), we have used Silver nanoparticles resulting from dewetting, yielding a very low reflection of 10% for the most part of the 400 to 1000 nm wavelength range. An unconventional plasmonic material, Chromium, exhibits an absorption peak of over 80% at 1000 nm. The complete device has been fabricated and the photo-collection tunneling MIM (at the bottom) suppresses the leakage current by metal workfunction difference. An optimized stack consisting of Silver – Hafnium Oxide – Chromium – Aluminum Oxide – Silver nanoparticles (from bottom to top) yields a dark current of 7 nA and a photoresponsivity peak of 0.962 mA/W at 1000 nm and a full width at half maximum of 300 nm, while applied bias is 50 mV and device areas are 300 μm × 600 μm. The concurring trend of scaling down the electronic and photonic devices has led to thriving devices that target this requirement by employing plasmonic nanoscale active layers. While Otto and Kretchmann configurations were the presumptive methods to excite surface plasmons (SP), gratings1245679101While it is plausible to fabricate perfect absorbers using patterning techniques such as electron beam lithography, prohibitive costs associated with such methods cast doubt on their practical use for large volume manufacturing. Therefore, dewetting of thin metallic films has emerged as a strong candidate to form nanostructures in a low cost and large-area compatible fashion. Not only does dewetting result in low-cost fabrication of active plasmonic layers, but also the structures formed with this technique exhibit broad absorption spectra. However, there is a trade-off between the spectral bandwidth and the peak height of the absorption profile. Dewetting approach has also been utilized for solar cells to increase scattered light reaching the active region18Incident light on nanoparticles excites localized surface plasmons (LSPs) or surface plasmons (SPs) and their non-radiative decay results in energetic hot electron-hole pairs that can be harnessed as photocurrent at a Schottky junction20k), widely used plasmonic metals are typically good reflectors due to high optical refractive indices (n′). Electric field does not efficiently penetrate into materials with high optical refractive index. Surface plasmon polariton (SPP) quality factor is a suitable metric for the attenuation of fields in a material, taking into consideration the absorption coefficient as well as the permittivity (ε = n2). In this paper, first absorbing top MIM is investigated computationally and experimentally and afterwards, the photocollection MIM is optimized and the overall photoresponsivity performance is examined. Traditionally Silver and Gold are metals of choice to construct plasmonic nanostructures in the visibleSimulations the MIMIM structure using FDTD Solutions  show that the absorption peak lies in the NIR range of interest for insulator (Al2O3) thickness values between 30–50 nm. The thickness of the insulator is chosen and fixed at 40 nm for all the simulations and experiments. We use atomic layer deposition system (ALD) to deposit insulators and control their thicknesses precisely. The main reason for choosing Aluminum Oxide as the spacer is the fact that among dielectrics, Aluminum Oxide is the least defective one and possesses the lowest k value (imaginary part of refractive index) compared to others like Zinc Oxide, Titanium Oxide and Hafnium Oxide.Ultra-thin, metal films like gold (<10 nm) are not stable due to surface forces at metal-air interface. As the metal film thickness is a few nanometers, not only the deposited film is unstable but also it can barely be regarded as a continuous film. When the thicknesses are in the order of 10 nm, the film is in a thermodynamically metastable state meaning that under some excitation, the film will evolve by agglomerating into islands to minimize the surface area with airabs). Both simulation and experimental results are normalized to Aluminum coated mirrors. The simulations are carried out in 3D while the nanoparticles are extracted from the SEM image into the simulation environment assuming a 50 nm thickness which is the only simplification in simulation  and 15 nm in x and y directions and the simulation area is 500 nm × 500 nm.The simulated MIMIM structures are shown in inset of . The 50 Spectral reflection results calculated computationally agree well with experimental ones. The little discrepancy between simulation and experiment stems from the fact that we have approximated the thickness of particles to be 50 nm for all of them. Moreover, there is generally a blue-shift in experimental reflection results compared to the simulation results which is attributed to the tarnishing of silver. This is justified once more in the photoresponsivity results in the following sections. It can be deduced from At 400 nm  we obserQSPP is defined asSPP quality factor where ε′ and ε″ are the real and imaginary part of permittivity respectivelyIn order to collect the resulting hot electrons or holes, a tunneling design is proposed. There is another possible choice which is using a semiconductor directly in Schottky contact with absorbing metal. The latter is rather inefficient since the dark current typically is rather high in Schottky contacts compared to the tunneling counterparts. Out of the four fabricated samples that are investigated, two are Aluminum – Hafnium Oxide – Aluminum or Chromium absorbing metal – Aluminum Oxide – Silver nanoparticles (from bottom to top), and the other two are the same structures without Silver nanoparticle layer (MIMI) as references to the first two, respectively. Studying these samples emphasizes plasmonically enhanced absorption as well as the effect of the choice of absorbing metal type. One important design consideration for the junction is the substantial role of absorbing metal thickness in photocurrent. If the absorbing metal is chosen to be too thick, ohmic loss would degrade photoresponsivity. Therefore, the thickness of absorbing metals is chosen to be 30 nm. The reason for choosing 30 nm thick top metals is that the skin depths of metals in the wavelengths of interest are approximately 20 nmIn order to design an efficient tunneling junction, the main parameters are the choice of bottom metal and tunneling insulator. The available and suitable insulators for tunneling purpose in our ALD system are Aluminum Oxide and Hafnium Oxide. Aluminum Oxide is chemically etched during lithography developing steps in the process (see methods). Given the fact that the tunneling insulator is thin, we chose Hafnium Oxide which is also a high-k dielectric. The thickness of the insulator is critical in performance. For thicknesses above 4 nm the photocurrent is highly suppressed due to the exponential dependence of tunneling current on insulator thickness. For thicknesses lower than 4 nm on the other hand, we observe poor uniformity of insulator film. On the grounds of this discussion the tunneling insulator was selected to be 4 nm.Another design parameter which is the bottom contact metal was chosen to be Silver. The main reason for this choice was to create an offset in the metal work functions at the two sides of the tunneling insulators. This offset facilitates the suppression of dark (leakage) current which is highly preferable in photodetector performance. Of the possible low resistivity choices, Silver revealed the best performance for both devices with Aluminum and Chromium absorbing metals, due to the fact that it has lower work function than the two absorbing metals. We could use a bottom metal with higher work function than that of Aluminum and Chromium for hot-hole collection but the scarcity of such low resistive metals lead us to Silver to collect hot electrons. The energy band diagram schematic of the tunneling junction is illustrated in The applied bias is negative as with the probes illustrated at As it can be inferred from It should be pointed out that the photoresponsivity curves follow the simulated absorption curves quite acceptably. There is a red-shift in photoresponsivity results compared to absorption results of The demonstration of absorbers and photodetectors were investigated in this work with focus on NIR region. We investigated the effect of four different bottom metals in plasmonically enhanced MIM absorbers with silver nanoparticles. After identifying MIMs with highest absorption in bottom metals of them, which happen to be Aluminum and Chromium, the complete MIMIM photodetector was fabricated in which the energy band engineering is done such that the dark current is suppressed and the photocurrent mechanism is based on tunneling of hot electrons. In an MIMIM structure, light falling on the particles excites SPs in the middle absorbing metal. The non-radiative decay of SPs result in hot EHPs and hot electrons are directed to the bottom metal by tunneling. We point out Chromium as an efficient absorbing metal due to high loss and suitable plasma frequency that happens in NIR region rather than UV for other metals of choice here. The resulting absorption and photocurrent curves are highly in agreement. Our photodetector design manages to decouple absorption and photocollection parts which facilitates design of efficient photodetectors by engineering each layer independently.