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Appalachian State University

UniversityBoone, North Carolina, United States

Research output, citation impact, and the most-cited recent papers from Appalachian State University (United States). Aggregated across the NobleBlocks index of 300M+ scholarly works.

Total works
15.6K
Citations
504.1K
h-index
240
i10-index
8.2K
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App StateAppalachian State University

Top-cited papers from Appalachian State University

Regulation of Renal Organic Anion Transporter 3 (SLC22A8) Expression and Function by the Integrity of Lipid Raft Domains and their Associated Cytoskeleton
Chutima Srimaroeng, Jennifer Perry Cecile, Ramsey Walden, John B. Pritchard
2013· Cellular Physiology and Biochemistry3.0Kdoi:10.1159/000350077

BACKGROUND/AIMS: In humans and rodents, organic anion transporter 3 (Oat3) is highly expressed on the basolateral membrane of renal proximal tubules and mediates the secretion of exogenous and endogenous anions. Regulation of Oat3 expression and function has been observed in both expression system and intact renal epithelia. However, information on the local membrane environment of Oat3 and its role is limited. Lipid raft domains (LRD; cholesterol-rich domains of the plasma membrane) play important roles in membrane protein expression, function and targeting. In the present study, we have examined the role of LRD-rich membranes and their associated cytoskeletal proteins on Oat3 expression and function. METHODS: LRD-rich membranes were isolated from rat renal cortical tissues and from HEK-293 cells stably expressing human OAT3 (hOAT3) by differential centrifugation with triton X-100 extraction. Western blots were subsequently analyzed to determine protein expression. In addition, the effect of disruption of LRD-rich membranes was examined on functional Oat3 mediated estrone sulfate (ES) transport in rat renal cortical slices. Cytoskeleton disruptors were investigated in both hOAT3 expressing HEK-293 cells and rat renal cortical slices. RESULTS: Lipid-enriched membranes from rat renal cortical tissues and hOAT3-expressing HEK-293 cells showed co-expression of rOat3/hOAT3 and several lipid raft-associated proteins, specifically caveolin 1 (Cav1), β-actin and myosin. Moreover, immunohistochemistry in hOAT3-expressing HEK-293 cells demonstrated that these LRD-rich proteins co-localized with hOAT3. Potassium iodide (KI), an inhibitor of protein-cytoskeletal interaction, effectively detached cytoskeleton proteins and hOAT3 from plasma membrane, leading to redistribution of hOAT3 into non-LRD-rich compartments. In addition, inhibition of cytoskeleton integrity and membrane trafficking processes significantly reduced ES uptake mediated by both human and rat Oat3. Cholesterol depletion by methyl-β-cyclodextrin (MβCD) also led to a dose dependent reduction Oat3 expression and ES transport by rat renal cortical slices. Moreover, the up-regulation of rOat3-mediated transport seen following insulin stimulation was completely prevented by MβCD. CONCLUSION: We have demonstrated that renal Oat3 resides in LRD-rich membranes in proximity to cytoskeletal and signaling proteins. Disruption of LRD-rich membranes by cholesterol-binding agents or protein trafficking inhibitors altered Oat3 expression and regulation. These findings indicate that the integrity of LRD-rich membranes and their associated proteins are essential for Oat3 expression and function.

Global Carbon Budget 2020
Pierre Friedlingstein, Michael O’Sullivan, Matthew W. Jones, Robbie M. Andrew +4 more
2020· Earth system science data2.6Kdoi:10.5194/essd-12-3269-2020

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions andtheir redistribution among the atmosphere, ocean, and terrestrial biospherein a changing climate – the “global carbon budget” – is important tobetter understand the global carbon cycle, support the development ofclimate policies, and project future climate change. Here we describe andsynthesize data sets and methodology to quantify the five major componentsof the global carbon budget and their uncertainties. Fossil CO2emissions (EFOS) are based on energy statistics and cement productiondata, while emissions from land-use change (ELUC), mainlydeforestation, are based on land use and land-use change data andbookkeeping models. Atmospheric CO2 concentration is measured directlyand its growth rate (GATM) is computed from the annual changes inconcentration. The ocean CO2 sink (SOCEAN) and terrestrialCO2 sink (SLAND) are estimated with global process modelsconstrained by observations. The resulting carbon budget imbalance(BIM), the difference between the estimated total emissions and theestimated changes in the atmosphere, ocean, and terrestrial biosphere, is ameasure of imperfect data and understanding of the contemporary carboncycle. All uncertainties are reported as ±1σ. For the lastdecade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), andELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budgetimbalance BIM of −0.1 GtC yr−1 indicating a near balance betweenestimated sources and sinks over the last decade. For the year 2019 alone, thegrowth in EFOS was only about 0.1 % with fossil emissions increasingto 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEANwas 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminarydata for 2020, accounting for the COVID-19-induced changes in emissions,suggest a decrease in EFOS relative to 2019 of about −7 % (medianestimate) based on individual estimates from four studies of −6 %, −7 %,−7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in thecomponents of the global carbon budget are consistently estimated over theperiod 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for therepresentation of semi-decadal variability in CO2 fluxes. Comparison ofestimates from diverse approaches and observations shows (1) no consensusin the mean and trend in land-use change emissions over the last decade, (2)a persistent low agreement between the different methods on the magnitude ofthe land CO2 flux in the northern extra-tropics, and (3) an apparentdiscrepancy between the different methods for the ocean sink outside thetropics, particularly in the Southern Ocean. This living data updatedocuments changes in the methods and data sets used in this new globalcarbon budget and the progress in understanding of the global carbon cyclecompared with previous publications of this data set (Friedlingstein et al.,2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014,2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

