E411 ESAPP Project Report 2004

Annual project report. Project description, budget, impact, achievement of objectives and outputs, and appraisal by Management Team.

E605 ESAPP Project Report 2004

Annual project report. Project description, budget, impact, achievement of objectives and outputs, and appraisal by Management Team.

E603 ESAPP Project Report 2004

Annual project report. Project description, budget, impact, achievement of objectives and outputs, and appraisal by Management Team.

E606 ESAPP Project Report 2004

Annual project report. Project description, budget, impact, achievement of objectives and outputs, and appraisal by Management Team.

E607 ESAPP Project Report 2004

Annual project report. Project description, budget, impact, achievement of objectives and outputs, and appraisal by Management Team.

E204 ESAPP Project Report 2004

Annual project report. Project description, budget, impact, achievement of objectives and outputs, and appraisal by Management Team.

Effects of climate, fire, and humans on forest dynamics: forest simulations compared to the palaeological record

In order to find out which factors influenced the forest dynamics in northern Italy during the Holocene, a palaeoecological approach involving pollen analysis was combined with ecosystem modelling. The dynamic and distribution based forest model DisCForm was run with different input scenarios for climate, species immigration, fire, and human impact and the similarity of the simulations with the original pollen record was assessed. From the comparisons of the model output and the pollen core, it appears that immigration was most important in the first part of the Holocene, and that fire and human activity had a major influence in the second half. Species not well represented in the simulation outputs are species with a higher abundance in the past than today (Corylus), with their habitat in riparian forests (Alnus) or with a strong response to human impact (Castanea).

7000-year human legacy of elevation-dependent European fire regimes

Variability in fire regime at the continental scale has primarily been attributed to climate change, often overshadowing the widely potential impact of human activities. However, human ignition modifies the rhythm of fire episodes occurrence (fire frequency), whereas land use alters vegetation composition and fuel load, and thus the amount of biomass burned. It is unclear, however, whether and how humans have exercised a significant influence over fire regimes at continental and millennial scales. Based on sedimentary charcoal records, we use new alternative estimate of fire frequency and biomass burned for the last 16000 years (here after 16 ky) that we evaluate with outputs from climate, vegetation, land use and population models. We find that pronounced regional-scale land use changes in southern Europe at the beginning of the Neolithic (8–6 ky), during the Bronze Age (5–4 ky) and the medieval period (1 ky) caused a doubling of fire frequency compared to the Holocene average (the last 11.5 ky). Despite anthropogenic influences, southern European biomass burned decreased from 7 ky, which is in line both with changes in orbital parameters leading climate cooling and also reductions in biomass availability because of land use. Our study underscores the role of elevation-dependent parameters, and particularly biomass and land management, as major drivers of fire regime variability. Results attest a determinant anthropogenic driving-force on fire regime and a decrease in fire-carbon emissions since 7 ky in Southern Europe.

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Accurate rainfall data are the key input parameter for modelling river discharge and soil loss. Remote areas of Ethiopia often lack adequate precipitation data and where these data are available, there might be substantial temporal or spatial gaps. To counter this challenge, the Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) readily provides weather data for any geographic location on earth between 1979 and 2014. This study assesses the applicability of CFSR weather data to three watersheds in the Blue Nile Basin in Ethiopia. To this end, the Soil and Water Assessment Tool (SWAT) was set up to simulate discharge and soil loss, using CFSR and conventional weather data, in three small-scale watersheds ranging from 112 to 477 ha. Calibrated simulation results were compared to observed river discharge and observed soil loss over a period of 32 years. The conventional weather data resulted in very good discharge outputs for all three watersheds, while the CFSR weather data resulted in unsatisfactory discharge outputs for all of the three gauging stations. Soil loss simulation with conventional weather inputs yielded satisfactory outputs for two of three watersheds, while the CFSR weather input resulted in three unsatisfactory results. Overall, the simulations with the conventional data resulted in far better results for discharge and soil loss than simulations with CFSR data. The simulations with CFSR data were unable to adequately represent the specific regional climate for the three watersheds, performing even worse in climatic areas with two rainy seasons. Hence, CFSR data should not be used lightly in remote areas with no conventional weather data where no prior analysis is possible.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Timely Reporting and Interactive Visualization of Animal Health and Slaughterhouse Surveillance Data in Switzerland