−6 Torr. Afterwards the sample was masked with a small margin (to get the negative contact) and a deposition of hafnium oxide at 200 °C was carried out with Cambridge Nanotech Inc., Savannah S100 ALD system using Trimethylhafnium and milli-Q water as Hafnium and Oxygen precursors respectively with a Nitrogen flow of 20 sccm. For a lift-off process, HMDS and AZ5214 photoresist was spin coated and exposed to UV light using mask aligner and was developed in AZ400K solution, a follow up deposition of 30 nm absorbing metal (either Aluminum or Chromium) was deposited in the same conditions as the negative contact metal and acetone was used for the lift off process. Afterwards, 40 nm Aluminum Oxide was deposited in the same conditions in ALD using Timethylaluminum and milli-Q water as Aluminum and Oxygen precursors respectively. Then, using an optimized inductively coupled plasma (ICP) dry etching recipe−6 Torr, and a consecutive rapid thermal annealing  step was carried out at 120 sccm N2 flow rate. Temperature was ramped up to 500 °C in 5 min, followed by 20 min annealing at constant temperature, 500 °C. At the end of the annealing step, the temperature is passively reduced to 250 °C followed by manually removing the sample to facilitate faster cooling which resulted in some oxidationA Silicon wafer with unintentional doping was used as the substrate. The substrate was cleaned in Acetone, Isopropanol and de-ionized water for 10 minutes each using ultrasonic agitation. Afterwards, 70 nm silver was deposited with thermal evaporation at a base pressure of 2–4 × 10We used a custom reflection measurement setup comprised of a Halogen illuminator connected to a microscope and directed perpendicular to sample and the reflected light from the microscope was fed to a Newport OSM2 spectrometer and the data was collected by interfacing the spectrometer with PC . All meaThe dark I-V measurements of the devices were conducted using KEITHLEY 2401 Sourcemeter. In order to measure photoresponsivity for the wavelengths between 400 to 800 nm, we used a Xenon-lamp illuminator. The output of the lamp goes through an Oriel 74004 monochromator . The monochromatic light was collimated and chopped with a mechanical chopper and the chop speed (380 Hz) was read by a lock-in amplifier (SR830). The short circuit current was measured by the lock-in amplifier. Also, a calibrated Si detector was used at the sample position to collect the light source power at each wavelength step. For the wavelengths between 800 to 1300 nm, a similar setup was used, except that the source was a super continuum laser (Fianium) which was monochromated by an acousto-optic transmission filter  and the chopper speed was 960 Hz. The spectral power was detected with a calibrated InGaAs detector .How to cite this article: Dereshgi, S. A. et al. Plasmonically enhanced metal–insulator multistacked photodetectors with separate absorption and collection junctions for near-infrared applications. Sci. Rep.7, 42349; doi: 10.1038/srep42349 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


HIV self‐test among men who have sex with men (MSM) and those involved in HIV prevention and care.The aim of the study was to explore preparedness for the MSM through a self‐completed questionnaire and oral fluid specimen collection (n = 999) was combined with qualitative, thematic, analysis of data collected through 12 expert focus groups (n = 55) consisting of gay men, National Health Service (NHS) staff, community organizations, entrepreneurs and activists. Findings were subsequently combined and assessed for synergies.A mixed methods exploratory research design was employed, detailing awareness and willingness to use the self‐test and the perceived barriers and facilitators to implementation. Quantitative and qualitative data collection and analysis were completed in parallel. Descriptive and inferential analysis of cross‐sectional bar‐based survey data collected from MSM, self‐test awareness was moderate (55%). Greater awareness was associated with increased educational attainment  and previous history of sexually transmitted infection (STI) testing . Willingness to use the test was high (89%) and associated with meeting sexual partners online . Experts highlighted the overall acceptability of self‐testing; it was understood as convenient, discreet, accessible, and with a low burden to services. However, some ambivalence towards self‐testing was reported; it could reduce opportunities to engage with wider services, wider health issues and the determinants of risk.Among HIV testing and enhance prevention and access to care. Levels of awareness are moderate but willingness to use is high. Self‐testing may amplify health inequalities.Self‐testing represents an opportunity to reduce barriers to  Gay and other men who have sex with men (MSM) are the group at highest risk of acquiring HIV infection within the UK. In 2013, they represented 54% of all new diagnoses. HIV testing rates amongst MSM overall have increased In the UK, rapid result HIV self‐test kits became legally available in April 2014, and subsequently April 2015) commercial products became available. While self‐testing has been available in the USA for some time, it is not yet available in other national settings such as Canada, Australia or New Zealand. The international literature from countries where self‐testing has been available shows that the key facilitators to implementing, and scaling up, programmes of HIV self‐testing interventions are that they are convenient  commerciHere, we present findings from the first UK study of self‐testing and explore three key research questions vital for future policy and practice development in the UK with potential transferability to other international settings where HIV self‐tests may be implemented.Which factors are associated with levels of awareness of the HIV self‐test among MSM?Which factors are associated with willingness to use the HIV self‐test among MSM?What are the key barriers and facilitators to the effective use of the rapid result HIV self‐test among the MSM population?Twin studies were combined and respective findings integrated to provide synergistic interpretations regarding preparedness for self‐testing across both MSM and those involved in providing HIV prevention and care services.