Global Carbon Budget 2022
Pierre Friedlingstein, Michael O’Sullivan, Matthew W. Jones, Robbie M. Andrew +4 more
2022· Earth system science data1.9Kdoi:10.5194/essd-14-4811-2022

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions andtheir redistribution among the atmosphere, ocean, and terrestrial biospherein a changing climate is critical to better understand the global carboncycle, support the development of climate policies, and project futureclimate change. Here we describe and synthesize data sets and methodologies toquantify the five major components of the global carbon budget and theiruncertainties. Fossil CO2 emissions (EFOS) are based on energystatistics and cement production data, while emissions from land-use change(ELUC), mainly deforestation, are based on land use and land-use changedata and bookkeeping models. Atmospheric CO2 concentration is measureddirectly, and its growth rate (GATM) is computed from the annualchanges in concentration. The ocean CO2 sink (SOCEAN) is estimatedwith global ocean biogeochemistry models and observation-baseddata products. The terrestrial CO2 sink (SLAND) is estimated withdynamic global vegetation models. The resulting carbon budget imbalance(BIM), the difference between the estimated total emissions and theestimated changes in the atmosphere, ocean, and terrestrial biosphere, is ameasure of imperfect data and understanding of the contemporary carboncycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, withfossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission(including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1(40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with aBIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low orsinks were too high). The global atmospheric CO2 concentration averaged over2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest anincrease in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %)globally and atmospheric CO2 concentration reaching 417.2 ppm, morethan 50 % above pre-industrial levels (around 278 ppm). Overall, the meanand trend in the components of the global carbon budget are consistentlyestimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadalvariability in CO2 fluxes. Comparison of estimates from multipleapproaches and observations shows (1) a persistent large uncertainty in theestimate of land-use change emissions, (2) a low agreement between thedifferent methods on the magnitude of the land CO2 flux in the northernextratropics, and (3) a discrepancy between the different methods on thestrength of the ocean sink over the last decade. This living data updatedocuments changes in the methods and data sets used in this new globalcarbon budget and the progress in understanding of the global carbon cyclecompared with previous publications of this data set. The data presented inthis work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Global Carbon Budget 2019
Pierre Friedlingstein, Matthew W. Jones, Michael O’Sullivan, Robbie M. Andrew +4 more
2019· Earth system science data1.7Kdoi:10.5194/essd-11-1783-2019

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions andtheir redistribution among the atmosphere, ocean, and terrestrial biosphere– the “global carbon budget” – is important to better understand theglobal carbon cycle, support the development of climate policies, andproject future climate change. Here we describe data sets and methodology toquantify the five major components of the global carbon budget and theiruncertainties. Fossil CO2 emissions (EFF) are based on energystatistics and cement production data, while emissions from land use change(ELUC), mainly deforestation, are based on land use and land use changedata and bookkeeping models. Atmospheric CO2 concentration is measureddirectly and its growth rate (GATM) is computed from the annual changesin concentration. The ocean CO2 sink (SOCEAN) and terrestrialCO2 sink (SLAND) are estimated with global process modelsconstrained by observations. The resulting carbon budget imbalance(BIM), the difference between the estimated total emissions and theestimated changes in the atmosphere, ocean, and terrestrial biosphere, is ameasure of imperfect data and understanding of the contemporary carboncycle. All uncertainties are reported as ±1σ. For the lastdecade available (2009–2018), EFF was 9.5±0.5 GtC yr−1,ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budgetimbalance BIM of 0.4 GtC yr−1 indicating overestimated emissionsand/or underestimated sinks. For the year 2018 alone, the growth in EFF wasabout 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history,ELUC was 1.5±0.7 GtC yr−1, for total anthropogenicCO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of−0.2 % to 1.5 %) based on national emissions projections for China, theUSA, the EU, and India and projections of gross domestic product correctedfor recent changes in the carbon intensity of the economy for the rest ofthe world. Overall, the mean and trend in the five components of the globalcarbon budget are consistently estimated over the period 1959–2018, butdiscrepancies of up to 1 GtC yr−1 persist for the representation ofsemi-decadal variability in CO2 fluxes. A detailed comparison amongindividual estimates and the introduction of a broad range of observationsshows (1) no consensus in the mean and trend in land use change emissionsover the last decade, (2) a persistent low agreement between the differentmethods on the magnitude of the land CO2 flux in the northernextra-tropics, and (3) an apparent underestimation of the CO2variability by ocean models outside the tropics. This living data updatedocuments changes in the methods and data sets used in this new globalcarbon budget and the progress in understanding of the global carbon cyclecompared with previous publications of this data set (Le Quéré etal., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated bythis work are available at https://doi.org/10.18160/gcp-2019 (Friedlingsteinet al., 2019).

