Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

Predicting chick body mass by artificial intelligence-based models

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

Probing functional groups at the gas-aerosol interface using heterogeneous titration reactions: a tool for predicting aerosol health effects?

The complex chemical and physical nature of combustion and secondary organic aerosols (SOAs) in general precludes the complete characterization of both bulk and interfacial components. The bulk composition reveals the history of the growth process and therefore the source region, whereas the interface controls--to a large extent--the interaction with gases, biological membranes, and solid supports. We summarize the development of a soft interrogation technique, using heterogeneous chemistry, for the interfacial functional groups of selected probe gases [N(CH(3))(3), NH(2)OH, CF(3)COOH, HCl, O(3), NO(2)] of different reactivity. The technique reveals the identity and density of surface functional groups. Examples include acidic and basic sites, olefinic and polycyclic aromatic hydrocarbon (PAH) sites, and partially and completely oxidized surface sites. We report on the surface composition and oxidation states of laboratory-generated aerosols and of aerosols sampled in several bus depots. In the latter case, the biomarker 8-hydroxy-2'-deoxyguanosine, signaling oxidative stress caused by aerosol exposure, was isolated. The increase in biomarker levels over a working day is correlated with the surface density N(i)(O3) of olefinic and/or PAH sites obtained from O(3) uptakes as well as with the initial uptake coefficient, γ(0), of five probe gases used in the field. This correlation with γ(0) suggests the idea of competing pathways occurring at the interface of the aerosol particles between the generation of reactive oxygen species (ROS) responsible for oxidative stress and cellular antioxidants.

Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.

Background: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+ BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings.Results: We developed a gene classifier consisting of 181 genes belonging to 13 biological clusters. In the independent set of adjuvantly-treated samples, it was able to define two distinct prognostic groups (HR 2.01 95% CI: 1.29-3.13; p = 0.002). Six of the 13 gene clusters represented pathways involved in cell cycle and proliferation. In 112 metastatic breast cancer patients treated with tamoxifen, one of the classifier components suggesting a cellular inflammatory mechanism was significantly predictive of response.Conclusion: We have developed a gene classifier that can predict clinical outcome in tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the important role of proliferation genes in prognosis, our approach proposes other genes and pathways that may elucidate further mechanisms that influence clinical outcome and prediction of response to tamoxifen.

Transcranial Doppler for predicting delayed cerebral ischemia after subarachnoid hemorrhage.

OBJECTIVE: Transcranial Doppler (TCD) is widely used to monitor the temporal course of vasospasm after subarachnoid hemorrhage (SAH), but its ability to predict clinical deterioration or infarction from delayed cerebral ischemia (DCI) remains controversial. We sought to determine the prognostic utility of serial TCD examination after SAH. METHODS: We analyzed 1877 TCD examinations in 441 aneurysmal SAH patients within 14 days of onset. The highest mean blood flow velocity (mBFV) value in any vessel before DCI onset was recorded. DCI was defined as clinical deterioration or computed tomographic evidence of infarction caused by vasospasm, with adjudication by consensus of the study team. Logistic regression was used to calculate adjusted odds ratios for DCI risk after controlling for other risk factors. RESULTS: DCI occurred in 21% of patients (n = 92). Multivariate predictors of DCI included modified Fisher computed tomographic score (P = 0.001), poor clinical grade (P = 0.04), and female sex (P = 0.008). After controlling for these variables, all TCD mBFV thresholds between 120 and 180 cm/s added a modest degree of incremental predictive value for DCI at nearly all time points, with maximal sensitivity by SAH day 8. However, the sensitivity of any mBFV more than 120 cm/s for subsequent DCI was only 63%, with a positive predictive value of 22% among patients with Hunt and Hess grades I to III and 36% in patients with Hunt and Hess grades IV and V. Positive predictive value was only slightly higher if mBFV exceeded 180 cm/s. CONCLUSION: Increased TCD flow velocities imply only a mild incremental risk of DCI after SAH, with maximal sensitivity by day 8. Nearly 40% of patients with DCI never attained an mBFV more than 120 cm/s during the course of monitoring. Given the poor overall sensitivity of TCD, improved methods for identifying patients at high risk for DCI after SAH are needed.