® Oral Specimen Collection Devices; OraSure Technologies, Inc, Bethlehem, PA, USA). Oral fluid specimens were analysed at the West of Scotland Specialist Virology Centre. These were tested for anti‐HIV using the Vironostika HIV Uni‐Form II Ag/Ab enzyme immunoassay . Positive samples were re‐tested and, if repeatedly reactive, were confirmed using western blot. Overall, 1340 men completed the questionnaire [45% response rate (RR)], with 1151 also providing oral fluid samples (38.6% RR). Men were excluded from the analysis if they: tested positive for HIV via the oral fluid sample (n = 61); did not provide a specimen (n = 189); had missing data on all self‐testing questions (n = 58); or did not self‐identify as being gay or bisexual (n = 33). This resulted in an overall sample size of n = 999.The University of Glasgow's triennial Gay Men's Sexual Health Survey was implemented in Glasgow, Edinburgh and Dundee in 17 venues (including two saunas) within the commercial gay scene in May 2014. Ethical approval was granted by the College of Social Sciences Ethics Subcommittee at the University of Glasgow (ref: 400130179). Data collection was similar to that in previous surveys Variables included were age, educational level and frequency of visits to the “gay scene” .Respondents were asked “With how many men have you had anal sex WITHOUT a condom in the last 12 months?” and those who reported at least one unprotected anal intercourse (UAI) partner were asked: “How often was this with a casual partner?”; “How often did you know these partners' HIV status?” and “Were any of your partners HIV positive?” A single measure of higher risk sexual behaviour was derived from the above to include men who reported UAI with at least two casual, and/or unknown/discordant partners in the previous 12 months .Men were introduced to questions regarding the self‐test with the following text: “HIV self‐testing kits were licensed in the UK in April. This will enable men to do a test themselves and get the result immediately. This is different from self‐sampling, when you do the test at home but send the sample to a laboratory for testing.” They were then asked about awareness of self‐testing kits by answering the question “Have you heard of self‐testing kits?” with the options of yes, no or don't know. This was then recoded to “yes”  and “no”/”don't know” (combined “no”/”don't know” responses).Men were also asked about the likelihood of using self‐testing kits in various settings: if it was freely available on the NHS; in a community clinic or supervised location; on their own; with a partner; and if they had to pay for it. Finally, men were asked to indicate if they would be willing to use self‐testing kits under the following conditions: after a condom burst or after an episode of unprotected sex; instead of going to a clinic; before having sex with a new partner; and instead of a self‐sampling kit.spss version 21. Chi squared tests were used for bivariate comparisons and binary and multivariate logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) to explore factors associated with awareness of and willingness to use self‐testing kits. The final model contained all variables significant at the bivariate level (P < 0.05) in order to assess which remained statistically significant.Data were entered and analysed using Twelve focus groups (FGs) were conducted with 55 multi‐professional, patient and provider “expert” participants between October 2014 and February 2015 in a range of settings . Group members were all involved in using, offering, or implementing self‐testing, or providing associated pathways into HIV care, and/or prevention. Recruitment used (1) the project funders, who assisted with local NHS recruitment within each respective health board, and (2) the research teams' existing connections with a range of organizations. Sampling balanced recruitment across urban and rural NHS board areas, and included heterogeneous groups of gay men (three FGs), a range of NHS staff (six FGs), and a range of staff from community organizations, activists and people working for businesses with vested interests in MSM (i.e. sex shop and sex sauna staff) (three FGs). An interview topic guide facilitated discussion regarding the barriers and facilitators to the implementation of self‐testing within the MSM population. Focus groups were conducted by a number of team members, primarily by CP but assisted by PF, JF, IY. Thus, facilitators were all white and included mixtures of straight, lesbian and gay researchers.nvivo 10 qualitative data analysis software  by three of the research team . The analytic focus was primarily descriptive, identifying areas of commonality in experts' beliefs. Ethical approval was given by Glasgow Caledonian University and NHS R and D approval for NHS Project ID: 164239; R&D2014AA089.Data were transcribed and analysed thematically using Following parallel and independent quantitative and qualitative data analysis, the key findings from each study were positioned within a single matrix, with a focus upon integration and synthesis. Given the differences in underlying epistemologies of each research approach , valuable knowledge was generated both within each study and across respective studies. As such, the matrix was interpreted by the first and last authors in relation to the complementarity and unique contribution of respective findings as patterned across various inter‐related and overlapping descriptions of context. A consensus was reached via iterative analysis and discussion. For ease of reading, the results of data integration are presented within the Discussion section of this paper.The average age of participants was 34 years  10.96 years) with the majority identifying as gay (92.5%). Most reported post‐secondary school education (86.7%), with 34.9% reporting further/vocational‐level education, and 51.8% reporting degree/postgraduate‐level education. Almost all reported sexual contact with a man in the previous 12 months (94.0%) and 53.7% reported higher risk sexual behaviours . Only 15.4% had never had an HIV test, while 39.8% had tested in the previous 6 months and 10.1% reported having a sexually transmitted infection (STI) in the previous 12 months .n = 599; 60.0%) and those who had not or did not know if they had  (Table Binary logistic regression compared men who had heard of self‐testing kits (%) Table . The oddn = 887; 88.8%) reported that they would be willing to use HIV self‐testing kits in at least one circumstance. Of these men, 77.3% (n = 686) reported that they would use a kit after a condom burst or after an episode of unprotected sex, 74.9% (n = 664) would use a kit instead of going to a clinic, 65.2% (n = 578) would use a kit before having sex with a new partner, and 59.9% (n = 531) would use one instead of a self‐sampling kit , 79.9% (n = 709) reported that they were very likely/likely to use self‐testing kits if they were freely available on the NHS, 68.0% (n = 603) were very likely/likely to use the kit on their own, 65.3% (n = 579) were very likely/likely to use the kit in a community clinic or supervised location, 57.5% (n = 510) were very likely/likely to use the kit with a partner, and 45.2% (n = 401) were very likely/likely to use the kit if they had to pay for it The convenience, immediacy, discretion and privacy of testing within one's own home were important factors in facilitating the likely implementation of the self‐test and understanding how the self‐test could reduce barriers to testing. Autonomy regarding decisions on when to test and dramatically reduced waiting times for results figured strongly within the discussions as other key facilitators. Self‐testing was welcomed in terms of cost‐efficiency and the rationalization of limited NHS resources; for example, “how expensive is it to post a kit as opposed to half an hour of a consultant's time?” (NHS staff FG). Self‐testing was also described as potentially reaching new populations by reducing perceived barriers to testing, such as the stigma associated with use of traditional genitourinary medicine (GUM) services: Discussions also detailed how the self‐test facilitated testing and would reduce barriers to testing by accommodating the hectic reality of many people's lives. It could be easily used by men who had busy lives, who lived rurally, and who would struggle to use traditional clinics for a range of reasons: “I think it's good, because some people, for example, they're married and they have children, for example, some men. But they still engage, like, in gay sex. Obviously, they're not going to their doctor to get a test, a blood test” (Gay men's FG). In this way, the self‐test could enable high‐risk men to test more frequently and could enable more vulnerable men to engage in testing for the first time. For example, “So what better way of actually taking away any stigma about it than to have something as regular where you might actually go to pick up paracetamol?” (Non‐NHS stakeholder FG).F1Uh‐huh. What are your concerns, P2?P2Just the one o'clock in the morning stuff.P1I know, and go and jump off a bridge or something.P2Yeah, and I suppose that is…P1That is the worry.P2…I suppose that is the difference between home testing and home sampling, is, okay, you've got the advantage that you've got it within 20 minutes, half an hour, whatever it might be, which is great. The disadvantage is just that lack of connection with other people, and that support.Key barriers to the uptake and use of self‐testing focussed upon lost opportunities to engage with a wide range of services, staff and holistic understandings