Global Carbon Budget 2021
Pierre Friedlingstein, Matthew W. Jones, Michael O’Sullivan, Robbie M. Andrew +4 more
2022· Earth system science data1.6Kdoi:10.5194/essd-14-1917-2022

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions andtheir redistribution among the atmosphere, ocean, and terrestrial biospherein a changing climate is critical to better understand the global carboncycle, support the development of climate policies, and project futureclimate change. Here we describe and synthesize datasets and methodology toquantify the five major components of the global carbon budget and theiruncertainties. Fossil CO2 emissions (EFOS) are based on energystatistics and cement production data, while emissions from land-use change(ELUC), mainly deforestation, are based on land use and land-use changedata and bookkeeping models. Atmospheric CO2 concentration is measureddirectly, and its growth rate (GATM) is computed from the annualchanges in concentration. The ocean CO2 sink (SOCEAN) is estimatedwith global ocean biogeochemistry models and observation-baseddata products. The terrestrial CO2 sink (SLAND) is estimated withdynamic global vegetation models. The resulting carbon budget imbalance(BIM), the difference between the estimated total emissions and theestimated changes in the atmosphere, ocean, and terrestrial biosphere, is ameasure of imperfect data and understanding of the contemporary carboncycle. All uncertainties are reported as ±1σ. For the firsttime, an approach is shown to reconcile the difference in our ELUCestimate with the one from national greenhouse gas inventories, supportingthe assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, withfossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLANDwas 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. Theglobal atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOSrelative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budgetare consistently estimated over the period 1959–2020, but discrepancies ofup to 1 GtC yr−1 persist for the representation of annual tosemi-decadal variability in CO2 fluxes. Comparison of estimates frommultiple approaches and observations shows (1) a persistent largeuncertainty in the estimate of land-use changes emissions, (2) a lowagreement between the different methods on the magnitude of the landCO2 flux in the northern extra-tropics, and (3) a discrepancy betweenthe different methods on the strength of the ocean sink over the lastdecade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understandingof the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; LeQuéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). Thedata presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Choosing Qualitative Research: A Primer for Technology Education Researchers
Marie Hoepfl
1997· Journal of Technology Education1.4Kdoi:10.21061/jte.v9i1.a.4

Scholarly Communication is located on the fourth floor of Carol M. Newman Library at Virginia Tech. Scholarly Communication is a dynamic landscape, and we are continually evolving. Many scholarly communications activities have spun-off into their own departments, such as VT Publishing and Digital Imaging and Preservation Services, and Digital Library Development. Our focus is on supporting the creation and dissemination of scholarship.

The compelling link between physical activity and the body's defense system
David C. Nieman, Laurel M. Wentz
2018· Journal of sport and health science/Journal of Sport and Health Science1.3Kdoi:10.1016/j.jshs.2018.09.009

This review summarizes research discoveries within 4 areas of exercise immunology that have received the most attention from investigators: (1) acute and chronic effects of exercise on the immune system, (2) clinical benefits of the exercise-immune relationship, (3) nutritional influences on the immune response to exercise, and (4) the effect of exercise on immunosenescence. These scientific discoveries can be organized into distinctive time periods: 1900-1979, which focused on exercise-induced changes in basic immune cell counts and function; 1980-1989, during which seminal papers were published with evidence that heavy exertion was associated with transient immune dysfunction, elevated inflammatory biomarkers, and increased risk of upper respiratory tract infections; 1990-2009, when additional focus areas were added to the field of exercise immunology including the interactive effect of nutrition, effects on the aging immune system, and inflammatory cytokines; and 2010 to the present, when technological advances in mass spectrometry allowed system biology approaches (i.e., metabolomics, proteomics, lipidomics, and microbiome characterization) to be applied to exercise immunology studies. The future of exercise immunology will take advantage of these technologies to provide new insights on the interactions between exercise, nutrition, and immune function, with application down to the personalized level. Additionally, these methodologies will improve mechanistic understanding of how exercise-induced immune perturbations reduce the risk of common chronic diseases.

The Generalizability of Survey Experiments
Kevin Mullinix, Thomas J. Leeper, James Druckman, Jeremy Freese
2015· Journal of Experimental Political Science1.3Kdoi:10.1017/xps.2015.19

Abstract Survey experiments have become a central methodology across the social sciences. Researchers can combine experiments’ causal power with the generalizability of population-based samples. Yet, due to the expense of population-based samples, much research relies on convenience samples (e.g. students, online opt-in samples). The emergence of affordable, but non-representative online samples has reinvigorated debates about the external validity of experiments. We conduct two studies of how experimental treatment effects obtained from convenience samples compare to effects produced by population samples. In Study 1, we compare effect estimates from four different types of convenience samples and a population-based sample. In Study 2, we analyze treatment effects obtained from 20 experiments implemented on a population-based sample and Amazon's Mechanical Turk (MTurk). The results reveal considerable similarity between many treatment effects obtained from convenience and nationally representative population-based samples. While the results thus bolster confidence in the utility of convenience samples, we conclude with guidance for the use of a multitude of samples for advancing scientific knowledge.