Predicting human resting-state functional connectivity from structural connectivity

In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks-including their spatial statistics and their persistence across time-can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.

Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model.

BACKGROUND: Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker's background. METHODS: Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients' data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. RESULTS: At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. CONCLUSIONS: Non-RTW may be predicted with a simple model constructed with variables independent of the patient's education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.

Dysmegakaryopoiesis predicting response to therapy in acute myeloid leukaemia. A histologic and clinical study

With standard induction therapy between 50 to 85% of patients with Acute Myeloid Leukaemia (AML) achieve Complete Remission (CR). We investigated whether any morphological feature of bone marrow (BM) plastic embedded biopsies could predict failure of therapy. We reviewed BM plastic embedded biopsies from 54 adult patients presenting with untreated AML. The main histologic parameters analysed were cellularity, dysmegakaryopoiesis (DysM), percentage of marrow blasts and fibrosis. CR was obtained in 34 of 49 treated patients (69%). The rate of CR was significantly lower in the group of patients presenting with DysM: CR was achieved in 54% of the 28 treated patients with DysM and in 90% of the 21 treated patients without DysM (p less than 0.02). Patients with DysM had a significantly lower blood count and bone marrow blasts at presentation. Median age was not significantly different in the 2 groups. Cellularity and fibrosis were not predictive. DysM may be the hallmark of an AML subgroup with distinct clinical behaviour and lower rate of CR with conventional therapy. DysM should be carefully looked for on BM marrow biopsies and aspirate from AML patients at diagnosis.

Empirical models for predicting the production of wild mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees

Mushroom picking has become a widespread autumn recreational activity in the Central Pyrenees and other regions of Spain. Predictive models that relate mushroom production or fungal species richness with forest stand and site characteristics are not available. This study used mushroom production data from 24 Scots pine plots over 3 years to develop a predictive model that could facilitate forest management decisions when comparing silvicultural options in terms of mushroom production. Mixed modelling was used to model the dependence of mushroom production on stand and site factors. The results showed that productions were greatest when stand basal area was approximately 20 m2 ha-1. Increasing elevation and northern aspect increased total mushroom production as well as the production of edible and marketed mushrooms. Increasing slope decreased productions. Marketed Lactarius spp., the most important group collected in the region, showed similar relationships. The annual variation in mushroom production correlated with autumn rainfall. Mushroom species richness was highest when the total production was highest.

Predicting neurological outcome after cardiac arrest.

Purpose of reviewTherapeutic hypothermia and aggressive management of postresuscitation disease considerably improved outcome after adult cardiac arrest over the past decade. However, therapeutic hypothermia alters prognostic accuracy. Parameters for outcome prediction, validated by the American Academy of Neurology before the introduction of therapeutic hypothermia, need further update.Recent findingsTherapeutic hypothermia delays the recovery of motor responses and may render clinical evaluation unreliable. Additional modalities are required to predict prognosis after cardiac arrest and therapeutic hypothermia. Electroencephalography (EEG) can be performed during therapeutic hypothermia or shortly thereafter; continuous/reactive EEG background strongly predicts good recovery from cardiac arrest. On the contrary, unreactive/spontaneous burst-suppression EEG pattern, together with absent N20 on somatosensory evoked potentials (SSEP), is almost 100% predictive of irreversible coma. Therapeutic hypothermia alters the predictive value of serum markers of brain injury [neuron-specific enolase (NSE), S-100B]. Good recovery can occur despite NSE levels >33 mu g/l, thus this cut-off value should not be used to guide therapy. Diffusion MRI may help predicting long-term neurological sequelae of hypoxic-ischemic encephalopathy.SummaryAwakening from postanoxic coma is increasingly observed, despite early absence of motor signs and frank elevation of serum markers of brain injury. A new multimodal approach to prognostication is therefore required, which may particularly improve early prediction of favorable clinical evolution after cardiac arrest.