of health and the actual determinants, as opposed to the consequences, of risk behaviour, as the following extract shows: “very rarely is it just about… When someone goes to, even a sexual health clinical, very rarely is it just about sexual health. There are other support needs” (Non‐NHS stakeholder FG). Primarily among NHS staff, there was some resistance to the idea that the decoupling of testing from traditional services was beneficial or warranted. Participants outlined the lost opportunities that self‐testing technologies herald; there is no guaranteed continuity of care, no easy access to the full range of clinical expertise , no partner notification, and no opportunity for additional risk reduction and intervention. Similarly, major concerns were articulated about the test users discovering their reactive results when alone. P1The test is going back to the timing from infection date to it being shown, showing up in your blood. So, I think the errors that can be involved in self‐testing are with yourself, you know, wait until you, what do you call it, your viral load becomes testable. There's no like pregnancy wait for two weeks […..].P4I think this probably then also depends on how educated you are about it in the first place, rather than, okay, you know everything about it, so we'll take a test and that's your result. It depends on how educated you are about when your potential exposure wasThe discussions also outlined barriers to the effective use of self‐testing in relation to health inequalities and health literacy. Key concerns focussed upon the skills, abilities and knowledge levels among those who were testing and their accurate interpretation of test results:This work has explored preparedness for self‐testing and describes the overall acceptability of the self‐test in the UK for the first time. The triangulation and integration of key constructs from the two constitutive studies reported here are shown in Table At the individual level, health and HIV literacy were important, with awareness of self‐testing associated with level of educational attainment. Concerns were also expressed about the accuracy of interpreting test results in relation to the window period and to specific risk events. Preferences to use the self‐test rather than visiting a clinic or using self‐sampling, combined with perceptions of convenience and ease of use and potential reductions in stigma, suggest that it is highly likely that the self‐test will reduce some barriers to testing. However, use of the self‐test may therefore lead to a lack of engagement with traditional NHS services. While this may rationalize limited NHS resources, it was also concerning from both the patient and provider perspectives, particularly in relation to follow‐up of reactive results and the accuracy of epidemiological surveillance, but also in relation to lost opportunities for prevention such as interventions that address the determinants of on‐going risk behaviour, or partner notification.The synthesis also indicates the likely importance of social and economic factors in shaping the kinds of men who may use the self‐test. It illuminates the complexity of these issues; the quantitative findings representing bar‐based urban populations of MSM suggest the potential importance of digital and social media use in the likelihood of using the self‐test, with concomitant repercussions extending to service redesign for accessing future regular testing, the provision of prevention interventions, accessing confirmatory testing, and on‐going care for those who test positive. These findings could reflect the role of digital literacy, or a propensity to engage with innovation or online consumerism; yet, tellingly, patterns in likelihood of use also relate to whether the test would be provided free by the NHS. The qualitative findings, which reflect perspectives from both urban and rural areas, highlighted how additional social and geographical factors, such as relative isolation, may also influence who might be more likely to use both the test and indeed the digital media. Although self‐tests could relieve pressures on clinical time and resources, deracinating HIV from wider and holistic health is contrary to the existing policy landscape and could have unintended consequences for broader sexual health and wellbeing.per se; for example, only 52% of MSM in the general population report having an HIV test within the past 5 years and comparison of population‐based probability surveys and venue‐based convenience surveys has shown increased recent testing in the latter The strengths of this study were its originality and its effective use of exploratory mixed methods. These maximized the strengths of the qualitative analysis which both captured the complexity of the issue and inductively identified areas of concern . The study also consolidated the benefits of quantitative research approaches, illuminating population means and other nomothetic insights (e.g. apparent preferences for self‐testing over self‐sampling). Its weaknesses relate to its exploratory cross‐sectional design and its geographical reach (Scotland only), its sole focus upon the MSM population rather than other populations such as black Africans in the UK, its use of a sample of MSM who mostly identified as gay and were highly educated, and the temporal collection of data prior to self‐testing products becoming commercially available. Recent surveys of MSM recruited via other approaches also suggest that our sampling strategies may well oversample those already engaged with testing behaviours Self‐testing represents a relatively new development within the HIV prevention and care tool kit. Our integrated exploratory findings anticipate that it has great potential to shape HIV prevention and care. Yet, our findings also suggest that its potential will not be realized if it is not considered in relation to the heterogeneity of MSM and the diversity of their needs and preferences. Self‐testing has the capacity to be a transformative technology, potentially, a core part of an integrated online HIV prevention and care system for men who choose this approach and have the requisite digital and health literacy [this could combine access to pre‐exposure prophylaxis (PrEP) and other behavioural prevention initiatives]. However, our integrated analysis also suggests the particular vulnerability of some MSM populations and the potential for further isolation if shifts to digital technologies and self‐managed diagnostic testing reduce provision for other testing opportunities.Further research must examine self‐testing at the individual, organisational and social levels. For example, at the individual level, who will use it? Why do they choose it, rather than, for example, self‐sampling, and indeed which type of self‐test do they prefer ? How will they use it? When, in which circumstances, and with what consequences  will they use it? At the organizational level, questions of the effectiveness and cost‐effectiveness of self‐testing become important; how can it be optimized to reduce barriers to testing? Can it reduce undiagnosed infection and late diagnosis? What, if any, are the health economic benefits of self‐testing and how can they be enhanced (e.g. by targeted self‐testing)? How should the NHS and other services be redesigned to accommodate the way in which self‐testing is decoupled from existing services? To what extent can online prevention and care services complement the self‐management of these diagnostic technologies, and would such online services improve patient experience and contribute to health improvements? With regard to the social consequences of self‐testing, research is needed that examines how self‐testing may amplify health inequities, enabling MSM with good health and digital literacy skills to improve their health while investment in more traditional services and their service users  reduces.PF and LM devised the study, and PF and JR wrote the first draft of the manuscript. PF led the qualitative analysis with assistance from CP and BA; JR undertook quantitative data analysis and contributed to drafts of the manuscript. PF and LM integrated the data analysis. All authors contributed to interpretation of the data, contributed revisions, and approved the final version of the manuscript.Table S1. Sample characteristics of men (n = 999).Click here for additional data file.


Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. ‘Animal models’, which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations.However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non‐randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology.We first illustrate how the occurrence of non‐random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce ‘genetic group’ methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents.r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods.We summarize the conceptual foundations of genetic group animal models and provide extensive, step‐by‐step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow.We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets. Adaptive evolution is a critical way by which populations can respond to environmental change and persist. Quantifying and predicting microevolutionary responses to environmental change in wild populations is consequently a major focus in biology  has revolutionized quantitative genetic studies of wild populations  and residual deviation (e)  as:In brief, an individual's phenotypic value for any polygenic trait is a sum of independent genetic and environmental factors  for each individual included in y are estimated as ‘random effects’, where the design matrix Z maps each individual's value in a to their phenotype in y.Factor levels for ‘fixed effects’ are grouped with the overall population mean (or intercept) in vector a potentially allow ecologists to quantify changes in population mean breeding value over time, and hence test for microevolutionary change, and to determine whether selection acts on genetic or environmental components of phenotypic variation , which quantifies the covariance in additive genetic effects among individuals. In diploid organisms, this covariance is proportional to twice the probability that two individuals inherited homologous alleles ‘identical‐by‐descent’ from common ancestors. The A matrix can be constructed from a pedigree following certain rules and assumptions . This property makes animal models very appealing for wild population studies, because A can prevent bias in estimates of σA2 due to selection, drift, assortative mating and inbreeding . Breeding values and environmental deviations, and thus phenotypes, of focal population founders were simulated from normal distributions with means of zero and variances of one. Mates were randomly assigned and offspring breeding values were calculated as the mean of their parents’ breeding values plus a Mendelian sampling deviation. The population was not subject to selection and is large enough to ensure that genetic drift is negligible. No change in mean breeding value or phenotype across generations is therefore expected. However, immigrants from a separate population were simulated with a mean breeding value three units greater , but the variance was also one . Therefore, immigrants have greater phenotypic values. Since natives and immigrants were paired with randomly assigned mates, the mean phenotypic value of all individuals within the focal population increased across generations solely due to gene flow stemming from immigration Fig. a.Since all immigrants have unknown parents, their phantom parents are by default assigned to an animal model base population, along with the phantom parents of the true focal population founders. This combined distribution of breeding values in the default base population is consequently heterogeneous, because the phantom parents of founders and immigrants have different mean values Fig. b,c. Two First, a basic animal model predicts breeding values assuming a mean of zero in the default base population Mrode , p. 40. Secondly, a basic animal model eqn  returns a priori as it depends on the mean and variance of true breeding values in the true base population relative to the means and variances of true breeding values for the different groups in the default base population. However, our simple simulation illustrates that breeding values defined within the context of a single population  quantify the average deviation from the population mean genotype attributed to the additive genetic effects of an individual's genotype  equals the expected average genetic effect in group j (gj) plus a deviation from the group mean caused by i's genotype . Because all breeding values are deviations from group means, all base population breeding values have an expectation of zero. Consequently, breeding values represent standardized measures of additive genetic effects that allow direct comparison across genetic groups of individuals’ additive genetic effects distinct from the mean additive genetic differences among groups.In an animal model with genetic groups, the ‘total additive genetic effect’ (Appendix S1) of an individual's genotype is separated into a genetic group effect and a breeding value. The genetic group effect measures the expected mean deviation from a reference attributed to a group's total additive genetic effects and the breeding value measures the average deviation from the genetic group mean caused by an individual's genotype . The expression for a quantitative trait phenotype ywhich expands to:i's genotype ui=∑j=1rqijgj+ai replaces the breeding value ai in a basic quantitative trait model without genetic groups. The jth group effect (gj), out of r groups in the base population, contributes to the total additive genetic effects of i in proportion to the expected fraction of i's genome derived from group j (qij). Each gj constitutes an element in the vector g containing all genetic group effects. The fraction qij can be calculated from qdj and qsj of i's parents d and s  and all genetic groups in the base population (r columns). Each row of Q, which lists the contributions of each genetic group to an individual, sums to one.Here, the total additive genetic effect of individual The collection of eqn u) are normally distributed with an expected covariance of σA2A . However, the expected mean of u is no longer zero, but Qg. Therefore, animal models that account for the contribution of genetic group effects to the total additive genetic effects of individuals do so by modelling the mean of u.The total additive genetic effects (anova contrasts) are estimable . However, because individuals inherit genetic group effects, estimating genetic group effects as separate fixed effects is more complicated than simply fitting categorical fixed effects of discrete group membership. An animal model that fits explicit genetic group effects is:a are breeding values of individuals with expected mean zero and covariance σA2A  is utilized as in a basic animal model without genetic groups  as the first r columns of the T matrix in Henderson's  is the sum of the genetic group effects, weighted by the contribution of each genetic group to that individual, plus the individual's breeding value (ai):Solutions to the model in eqn u) (Quaas & Pollak g) are inherited the same way as breeding values (a), as quantified by the A matrix. Therefore, the sum of their effects can be modelled by augmenting A−1 to implicitly incorporate the group effects into predictions of u , and estimates of the r group effects g. In contrast to the approach where genetic group effects are explicitly estimated as separate fixed regression coefficients, obtaining the predicted breeding value for an individual (ai) requires subtracting the sum of the genetic group effects, weighted by the contribution of each genetic group to that individual, from the predicted total genetic effects of the individual (ui) returned by the model:Solutions to the model in eqn g). The genetic group effects can be fitted either explicitly by including fixed covariate regressions on columns of Q, or implicitly by modelling total additive genetic effects using A*. Both models provide equivalent unbiased predictions of breeding values and individual total additive genetic effects as well as unbiased estimates of the additive genetic variance for the simulated data set , thereby extending and standardizing current software capabilities. We comprehensively demonstrate how to use these functions with MCMCglmm, asreml, WOMBAT and ASReml (Appendix S6.4) and model outputs have been deposited in the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.jf7cr .While the complexities of fitting genetic group animal models might seem intimidating, in fact there is no technical reason why such models cannot be fitted to appropriate wild population data using either residual maximum likelihood or Bayesian methods. Further, genetic group methods can be applied to multivariate and non‐Gaussian response variables. In Appendix S6.4, we provide extensive tutorials that demonstrate how to fit genetic groups either explicitly as separate fixed regressions or implicitly within the random effects structures that predict total additive genetic effects in animal models implemented in Hadfield  and asreHowever, as with any complex quantitative genetic analyses of wild population data, key decisions need to be carefully made before animal models with genetic groups can be fitted. Further, limitations of and constraints on the genetic group methods remain, meaning that estimated effects will need to be interpreted with due caution. In the following sections, we summarize some key decisions and suggest potential resolutions to current limitations.a vs. u), values are easily transformed from one to the other through simple mathematical formulae  means that the uncertainty in genetic group effects is included in the prediction of u, which can increase the prediction error variance of u. Unless uncertainty is incorporated into calculations transforming values from u and a, the accuracy of calculated breeding values . However, it is less clear how priors specified for additive genetic variances affect posterior inference on genetic group effects fitted implicitly (Appendix S6.4.2.3), particularly when variance component estimation is the aim of the analysis. Consequently, fitting genetic groups explicitly as separate fixed regressions may be the most straightforward approach when using Bayesian inference . However, evolutionary dynamics can cause the variance in true total additive genetic values within genetic groups to differ among groups, violating the assumption of homogeneous variances  assume that phantom parents are unrelated within and across groups and that no drift or inbreeding occurs within the base population . Since the consequences of missing pedigree information are likely to differ from the consequences of missing phenotypic information . We propose that such methods should be applied in animal model analyses of wild population data and provide Hadfield , it remaet al. et al. Genetic group methods are not a panacea; they cannot be expected to completely rescue analyses based on poor population pedigrees or fix all resulting problems. Missing pedigree information may still bias animal model parameter estimates even when genetic groups are modelled. Unknown parents decrease pedigree connectedness, potentially causing phenotypic variation to be attributed to environmental rather than genetic effects. Likewise, missing parent information can affect estimates of inbreeding and hence inbreeding depression (Pemberton It is equally important to realize the opportunities for conceptual advances that genetic group animal models afford, rather than solely viewing them as technical means to minimize bias. Genetic group methods can reveal the relative additive genetic values of natives and immigrants for any quantitative trait of interest, and thereby distinguish microevolutionary changes in population mean phenotypes arising through direct responses to local selection from changes caused by immigration and resulting gene flow. We can thereby quantify the degree to which immigrants introduce genetic effects that are congruent or counter to the direction of local selection, which is an essential step towards predicting adaptive responses to environmental change and explaining microevolutionary stasis (Merilä, Sheldon & Kruuk http://dx.doi.org/10.5061/dryad.jf7cr (Wolak & Reid Data associated with this paper are deposited in the Dryad Digital Repository Appendix S1. Glossary.Appendix S2. Collection of pedigrees from wild populations.Appendix S3. Breeding value prediction.Appendix S4. Simulating data with genetic group effects.Appendix S5. Development of genetic groups in agricultural science.Appendix S6. Construction and implementation of Q and A*.Appendix S7. Fuzzy classification of genetic groups.Click here for additional data file.