Prevention, Diagnosis, and Treatment of the Overtraining Syndrome
Romain Meeusen, Martine Duclos, Carl Foster, Andrew C. Fry +4 more
2013· Medicine & Science in Sports & Exercise1.2Kdoi:10.1249/mss.0b013e318279a10a

Romain Meeusen, Belgium (Chair) Martine Duclos, France Carl Foster, United States Andrew Fry, United States Michael Gleeson, United Kingdom David Nieman, United States John Raglin, United States Gerard Rietjens, the Netherlands Jürgen Steinacker, Germany Axel Urhausen, Luxembourg ABSTRACT Successful training not only must involve overload but also must avoid the combination of excessive overload plus inadequate recovery. Athletes can experience short-term performance decrement without severe psychological or lasting other negative symptoms. This functional overreaching will eventually lead to an improvement in performance after recovery. When athletes do not sufficiently respect the balance between training and recovery, nonfunctional overreaching (NFOR) can occur. The distinction between NFOR and overtraining syndrome (OTS) is very difficult and will depend on the clinical outcome and exclusion diagnosis. The athlete will often show the same clinical, hormonal, and other signs and symptoms. A keyword in the recognition of OTS might be “prolonged maladaptation” not only of the athlete but also of several biological, neurochemical, and hormonal regulation mechanisms. It is generally thought that symptoms of OTS, such as fatigue, performance decline, and mood disturbances, are more severe than those of NFOR. However, there is no scientific evidence to either confirm or refute this suggestion. One approach to understanding the etiology of OTS involves the exclusion of organic diseases or infections and factors such as dietary caloric restriction (negative energy balance) and insufficient carbohydrate and/or protein intake, iron deficiency, magnesium deficiency, allergies, and others together with identification of initiating events or triggers. In this article, we provide the recent status of possible markers for the detection of OTS. Currently, several markers (hormones, performance tests, psychological tests, and biochemical and immune markers) are used, but none of them meet all the criteria to make their use generally accepted. Key Words: OVERTRAINING SYNDROME, OVERREACHING, TRAINING, PERFORMANCE, UNDERPERFORMANCE The goal in training competitive athletes is to provide training loads that are effective in improving performance. During this process, athletes may go through several stages within a competitive season of periodized training. These phases of training range from insufficient training, during the period between competitive seasons or during active rest and taper, to “overreaching” (OR) and “overtraining” (OT), which includes maladaptations and diminished competitive performance. Literature on “OT” has increased enormously; however, the major difficulty is the lack of common and consistent terminology as well as a gold standard for the diagnosis of OT syndrome (OTS). In 2006, the European College of Sport Science (ECSS) published its consensus statement on OT (94). We decided to write an update and to ask the American College of Sports Medicine to provide input in this article so that this can be considered as a mutual “consensus statement” of both international organizations. In this “consensus statement,” we will present the current state of knowledge on the OTS going through its definition, diagnosis, treatment, and prevention. DEFINITION Successful training must involve overload but also must avoid the combination of excessive overload with inadequate recovery. The process of intensifying training is commonly used by athletes in an attempt to enhance performance. As a consequence, the athlete may experience acute feelings of fatigue and decreases in performance as a result of a single intense training session or an intense training period. The resultant acute fatigue after an adequate rest period can be followed by a positive adaptation or improvement in performance and is the basis of effective training programs. However, if the balance between appropriate training stress and adequate recovery is disrupted, an abnormal training may and a state of may the evidence for a after of training is not recent to the of for the of OT and of training and/or stress in short-term decrement in performance with or without and psychological signs and symptoms of in which of performance may from several to several of training and/or stress in decrement in performance with or without and psychological signs and symptoms of in which of performance may several or As by several that the between OT and is the of for performance and not the or of training stress or of These also that there may be an of psychological signs with the is possible to from a state of within a period may be that this is a and of the training However, athletes are in an state may or to The difficulty in the that might between athletes and those an The also that show and that the OT may be an avoid of we the and OTS on the used by and and and In “OT” is used as a a process of training with possible of short-term or OTS. the we the etiology and that is not the of the is often used by athletes during a training to enhance performance. training can result in a in however, appropriate of recovery are a may with the athlete an performance with This process is often used going on a and will lead to a performance which is followed by performance. In this the will the stress This of short-term can also be When this the athletes can a state of or which will lead to a or in performance that will not for several or However, athletes will be to after that not only the on the is by the in training but also signs and symptoms of psychological and/or This is in with the of and recent a In the stages that training from and and from OTS are can be as a process of overload that is used to which in acute fatigue to an improvement in performance. When training or athletes use a short-term period training to training can experience short-term performance decrement without severe psychological or lasting other negative symptoms. This short-term will eventually lead to an improvement in performance after recovery. However, athletes do not sufficiently respect the balance between training and recovery, NFOR can occur. this the signs and symptoms of training such as performance psychological increased and hormonal will and the athletes will or to factors such as inadequate and/or carbohydrate infections and and may be this the distinction between NFOR and OTS is very difficult and will depend on the clinical outcome and exclusion diagnosis. The athlete will often show the same clinical, hormonal, and other signs and symptoms. the diagnosis of OTS can often only be the can be A keyword in the recognition of OTS might be “prolonged maladaptation” not only of the athlete but also of several biological, neurochemical, and hormonal regulation of the stages of training, and between performance and performance to is This to and biochemical The OTS is by the that the clinical are from to and are and in recent the knowledge of of OTS has there is a for for the diagnosis of OTS. OTS is by a in performance together with in mood This a period of recovery lasting several or there is no to an athlete as OTS, the to the diagnosis can only be by all other possible on in performance and mood if no for the can be OTS is and recognition of OTS is the only is a in performance during or training. The diagnosis of OTS the exclusion of an organic or and iron with or diseases and major or such as and also be However, be that and clinical to and OTS can other The between and is very difficult to In is generally thought that symptoms of OTS, such as fatigue, performance decline, and mood disturbances, are more severe than those of However, there is no scientific evidence to either confirm or refute this suggestion. there is no evidence that the athlete is OTS. In in the that a state of of the and biochemical to the increased training with in and others and the of and OTS and not This is also the signs and symptoms of OTS are and is not and to an athlete in such a that will OTS. are and only on OTS. One approach to understanding the etiology of OTS involves the exclusion of organic diseases or infections and factors such as dietary caloric restriction (negative energy balance) and insufficient carbohydrate and/or protein intake, iron deficiency, magnesium deficiency, allergies, and so on together with identification of initiating events or triggers. One of the is a training in an between and recovery. possible might be the of training, and and of commonly are and However, scientific evidence is not for of triggers. such as or infections may to or OTS but might not be present the the athlete to a possible initiating events has not the of the OTS. that the OTS be as the syndrome that on the of in OTS than on the mechanisms. This terminology has not the Athletes and the of in if a for the diagnosis of OTS. no this but there is a for a combination of to possible markers for OTS. there is a for a detection for training loads as well as other can the However, this