The reporting of outputs from health surveillance systems should be done in a near real-time and interactive manner in order to provide decision makers with powerful means to identify, assess, and manage health hazards as early and efficiently as possible. While this is currently rarely the case in veterinary public health surveillance, reporting tools do exist for the visual exploration and interactive interrogation of health data. In this work, we used tools freely available from the Google Maps and Charts library to develop a web application reporting health-related data derived from slaughterhouse surveillance and from a newly established web-based equine surveillance system in Switzerland. Both sets of tools allowed entry-level usage without or with minimal programing skills while being flexible enough to cater for more complex scenarios for users with greater programing skills. In particular, interfaces linking statistical softwares and Google tools provide additional analytical functionality (such as algorithms for the detection of unusually high case occurrences) for inclusion in the reporting process. We show that such powerful approaches could improve timely dissemination and communication of technical information to decision makers and other stakeholders and could foster the early-warning capacity of animal health surveillance systems.

An ultrasensitive NanoLuc-based luminescence system for monitoring Plasmodium berghei throughout its life cycle

BACKGROUND: Bioluminescence imaging is widely used for cell-based assays and animal imaging studies, both in biomedical research and drug development. Its main advantages include its high-throughput applicability, affordability, high sensitivity, operational simplicity, and quantitative outputs. In malaria research, bioluminescence has been used for drug discovery in vivo and in vitro, exploring host-pathogen interactions, and studying multiple aspects of Plasmodium biology. While the number of fluorescent proteins available for imaging has undergone a great expansion over the last two decades, enabling simultaneous visualization of multiple molecular and cellular events, expansion of available luciferases has lagged behind. The most widely used bioluminescent probe in malaria research is the Photinus pyralis firefly luciferase, followed by the more recently introduced Click-beetle and Renilla luciferases. Ultra-sensitive imaging of Plasmodium at low parasite densities has not been previously achieved. With the purpose of overcoming these challenges, a Plasmodium berghei line expressing the novel ultra-bright luciferase enzyme NanoLuc, called PbNLuc has been generated, and is presented in this work. RESULTS: NanoLuc shows at least 150 times brighter signal than firefly luciferase in vitro, allowing single parasite detection in mosquito, liver, and sexual and asexual blood stages. As a proof-of-concept, the PbNLuc parasites were used to image parasite development in the mosquito, liver and blood stages of infection, and to specifically explore parasite liver stage egress, and pre-patency period in vivo. CONCLUSIONS: PbNLuc is a suitable parasite line for sensitive imaging of the entire Plasmodium life cycle. Its sensitivity makes it a promising line to be used as a reference for drug candidate testing, as well as the characterization of mutant parasites to explore the function of parasite proteins, host-parasite interactions, and the better understanding of Plasmodium biology. Since the substrate requirements of NanoLuc are different from those of firefly luciferase, dual bioluminescence imaging for the simultaneous characterization of two lines, or two separate biological processes, is possible, as demonstrated in this work.

Productivity Growth and Efficiency in Indian Banking: A Comparison of Public, Private, and Foreign Banks

India's public sector banks (PSBs) are compared unfavorably with their private sector counterparts, domestic and foreign. This comparison rests, for the most part, on financial measures of performance, and such a comparison provides much of the rationale for privatization of PSBs.In this paper, we attempt a comparison between PSBs and their private sector counterparts based on measures of productivity that use quantities of outputs and inputs. We employ two measures of productivity: Tornqvist and Malmquist total factor productivity growth. We attempt these comparisons over the period 1992-2000, comparing PSBs with both domestic private and foreign banks. Out of a total of four comparisons we have made, there are no differences in three cases, PSBs do better in two, and foreign banks in one. To put it differently, PSBs are seen to be at a disadvantage in only one out of six comparisons. It is difficult, therefore, to sustain the proposition that efficiency and productivity have been lower in public sector banks relative to their peers in the private sector.

Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.

Monetary and Exchange Rate Policy in Multisectorial Economies

We develop a two-sector economy where each sector is classified as classical/Keynesian (contract/noncontract) in the labor market and traded/nontraded in the product market. We consider the effects of changes in monetary and exchange rate policy on sectoral and aggregate prices and outputs for different sectoral characterizations. Duca (1987) shows that nominal wage rigidity facilitates the effectiveness of monetary policy even in the classical sector. We demonstrate that trade price rigidity provides a similar path for the effectiveness of monetary policy, in this case, even when both sectors are classical.