COG) develops and implements multi‐institutional clinical trials with the primary goal of assessing the efficacy and safety profile of treatment regimens for various pediatric cancers. However, the monetary costs of treatment regimens are not measured. AALL0232 was a COG randomized phase III trial for children with acute lymphoblastic leukemia that found that dexamethasone (DEX) was a more effective glucocorticoid than prednisone (PRED) in patients younger than 10 years, but PRED was equally effective and less toxic in older patients. In addition, high‐dose methotrexate (HD‐MTX) led to better survival than escalating doses of methotrexate (C‐MTX). Cost data from the Pediatric Health Information System database were merged with clinical data from the COG AALL0232 trial. Total and component costs were compared between treatment arms and across hospitals. Inpatient costs were higher in the HD‐MTX and DEX arms when compared to the C‐MTX and PRED arms at the end of therapy. There was no difference in cost between these arms at last follow‐up. Considerable variation in total costs existed across centers to deliver the same therapy that was driven by differences in inpatient days and pharmacy costs. The more effective regimens were found to be more expensive during therapy but were ultimately cost‐neutral in longer term follow‐up. The variations in cost across centers suggest an opportunity to standardize resource utilization for patients receiving similar therapies, which could translate into reduced healthcare expenditures.The Children's Oncology Group ( The adult healthcare community has embraced health economics as a necessary tool in maximizing the value of medical care, which is the optimal patient outcome at the lowest cost Few studies identifying pediatric cancer costs exist in bone marrow transplant, acute lymphoblastic leukemia (ALL), and rhabdomyosarcoma The Children's Oncology Group (COG) is a cooperative group that designs and implements multi‐institutional clinical trials. Most children with newly diagnosed cancer in the United States (US) are enrolled in COG clinical trials AALL0232 was a COG phase III pediatric B‐ALL trial that randomized patients upfront to 14 days of dexamethasone (DEX) or 28 days of prednisone (PRED) during an Induction phase and to high‐dose methotrexate with leucovorin rescue (HD‐MTX) or escalating “Capizzi” methotrexate plus pegaspargase (C‐MTX) in the Interim Maintenance I (IM 1) phase. The trial found that HD‐MTX had superior event‐free survival (EFS) when compared to C‐MTX, and, for patients <10 years of age, DEX was superior to PRED The study population included patients enrolled on COG AALL0232 who were successfully linked to the PHIS dataset. AALL0232 enrolled patients between the ages of 1 and 30 years with high‐risk B‐ALL from December 29, 2003 until January 21, 2011 Patients enrolled on AALL0232 at a PHIS contributing center were linked to their PHIS data using common elements in the two data sources The final dataset was inclusive of data elements that were unique to the COG and PHIS data sources. COG provided data specific to a patient's trial participation: date of enrollment, treatment arm, end of Induction response, date of relapse, and date of last follow‐up or end of study date. COG data also informed demographic and insurance variables. PHIS data were inclusive of information generated from billing charges specific to care provided in the inpatient setting, emergency department, and observation unit at 44 free‐standing US pediatric hospitals PHIS provides daily inpatient adjusted charges for each patient for the following components: pharmacy; laboratory services; clinical, which is comprised mostly of surgical and procedural costs; supplies, which includes medical devices and surgical equipment; imaging, and room and board. Daily adjusted inpatient cost was calculated by multiplying PHIS Health Care Financing Administration wage/price index adjusted charges by the appropriate ratio of cost to charge (RCC) provided by each hospital and standardized to the 2013 consumer price value The relationship of treatment arm and cost with age , gender, race , and insurance status  was explored.P < 0.05. Demographic variables for patients enrolled on AALL0232 and successfully merged to PHIS data were compared to patients on AALL0232 but not merged to ensure the cohort was representative of the trial participants. Medians with interquartile ranges (IQR) were calculated for age; frequencies with percentages were calculated for gender, race, and geographic region. To compare the two groups, a Wilcoxon rank sum test was used for age and a chi‐square test was used for gender, race, and geographic region.All statistical analyses were performed using STATA  and Prism . Statistical significance was determined by a two‐sided Standard descriptive statistics  were summarized for total costs per randomized arm of all patients in the cohort at the following time intervals: at the completion of the courses of therapy involved in the randomization, which was IM 1 for the methotrexate arms and Induction for the steroid arms, at the completion of protocol therapy, and at the time of last follow‐up. Component costs were compared between randomized arms at these same timeframes to identify drivers of total cost. Daily inpatient costs were compared between the randomized arms for the courses involved in the randomization. Standard descriptive statistics and a Wilcoxon rank sum test were used to compare length of follow‐up at the end of protocol and the last follow‐up timeframes. A Wilcoxon rank sum test was used to perform the univariate analysis of adjusted inpatient total costs and component costs by randomized arm at each time interval and daily costs during the randomized courses only. A linear regression model was used to compare costs between randomized arms, adjusting for the covariates above. As cost was not normally distributed, it was log transformed prior to inclusion in the linear regression model. Patients were censored at relapse in all timeframe analyses except the last follow‐up timeframe; therefore, the last follow‐up analysis includes costs associated with salvage therapy. In each timeframe, patients were censored at time of death and when removed from study protocol.Variation in total cost across hospitals was investigated for patients randomized to HD‐MTX during IM 1. Median and IQR for total costs were tabulated for each hospital with patients on the HD‐MTX arm during IM 1. A Kruskal‐Wallis test was used to determine whether the total costs were significantly different across hospitals.It is possible that patients who suffer a significant toxicity from therapy may incur increased costs that could skew the estimates for a specific hospital. To establish a more uniform comparison between hospitals, a second analysis comparing total cost, pharmacy cost, and hospital days including only patients who received HD‐MTX according to the proposed course schedule was performed. Patients were deemed to have received HD‐MTX according to schedule if they had pharmacy billing codes for four doses of HD‐MTX within the proposed 67‐day course. Patients who received four doses of HD‐MTX within 67 days were compared to patients who did not receive four doses in that timeframe using a Wilcoxon rank sum test for age and a Chi‐square test for gender and race.Two sensitivity analyses were performed. The first analysis aimed to estimate how much less the C‐MTX costs would need to be in comparison to HD‐MTX for the difference in cost to remain statistically significant. Since C‐MTX is administered in the outpatient setting at most institutions, we are missing the costs of C‐MTX administration for patients randomized to this arm. We sequentially inputted additional costs to the C‐MTX arm during IM 1 to represent the missing outpatient administration costs and compared these arbitrary totals using a Wilcoxon rank sum test to the existing HD‐MTX data.The second analysis was performed to account for patients who may have received additional, unplanned cycles of chemotherapy that would increase cost totals. Patients randomized to C‐MTX at the time the AALL0232 interim analysis was reported on April 1, 2011 were rerouted to a HD‐MTX arm if they were still on therapy and had not yet completed the first cycle of Maintenance. The first cycle of Maintenance for slow early responders could occur as late as 14 months after the start of therapy. Therefore, patients enrolled prior to February 2010 could not be re‐routed. Analysis