is not which eventually to OTS. of and there are several possible recent on for the the OTS. more are a of athletes may the is or are It is difficult to on not all the of and other athletes a training a of of in other but are by of and OTS. The of the of an of of and of of OTS, with a of in of OTS by a from with OTS common and a in athletes can be to or but a recent of with the to These both the of OTS for athletes and the of in OTS there is evidence that athletes OTS are a of In a of that of the OTS during their training season with OTS in or more of the of training. In only of of OTS during their of a diagnosis of OTS This in the for has in athletes the same overload training. In a of competitive of training the same and the training but others the training and athletes of mood and of so by the training that to be from the It that are to the OTS to overload training or to the OTS the of of psychological factors and not the of OTS to be by or OT OTS the attempt of the to with and other that OTS the of such as training, to to and of and OTS can be within the of the of to this the is to the stress The and all to and enhance the of the to stress may lead to to on the as well as on the with the either to an or increased or and adaptation in will the that the state of or OTS the of the It has that acute stress not only but also and stress and the an in the of to acute has that in acute and the of The lack of criteria for OTS is in of the and “OT” by a lack of consistent are several criteria that a for the of the OTS must the be to the training and be by other factors and in the the of the OTS, and in to acute be from the be to with a of the not are not well and not the be rest from or of in not to with the training However, none of the or markers meet all of In training, to and are in and as well as the of and is this is of the of OTS, is athletes are is also not may can be on the training status of the factors that are in are the status and possible decreases in and to increased training. One consistent in and athletes the OTS, is a diminished or increased of which to and with lasting from several to to a and/or rest may provide an and/or but are not to an or OTS state with not only during of but also and after single to a with a to the the positive to training for several of with short-term of performance and mood well as a and a to after of training in well The of has as a possible of excessive training stress However, not all a during of increased training and OT and are not a of in OTS, other the as an of of the and are not of or OTS, are in on the status of the athlete and in the The with are as are within the of the and depend on the of the are in may with increased training but is not a consistent in OTS. several has that a hormonal during the of the OTS, and that of to the OTS The of the to this is from of factors that the of intake, after the of may the hormonal In and/or detection of the used may between of regulation functional that are considered and and are and the of is the of all are by the by other or This is the of is for the of hormonal also the that to regulation are by more than a single in a a the considered as an of the This decreases in to the and of training, and is that this only the of training and be used for diagnosis of or OTS of the that and OTS must be on a with a an and a of the and all other the adaptation to training is by increased only during recovery to to and by of to However, be that during a in of is and in are to those of an of the this is in with the in the of as in the of and are during and with and can be that is not a is no consensus or of for the of the training and/or an an a or no of a with training, or OTS. other than training and result in between factors and of and the between or and performance or training is is to use in as a to training In OTS, a in and in to a is the acute hormonal to the to the by the OTS is not that can be used for diagnosis is A of is by the and has to with in These on the and are in the hormonal regulation of and training However, the same to energy deficiency, which can be with training and/or of the training status or of energy the stress and to and might also be of the factors that can lead to the of OTS. 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However, more scientific are to which of are the for The with psychological are as state and other factors can be by to training and recovery. if on psychological use mood or of and training be more can be or by of or must be with the appropriate and on the training must be with state of mood can be by to be to the of psychological to and athletes may be or be in the and of the several as to which might be of or OTS. after increased training may be the result of of a to and of in or may be the result of a with other in might be a of a in and not to and be that of are not in athletes OTS has used as a of with an in an in to the of training on of but to in or OTS with either no or in increased the training of by a training to fatigue, and and all in to the however, all of there no in or the of to both in the and after no in and balance in athletes after of training. This the of balance by and In both the and in the during rest and in to that in the a to in to training in the as This may and/or increased However, in this increased and in a single athlete with In with during the same the an in and in the during training, which after recovery. The in to be the result of increased that the after to training with the of performance after in but to there are no published from athletes in or OTS. In a very recent in a of a of after and an of of with positive to training, but the not of markers not of a performance A that short-term overload training in an increased and a between and However, this no the after training lasting the only a the often in a clinical that such as or other in athletes be by a state of or OTS, this not by or OTS. However, be that an by the in an athlete in training may the to a of a and The with are as to be a in but not provide consistent One to be as an outcome there are to and the Currently, there is no consensus the and the of The present do not to between in from and OTS. are on to increased training, and also in and OTS It that training to or may both the of the and the of the resultant However, the of scientific to is are that of and has but on the immune to in and and in stress and and The immune and stress are with an of during the period after competitive and events These that immune and increased may result training is to and OTS, but to this that the of of training on immune and on to that several of to be to the training A period of training in with a in the In and and and of rest after of training in and to be after in the training in athletes a in A with training, and not a negative between and of with of training, several of both and are of or and to may to in athletes in immune during of training However, this often involves not only but also dietary energy deficiency, and psychological These are to a of that the that athletes to a training the of a to competitive a of of both and In commonly either the of the season or after the of training and/or athletes are not immune is possible that the of in several immune may to common such as immune with training may to of major However, might be that the increased the increased stress with increased training, of the of the athlete to the increased symptoms of by athletes may be to from than with a that immune is in athletes OTS is of insufficient scientific However, from athletes and of an increased with OTS by a In a of athletes the more than of the athletes symptoms of “OT” with with none of the athletes in the In during and after a training of the In a by competitive athletes with fatigue and performance a which with the to fatigue and/or infections in of the The common immune and of in of the athletes a to period has to mood state and that a of athletes are as OTS may experience increased are only a of in immune status in athletes with athletes and on athletes to of with and training. training, the However, this has not to be in athletes as OTS with One has that the of other on the of to be to between the of athletes and The of on not the of in athletes OTS with this “OT” be with and However, is a of and of on may be of the of acute which of a possible of the a in markers in of infections are not in but may be this in fatigue and in training and might be of the factors that can lead to the of OTS, or in the diagnosis of OTS be from a state of fatigue such as that with of In the OTS is to for and infections and diseases with the in OTS. It is in the in the of OTS, a of can be which may to the of symptoms. However, that this distinction between and may be in may in the and of fatigue and of OTS in When training the of in performance a for OTS. The with the in of other major diseases that are to are performance are and possible and diseases are This is on the evidence together with the of the is that the immune is to and immune be used as an of stress in to training. The current the immune and that of training result in immune with or no in However, immune in to increased training