Acting, predicting and intervening in a socio-hydrological world

This paper asks a simple question: if humans and their actions co-evolve with hydrological systems (Sivapalan et al., 2012), what is the role of hydrological scientists, who are also humans, within this system? To put it more directly, as traditionally there is a supposed separation of scientists and society, can we maintain this separation as socio-hydrologists studying a socio-hydrological world? This paper argues that we cannot, using four linked sections. The first section draws directly upon the concern of science-technology studies to make a case to the (socio-hydrological) community that we need to be sensitive to constructivist accounts of science in general and socio-hydrology in particular. I review three positions taken by such accounts and apply them to hydrological science, supported with specific examples: (a) the ways in which scientific activities frame socio-hydrological research, such that at least some of the knowledge that we obtain is constructed by precisely what we do; (b) the need to attend to how socio-hydrological knowledge is used in decision-making, as evidence suggests that hydrological knowledge does not flow simply from science into policy; and (c) the observation that those who do not normally label themselves as socio-hydrologists may actually have a profound knowledge of socio-hydrology. The second section provides an empirical basis for considering these three issues by detailing the history of the practice of roughness parameterisation, using parameters like Manning's n, in hydrological and hydraulic models for flood inundation mapping. This history sustains the third section that is a more general consideration of one type of socio-hydrological practice: predictive modelling. I show that as part of a socio-hydrological analysis, hydrological prediction needs to be thought through much more carefully: not only because hydrological prediction exists to help inform decisions that are made about water management; but also because those predictions contain assumptions, the predictions are only correct in so far as those assumptions hold, and for those assumptions to hold, the socio-hydrological system (i.e. the world) has to be shaped so as to include them. Here, I add to the ``normal'' view that ideally our models should represent the world around us, to argue that for our models (and hence our predictions) to be valid, we have to make the world look like our models. Decisions over how the world is modelled may transform the world as much as they represent the world. Thus, socio-hydrological modelling has to become a socially accountable process such that the world is transformed, through the implications of modelling, in a fair and just manner. This leads into the final section of the paper where I consider how socio-hydrological research may be made more socially accountable, in a way that is both sensitive to the constructivist critique (Sect. 1), but which retains the contribution that hydrologists might make to socio-hydrological studies. This includes (1) working with conflict and controversy in hydrological science, rather than trying to eliminate them; (2) using hydrological events to avoid becoming locked into our own frames of explanation and prediction; (3) being empirical and experimental but in a socio-hydrological sense; and (4) co-producing socio-hydrological predictions. I will show how this might be done through a project that specifically developed predictive models for making interventions in river catchments to increase high river flow attenuation. Therein, I found myself becoming detached from my normal disciplinary networks and attached to the co-production of a predictive hydrological model with communities normally excluded from the practice of hydrological science.

Conditional equilibrium constants in multicomponent heterogeneous adsorption: The conditional affinity spectrum

The concept of conditional stability constant is extended to the competitive binding of small molecules to heterogeneous surfaces or macromolecules via the introduction of the conditional affinity spectrum (CAS). The CAS describes the distribution of effective binding energies experienced by one complexing agent at a fixed concentration of the rest. We show that, when the multicomponent system can be described in terms of an underlying affinity spectrum [integral equation (IE) approach], the system can always be characterized by means of a CAS. The thermodynamic properties of the CAS and its dependence on the concentration of the rest of components are discussed. In the context of metal/proton competition, analytical expressions for the mean (conditional average affinity) and the variance (conditional heterogeneity) of the CAS as functions of pH are reported and their physical interpretation discussed. Furthermore, we show that the dependence of the CAS variance on pH allows for the analytical determination of the correlation coefficient between the binding energies of the metal and the proton. Nonideal competitive adsorption isotherm and Frumkin isotherms are used to illustrate the results of this work. Finally, the possibility of using CAS when the IE approach does not apply (for instance, when multidentate binding is present) is explored. © 2006 American Institute of Physics.