of total cost was performed on only those patients enrolled before February 2010 to see if cost estimates changed.Of the 3,083 patients COG enrolled on AALL0232, 935 patients matched within the COG and PHIS databases and had inpatient data within the COG treatment period, which is 91% of the patients enrolled by COG and treated at PHIS institutions and 30% of the total patients enrolled by COG  with a median total inpatient cost for the DEX arm being $37,724  and median total inpatient cost for the PRED arm being $33,554 .There was a significant difference in total cost between the DEX and PRED arms during Induction in both the univariate and multivariable analyses  than the median cost of C‐MTX at $15,786  in both the univariate and multivariable analyses  versus $152,366  for DEX versus PRED, unadjusted During Induction, the median inpatient costs for all components of care were greater on the DEX arm when compared to the PRED arm. This difference was significant in both the univariate and multivariable analyses for supply and room and board costs, univariate analysis only for imaging costs, and multivariable analysis only for the laboratory costs. The median pharmacy, supply, laboratory, imaging, and room and board costs for the DEX arm were significantly greater than the PRED arm during protocol therapy in both the univariate and multivariable analyses. Similar to the total cost analysis, there was no difference in component costs between the DEX and PRED arms at last follow‐up in the univariate or multivariable analyses.All of the cost components had significantly greater median inpatient costs on the HD‐MTX arm when compared to the C‐MTX arm during IM 1 in both the univariate and multivariable analyses, except for imaging Table . UnadjusP < 0.001). No significant difference in age or race existed between the patients who adhered to the proposed IM 1 schedule compared with those who did not (Table P < 0.001). Total median costs during IM 1 ranged from $24,520  to $97,919  . Pharmacy median costs ranged from $1,188 in a hospital with only one patient in this category to $23,023   to $103,953   at last follow‐up.The second sensitivity analysis revealed that 43 patients in the merged cohort were randomized to C‐MTX within 14 months of the interim analysis, which made them eligible to be re‐routed to a HD‐MTX course. Excluding these patients resulted in similar results to the primary analysis characterized by higher median total inpatient cost for HD MTX  when compared to C‐MTX . No statistically significant difference in total costs existed between these groups . Since the total costs were within a $5000 range, the longer length of stay (LOS) on the DEX arm largely explains the lower daily costs despite the higher total costs.Despite DEX having greater total inpatient costs during Induction, PRED had significantly higher daily costs during that period. Since our analysis on the components of total cost showed that room and board was the greatest driver of total cost, we investigated whether a difference in length of inpatient stay would explain this discrepancy. The median inpatient days during Induction for DEX were 14 (IQR: 8–24) and were 12 (IQR: 6–21) for PRED. This difference was statistically significant (Patients receiving HD‐MTX incurred significantly more inpatient costs than patients on the C‐MTX arm during IM1 and throughout the protocol period. At almost all institutions, HD‐MTX was administered in the inpatient setting, and C‐MTX was given in the outpatient clinic. As we are not capturing outpatient data, it is not surprising that C‐MTX had lower associated costs. The sensitivity analysis revealed that $18,000 to $19,000 would need to be missing in outpatient C‐MTX costs for the HD‐MTX and C‐MTX cost comparison to lose significance. The primary analysis demonstrated that room and board was the greatest cost driver with a median total cost on the HD‐MTX arm during IM 1 of $22,226. It is reasonable to conclude that the missing outpatient costs on the C‐MTX arm do not total the $19,000 necessary to lose statistical significance, because it can be assumed that costs associated with an inpatient stay are greater than $3000 more than outpatient administration costs. Therefore, even though we are missing outpatient administration costs on the C‐MTX arm, we are confident that the significant difference in costs between these arms during IM 1 would persist even if these outpatient costs were able to be included. While the sensitivity analysis addresses the missing outpatient costs, the best approach to this limitation of our dataset would be to input outpatient data from another source, which will be conducted in a future analysis.In investigating the drivers of total cost, room, and board was found to be the greatest contributor to total cost by a considerable margin. Pharmaceutical costs were the next largest driver of cost after inpatient hospital stay. While we did investigate the drivers of cost associated with this ALL therapy, we did not develop a predictive model for cost. To perform predictive modeling, preliminary work must exist to inform variables chosen for the models. No data currently exists in the literature on the cost of upfront ALL therapy; therefore, this data serves a basis to perform predictive modeling in the future.The data specific to inpatient expenditures during IM 1 supports that substantial variation exists across institutions when caring for children receiving the same chemotherapy regimen. Significant differences were demonstrated even in the analysis that only included patients who received HD‐MTX similar to the proposed course schedule. This sub‐analysis should have eliminated patients who suffered toxicity that extended their therapeutic course, resulting in increased costs and prolonged hospital stays. Quality data were not available in the retrospective dataset; however, HD‐MTX, as the arm with the greatest EFS, can service as a proxy for the quality component of value. The variation in adjusted costs to deliver HD‐MTX across hospitals demonstrates that this identical “highest quality” therapy, even in the setting of clinical trials, is administered at a wide variety of costs. Therefore, the greatest quality care can still be administered at a cost saving.Bessaha et al. identified predictors of the length and cost of inpatient stays for adult patients with psychotic disorders using an inpatient database of US community hospitals The primary limitation of this study is the lack of outpatient cost within the PHIS data. This limitation did not impact the cost comparison between DEX versus PRED arms, but the comparison of HD‐MTX versus C‐MTX was vulnerable to the lack of outpatient data, as most patients received C‐MTX as an outpatient. Future comparisons of therapeutic arms that vary in location of care need to consider both inpatient and outpatient costs. Another limitation to the study is generalizability. Patients managed at non‐PHIS sites are not included in the study. Only 30% of patients enrolled on AALL0232 were captured in the COG and PHIS merged cohort. However, the assembled cohort was shown to be similar to the unmerged AALL0232 patients Table .This study is the first to evaluate costs of the treatment of pediatric ALL in the US. Cost data from an administrative dataset can augment the clinical data already collected by the COG and allow accurate estimates of costs of care delivery. The information learned from these merged datasets can serve as a foundation for building value‐based models for care delivery. While the clinically superior arm of HD‐MTX was more expensive during therapy, costs were equivalent between HD‐MTX and C‐MTX at last follow‐up. This convergence of cost likely represents costs associated with salvage therapy in the less efficacious arm; this assumption should be evaluated in future studies. These data support that the most efficacious upfront therapy, even if it is more expensive, is cost‐neutral in the long‐term. The considerable hospital‐level variation in costs and inpatient days to deliver the same therapy found in this study demonstrates that HD‐MTX can be administered in a more cost‐effective manner. Therefore, cost savings can still be achieved without sacrificing survival.None declared.Table S1. Total inpatient follow‐up days by randomized arm.Table S2. Inputting arbitrary costs on C‐MTX arm during IM 1 to determine magnitude of cost difference necessary to maintain HD‐MTX's statistically significant higher costsClick here for additional data file.