Signals, Regulatory Networks, and Materials That Build and Break Bacterial Biofilms
Ece Karatan, Paula I. Watnick
2009· Microbiology and Molecular Biology Reviews1.0Kdoi:10.1128/mmbr.00041-08

Biofilms are communities of microorganisms that live attached to surfaces. Biofilm formation has received much attention in the last decade, as it has become clear that virtually all types of bacteria can form biofilms and that this may be the preferred mode of bacterial existence in nature. Our current understanding of biofilm formation is based on numerous studies of myriad bacterial species. Here, we review a portion of this large body of work including the environmental signals and signaling pathways that regulate biofilm formation, the components of the biofilm matrix, and the mechanisms and regulation of biofilm dispersal.

Hardnose the Dictator
Todd L. Cherry, Peter Frykblom, Jason F. Shogren
2002· American Economic Review905doi:10.1257/00028280260344740

Lab experiments have gone to extremes to isolate and repress other-regarding behavior in extensive-form bargaining games, with limited success. Consider, for example, Elizabeth Hoffman et al.’s (1996; hereafter HMS) Anonymous Dictator game. This game controls self-interested strategic behavior by giving a person complete control over the distribution of wealth, and complete anonymity from all others including the experimenter. While theory predicts people with complete control and complete anonymity will offer up nothing to others, in fact they still share the wealth in about 40 percent of the observed bargains. Such other-regarding choice is another example in which individual behavior differs from that predicted by subgame perfection, and supports the call for a new “behavioral game theory” (Colin F. Camerer, 1997). Herein we extend the work of HMS to reveal a setting in which 95 percent of dictators follow game-theoretic predictions. In contrast to previous studies, our design has people bargain over earned wealth rather than unearned wealth granted by the experimenter. We argue that just as rewards must be salient (Kyung Hwan Baik et al., 1999), the assets in a bargain must be legitimate to produce rational behavior. Our results support this conjecture. Dictators bargaining over earned wealth were more selfinterested than observed in previous studies; and when they had complete anonymity, selfless behavior is essentially eliminated.

Formal Organizational Initiatives and Informal Workplace Practices: Links to Work-Family Conflict and Job-Related Outcomes
Stella E. Anderson, Betty S. Coffey, Robin T. Byerly
2002· Journal of Management900doi:10.1177/014920630202800605

Many organizations have implemented a variety of initiatives to address work-family conflict issues. This study investigates the impact of formal and informal work-family practices on both work-to-family and family-to-work conflict (WFC, FWC) and a broad set of job-related outcomes. We utilized structural equation modeling to analyze data from the 1997 National Study of the Changing Workforce (NSCW). Results showed that negative career consequences and lack of managerial support were significantly related to work-to-family conflict. These were significant predictors of conflict even when accounting for the effects of work schedule flexibility. Work-to-family conflict was linked to job dissatisfaction, turnover intentions and stress, while family-to-work conflict was linked to stress and absenteeism. There were no apparent differences between women and men in terms of the observed relationships.

Evidence-Based Secondary Transition Predictors for Improving Postschool Outcomes for Students With Disabilities
David W. Test, Valerie L. Mazzotti, April L. Mustian, Catherine Fowler +2 more
2009· Career Development for Exceptional Individuals875doi:10.1177/0885728809346960

The purpose of this study was to conduct a systematic review of the secondary transition correlational literature to identify in-school predictors of improved postschool outcomes in the areas of education, employment, and/or independent living for students with disabilities. Based on results of this review, 16 evidence-based, in-school predictors of postschool outcomes were identified. Of the 16 predictors, 4 (25%) predicted improved outcomes in all three postschool outcome areas, 7 (43.8%) predicted improved outcomes for only postschool education and employment, and 5 (31.3%) predicted improved outcomes for employment only. Limitations and implications for future research and practice are discussed.