Affinity distribution functions in multicomponent heterogeneous adsorption. Analytical inversion of isotherms to obtain affinity spectra

An analytical approach for the interpretation of multicomponent heterogeneous adsorption or complexation isotherms in terms of multidimensional affinity spectra is presented. Fourier transform, applied to analyze the corresponding integral equation, leads to an inversion formula which allows the computation of the multicomponent affinity spectrum underlying a given competitive isotherm. Although a different mathematical methodology is used, this procedure can be seen as the extension to multicomponent systems of the classical Sips’s work devoted to monocomponent systems. Furthermore, a methodology which yields analytical expressions for the main statistical properties (mean free energies of binding and covariance matrix) of multidimensional affinity spectra is reported. Thus, the level of binding correlation between the different components can be quantified. It has to be highlighted that the reported methodology does not require the knowledge of the affinity spectrum to calculate the means, variances, and covariance of the binding energies of the different components. Nonideal competitive consistent adsorption isotherm, widely used in metal/proton competitive complexation to environmental macromolecules, and Frumkin competitive isotherms are selected to illustrate the application of the reported results. Explicit analytical expressions for the affinity spectrum as well as for the matrix correlation are obtained for the NICCA case. © 2004 American Institute of Physics.

Synteesikaasun liukoisuus ja aineensiirto fermentointiliuokseen

Työssä tutkittiin synteesikaasun komponenttien: hiilimonoksidin, vedyn ja hiilidioksidin liukoisuutta ja aineensiirtonopeutta fermentointiliuokseen. Kirjallisuusosassa käsitellään kaasujen liukoisuuksiin ja kaasu-nesteaineensiirtoon vaikuttavia tekijöitä ja esitellään korrelaatioita, jotka on kehitetty volumetrisen aineensiirtokertoimen ennustamiseen sekoitetussa fermentorissa. Kirjallisuus-osassa esitetään myös synteesikaasun komponenttien liukoisuudet veteen, etanoliin ja etikkahappoon 37 ºC lämpötilassa ja esitellään Flowbat-simulointiohjelman MHV2-mallin käyttöä kaasu-neste tasapainojen mallin-nuksessa. Työn kokeellisessa osassa tutkittiin synteesikaasun komponenttien liukoisuuksia veteen ja kasvatusalustaan sekä kehitettiin mittausmenetelmä kaasu-neste tasapainojen mittaukseen. Tasapainomittauksissa tutkittiin etanolin ja etikkahapon konsentraatioiden vaikutusta synteesikaasun liukoisuuteen. Lisäksi mallinnettiin kaasu-neste tasapainoja monikomponenttisysteemeissä MHV2-mallin avulla. Kokeellisen osan aineensiirtomittauksissa tutkittiin sekoitusnopeuden ja kaasun volumetrisen syöttönopeuden vaikutusta hiilimonoksidin ja vedyn volumetriseen aineensiirtokertoimeen kLa kahden litran tilavuuksisessa laboratoriofermentorissa. Mittaustulosten perusteella kasvatusalustan komponentit vaikuttavat merkittävästi hiilidioksidin liukoisuuteen. Lisäys etanolin ja etikkahapon konsentraatioissa parantaa hiilimonoksidin liukoisuutta kasvatusalustaan. Kaasun volumetrinen syöttönopeus ja sekoitusnopeus vaikuttavat voimakkaasti volumetrisen aineensiirtokertoimen arvoon. Tutkitussa systeemissä korkein teoreettinen solutiheys, joka voitiin saavuttaa suurimmalla hiilimonoksidin aineensiirto-nopeudella, oli 3 g/L. Tämä on kaksinkertainen verrattuna aiemmissa VTT:n kokeissa saavutettuihin solutiheyksiin.