The global terrestrial carbon sink has increased since the start of this century at a time of growing carbon emissions from fossil fuel burning. Here we test the hypothesis that increases in atmospheric aerosols from fossil fuel burning enhanced the diffuse light fraction and the efficiency of plant carbon uptake. Using a combination of models, we estimate that at global scale changes in light regimes from fossil fuel aerosol emissions had only a small negative effect on the increase in terrestrial net primary production over the period 1998–2010. Hereby, the substantial increases in fossil fuel aerosol emissions and plant carbon uptake over East Asia were effectively canceled by opposing trends across Europe and North America. This suggests that if the recent increase in the land carbon sink would be causally linked to fossil fuel emissions, it is unlikely via the effect of aerosols but due to other factors such as nitrogen deposition or nitrogen‐carbon interactions. Increase in FF aerosol emission over East Asia contributes substantially to regional increase in plant carbon uptakeThe global impact is small since increases in FF aerosols and NPP in East Asia were canceled by opposing trends in Europe and North AmericaThus, the increase in FF aerosols over East Asia may not act as a mechanism that would explain coincident increase in land carbon sink Using atmospheric models, including an aerosol model with size‐resolved aerosol microphysics, we first simulate aerosol distributions (originating from fossil fuel and fires) and corresponding effects on light regimes over 1998 to 2010. We then use these to drive a land surface model to estimate their relative contributions to changes in regional and global NPP.2Mann et al., Edwards and Slingo, Best et al., Clark et al., Rap et al. [The distribution of anthropogenic aerosols was simulated using a global aerosol model [p et al. .2.1Mann et al., Chipperfield, 2) including fossil fuel and biofuel emissions are taken from the MACCity inventory [Granier et al., 2) are taken from the Global Fire Emissions Database version 3 [van der Werf et al., The aerosol distribution was simulated using the Global Model of Aerosol Processes (GLOMAP) [Mann et al., Reddington et al., Rap et al., Hsu et al., Platnick et al., GLOMAP has been evaluated extensively in previous work and generally found to match ground‐based station observations  well [2.2Edwards and Slingo [Rap et al., Bellouin et al. [Rossow and Schiffer, Rap et al., http://www.fluxdata.org) sites in Europe and North America and also found generally good agreement between observed and modeled light regimes; however, at some of the sites overestimation of total radiation and underestimation of diffuse radiation were apparent  land surface model used here simulates the exchange of carbon, water, energy, and momentum between the land surface and atmosphere [2, and incoming solar radiation due to aerosols associated with (3) anthropogenic emissions, (4) fire emissions, and (5) cloud cover. We started with a “control” simulation in which only climate variables were varied and anthropogenic and fire aerosol emissions remained at year 2000 values to avoid the anomalous 1998 El Niño–Southern Oscillation year, and atmospheric CO2 was held fixed at 1998 levels, whereas cloud cover was based on a climatology for the whole study period 1998–2010. Four additional simulations were carried out whereby in each simulation one additional driver was varied, so that our final simulation had monthly varying fire emissions and cloud cover for the whole period and anthropogenic emissions and the atmospheric CO2 level varied annually. We first calculated the trend (based on linear regression) in annual AOD, surface diffuse radiation (SDR), and NPP for each simulation. The climate effect and combined effect can be inferred directly from the first (only climate varied) and last  model runs. To isolate the impact of the remaining single drivers, the difference between the trends of two simulations that only differ by that driver was used.We performed a set of factorial simulations with JULES over the period 1998–2010 to isolate the impact of single drivers on NPP. The five drivers considered include (1) climate, (2) atmospheric CO3Granier et al., The simulated impact of anthropogenic aerosol emissions on AOD and SDR from 1998 to 2010 is shown in Figure 2 concentrations) to the trend in NPP in the study period 1998–2010. Results show that the spatial patterns in the overall NPP trends  were the most dominant drivers of trends in NPP  led to significant negative contributions to the overall NPP trends  and near‐surface climate (−0.09 Pg C yr−2) playing a dominant role  since the increasing contributions over East Asia are effectively canceled out by the declining contributions from Europe and North America.In Figure P Figure a. Howeve Figures . At globe Figure d. At thi4−2) over the period 1998 to 2010 is largely driven by increasing atmospheric CO2, through a combination of direct CO2 fertilization and the indirect effects of improved water use efficiency in line with previous model studies [Schimel et al., Sitch et al., 2 fertilization effect on trends in NPP should, however, be viewed with caution as more recent studies showed that land surface models may overestimate corresponding impacts considerably [Brienen et al., Smith et al., Zhao and Running, Our results suggest that the simulated increase in global NPP (0.14 Pg C yrLu et al., Granier et al., Büntgen et al., Radiative effects associated with aerosol emissions from FF and fire activity and those related to clouds on trends in NPP played only a minor role at global scale. Our results, however, do show that at more regional levels, FF aerosol emissions and corresponding effects on diffuse radiation are potent drivers of NPP changes, particularly over East Asia where they contribute 33% to the total NPP trend. In this region, the recent trend in fossil fuel aerosol emissions are mainly driven by increases in coal burning and associated sulfate aerosols [Zaehle, 2 fertilization effect [Norby et al., Intergovernmental Panel on Climate Change, Our findings presented here thus indicate that the marked post‐2000 increase in the global land carbon sink may not be explained by changes in light regimes resulting from coincident changes in fossil fuel aerosol emissions and corresponding effects on NPP. This is to a large part a result of the opposing contributions from Asia and from Europe and North America leading to a relatively small global impact. This opens the door for investigations of alternative carbon sink mechanisms that are causally linked to increasing FF emissions. In this regard, nitrogen deposition may act as a potent driver through both its direct effect on photosynthesis, plant respiration, and soil respiration Zaehle,  as well Supporting Information S1Click here for additional data file.