Entrepreneurial Perceptions and Intentions: The Role of Gender and Culture
Rachel S. Shinnar, Olivier Giacomin, Frank Janssen
2012· Entrepreneurship Theory and Practice867doi:10.1111/j.1540-6520.2012.00509.x

This paper examines how culture and gender shape entrepreneurial perceptions and intentions within Hofstede's cultural dimensions framework and gender role theory. We test whether gender differences exist in the way university students in three nations perceive barriers to entrepreneurship and whether gender has a moderating effect on the relationship between perceived barriers and entrepreneurial intentions across nations. Findings indicate significant gender differences in barrier perceptions. However, this gap is not consistent across cultures. Also, a moderating effect of gender on the relationship between barriers and entrepreneurial intentions is identified. Implications for research and practice are discussed.

Children with Minimal Sensorineural Hearing Loss: Prevalence, Educational Performance, and Functional Status
Fred H. Bess, Jeanne Dodd-Murphy, Robert A. Parker
1998· Ear and Hearing851doi:10.1097/00003446-199810000-00001

OBJECTIVE: This study was designed to determine the prevalence of minimal sensorineural hearing loss (MSHL) in school-age children and to assess the relationship of MSHL to educational performance and functional status. DESIGN: To determine prevalence, a single-staged sampling frame of all schools in the district was created for 3rd, 6th, and 9th grades. Schools were selected with probability proportional to size in each grade group. The final study sample was 1218 children. To assess the association of MSHL with educational performance, children identified with MSHL were assigned as cases into a subsequent case-control study. Scores of the Comprehensive Test of Basic Skills (4th Edition) (CTBS/4) then were compared between children with MSHL and children with normal hearing. School teachers completed the Screening Instrument for Targeting Education Risk (SIFTER) and the Revised Behavior Problem Checklist for a subsample of children with MSHL and their normally hearing counterparts. Finally, data on grade retention for a sample of children with MSHL were obtained from school records and compared with school district norm data. To assess the relationship between MSHL and functional status, test scores of all children with MSHL and all children with normal hearing in grades 6 and 9 were compared on the COOP Adolescent Chart Method (COOP), a screening tool for functional status. RESULTS: MSHL was exhibited by 5.4% of the study sample. The prevalence of all types of hearing impairment was 11.3%. Third grade children with MSHL exhibited significantly lower scores than normally hearing controls on a series of subtests of the CTBS/4; however, no differences were noted at the 6th and 9th grade levels. The SIFTER results revealed that children with MSHL scored poorer on the communication subtest than normal-hearing controls. Thirty-seven percent of the children with MSHL failed at least one grade. Finally, children with MSHL exhibited significantly greater dysfunction than children with normal hearing on several subtests of the COOP including behavior, energy, stress, social support, and self-esteem. CONCLUSIONS: The prevalence of hearing loss in the schools almost doubles when children with MSHL are included. This large, education-based study shows clinically important associations between MSHL and school behavior and performance. Children with MSHL experienced more difficulty than normally hearing children on a series of educational and functional test measures. Although additional research is necessary, results suggest the need for audiologists, speech-language pathologists, and educators to evaluate carefully our identification and management approaches with this population. Better efforts to manage these children could result in meaningful improvement in their educational progress and psychosocial well-being.

Psychometrics: An Introduction.
R. Michael Furr, Verne R. Bacharach
2007791

1. Psychometrics and the Importance of Psychological Measurement Observable Behavior and Unobservable Attributes Psychological Tests: Definition and Types Psychometrics Challenges to Measurement in Psychology Theme: The Importance of Individual Differences 2. Scaling Fundamental Issues and Numbers Units of Measurement Additivity and Counting Four Scales of Measurement Summary Suggested Readings 3. Individual Differences and Correlations The Nature of Variability Importance of Individual Differences Variability and Distribution of Distribution Shapes and Normal Distributions Quantifying the Association Between Distributions Variance of Composite Scores Interpreting Test Test Norms Summary Suggested Readings 4. Test Dimensionality and Factor Analysis Test Dimensionality Factor Analysis: Examining the Dimensionality of a Test Summary Suggested Readings 5. Reliability: Conceptual Basis Overview of Reliability and Classical Test Theory Observed Scores, True and Measurement Error Variances in Observed Scores, True and Error Four Ways to Think of Reliability Reliability and the Standard Error of Measurement Parallel Tests Summary 6. Empirical Estimates of Reliability Alternate Forms Reliability Test-Retest Reliability Internal Consistency Reliability Factors Affecting the Reliability of Test Sample Homogeneity and Reliability Generalization Reliability of Difference Summary 7. Importance of Reliability Behavioral Research Applied Behavioral Practice: Evaluation of an Individual's Test Score Test Construction and Refinement Summary Suggested Readings 8. Validity: The Conceptual Basis What is Validity? Validity Evidence: Test Content Validity Evidence: Internal Structure of the Test Validity Evidence: Response Processes Validity Evidence: Associations with Other Variables Validity Evidence: Consequences of Testing Other Perspectives on Validity Contrasting Reliability and Validity The Importance of Validity Summary Suggested Readings 9. Validity: Estimating and Evaluating Convergent and Discriminant Validity Methods for Evaluating Convergent and Discriminant Validity Factors Affecting a Validity Coefficient Interpreting a Validity Coefficient Summary Suggested Readings 10. Response Biases Types of Response Biases Methods for Coping with Response Biases Response Biases, Response Sets, and Response Styles Summary Suggested Readings 11. Test Bias Why Worry about Test Score Bias Detecting Construct Bias: Internal Evaluation of a Test Detecting Predictive Bias: External Evaluation of a Test Summary Suggested Readings 12. Generalizability Theory Multiple Facets of Measurement Generalizability and Variance Components G Studies and D Studies Conducting and Interpreting Generalizability Theory Analysis: A One-facet Design Conducting and Interpreting Generalizability Theory Analysis: A Two-facet Design Other Measurement Designs Summary Footnote Suggested Readings 13. Item Response Theory and Rasch Models Basics of IRT IRT Measurement Models An Example of IRT: A Rasch Model Item and Test Information Applications of IRT Summary Suggested Readings

The global carbon budget 1959–2011
Corinne Le Quéré, R. J. Andres, T. A. Boden, T. J. Conway +4 more
2013· Earth system science data761doi:10.5194/essd-5-165-2013

Abstract. Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002–2011), EFF was 8.3 ± 0.4 PgC yr−1, ELUC 1.0 ± 0.5 PgC yr−1, GATM 4.3 ± 0.1 PgC yr−1, SOCEAN 2.5 ± 0.5 PgC yr−1, and SLAND 2.6 ± 0.8 PgC yr−1. For year 2011 alone, EFF was 9.5 ± 0.5 PgC yr−1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 ± 0.5 PgC yr−1, approximately constant throughout the decade; GATM was 3.6 ± 0.2 PgC yr−1, SOCEAN was 2.7 ± 0.5 PgC yr−1, and SLAND was 4.1 ± 0.9 PgC yr−1. GATM was low in 2011 compared to the 2002–2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9–3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future. All data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_V2013). Global carbon budget 2013

Telepresence via Television: Two Dimensions of Telepresence May Have Different Connections to Memory and Persuasion.[1]
Tae-Yong Kim, Frank Biocca
2006· Journal of Computer-Mediated Communication739doi:10.1111/j.1083-6101.1997.tb00073.x

To be truly useful for media theory, the concept of presence should be applicable to all forms of virtual environments including those of traditional media like television and traditional content such as advertising. This study reports the results of an experiment on the effects of the visual angle of the display (sensory saturation) and room illumination (sensory suppression) on the sensation of telepresence during normal television viewing. A self-report measure of presence yielded two factors. Using [Gerrig's (1993)] terminology for the sense of being transported to a mediated environments, we labeled the two factors “arrival,” for the feeling of being there in the virtual environment, and “departure,” for the feeling of not being there in the in physical environment. It appears that being in the virtual environment is not equivalent to not being in the physical environment. A path analysis found that these two factors have very different relationships to viewer memory for the experience and for attitude change (i.e., buying intention and confidence in product decision). We theorize that the departure factor may be measuring the feeling that the medium has disappeared and may constitute a deeper absorption into the virtual environment. The study did not find evidence that visual angle and room illumination affected the sensation of telepresence

Global carbon budget 2014
Corinne Le Quéré, R. Moriarty, Robbie M. Andrew, Glen P. Peters +4 more
2015· Earth system science data735doi:10.5194/essd-7-47-2015

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover-change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2004–2013), EFF was 8.9 ± 0.4 GtC yr−1, ELUC 0.9 ± 0.5 GtC yr−1, GATM 4.3 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 2.9 ± 0.8 GtC yr−1. For year 2013 alone, EFF grew to 9.9 ± 0.5 GtC yr−1, 2.3% above 2012, continuing the growth trend in these emissions, ELUC was 0.9 ± 0.5 GtC yr−1, GATM was 5.4 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and SLAND was 2.5 ± 0.9 GtC yr−1. GATM was high in 2013, reflecting a steady increase in EFF and smaller and opposite changes between SOCEAN and SLAND compared to the past decade (2004–2013). The global atmospheric CO2 concentration reached 395.31 ± 0.10 ppm averaged over 2013. We estimate that EFF will increase by 2.5% (1.3–3.5%) to 10.1 ± 0.6 GtC in 2014 (37.0 ± 2.2 GtCO2 yr−1), 65% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the global economy. From this projection of EFF and assumed constant ELUC for 2014, cumulative emissions of CO2 will reach about 545 ± 55 GtC (2000 ± 200 GtCO2) for 1870–2014, about 75% from EFF and 25% from ELUC. This paper documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this living data set (Le Quéré et al., 2013, 2014). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2014).

Minimum LM Unit Root Test with One Structural Break
Junsoo Lee, Mark C. Strazicich
2004· RePEc: Research Papers in Economics628

In this paper, we propose a minimum LM unit root test that endogenously determines a structural break in intercept and trend. Critical values are provided, and size and power properties are compared to the endogenous one-break unit root test of Zivot and Andrews (1992). Nunes, Newbold, and Kuan (1997) and Lee and Strazicich (2001) previously demonstrated that the Zivot and Andrews test exhibits size distortions in the presence of a break under the null. In contrast, the one-break minimum LM unit root test exhibits no size distortions in the presence of a break under the null. As such, rejection of the null unambiguously implies a trend stationary process.