Willingness to pay for biofertilizers among grain legume farmers in northern Ghana

Open Access Journal; Published online: 27 April 2018Background: The call for use of improved Soil Fertility Management (SFM) technologies is a prerequisite to increase agricultural productivity among farmers. This study assessed farmers’ willingness to pay (WTP) for selected financially rewarding biofertilizer technologies/packages for legume production in northern Ghana. Primary data was elicited from 400 grain legume farmers selected from Northern and Upper West Regions of Ghana through a simple random sampling technique. The double bounded dichotomous choice (DBDC) format of contingent valuation approach was employed to elicit willingness to pay values and determinants of farmers WTP was evaluated using the maximum likelihood estimation procedure. Results: The results showed that about 60%, 25% and 46% of soya, cowpea and groundnuts farmers were willing to pay for the selected biofertilizers (Biofix, BR3267 and Legumefix respectively) at prices not exceeding GHC 14.00, GHC 28.00 and GHC 20.00 per 0.2kg of the respective biofertilizers. Legume farmers in Northern Region were however willing to pay higher for the three biofertilizer technologies as compared to their counterparts in Upper West Region. For 0.2 kg each of Biofix, BR3267 and Legumefix, farmers in Northern Region were willing to pay approximately GHC 17.00, GHC 12.00 and GHC 23.00 respectively whereas those in Upper West Region were willing to pay GHC 14.00, GHC 9.00 and GHC 11.00 for the same quantity of each biofertilizer. The study identified farming experience, FBO membership, awareness and previous use of biofertilizers as significant determinants of farmers’ willingness to pay for Biofertilizers. Conclusion: Comparatively, mean prices farmers are willing to pay for these three technologies are below ex-factory prices, hence subsidizing the cost of production of these biofertilizers in the initial stages would be relevant for improving farmers’ uptake of these fertilizers. Sustained awareness creation through periodic education and sensitization by using FBOs as leverage points is also highly recommended to improve farmers’ understanding of the concept of biofertilizer use

Characterization of Patients with Chronic Diseases and Complex Care Needs: A New High-Risk Emergent Population

Background: To analyze the prevalence and main epidemiological, clinical and outcome features of in-Patients with Complex Chronic conditions (PCC) in internal medicine areas, using a pragmatic working definition. Methods: Prospective study in 17 centers from Spain, with 97 in-hospital, monthly prevalence cuts. A PCC was considered when criteria of polypathological patient (two or more major chronic diseases) were met, or when a patient suffered one major chronic disease plus one or more of nine predefined complexity criteria like socio-familial risk, alcoholism or malnutrition among others (PCC without polypathology). A complete set of baseline features as well as 12-months survival were collected. Then, we compared clinical, outcome variables, and PROFUND index accuracy between polypathological patients and PCC without polypathology. Results: The global prevalence of PCC was 61% (40% of them were polypathological patients, and 21% PCC withouth polypathology) out of the 2178 evaluated patients. Their median age was 82 (59.5% men), suffered 2.3 ± 1.1 major diseases (heart diseases (70.5%), neurologic (41.5%), renal (36%), and lung diseases (26%)), 5.5 ± 2.5 other chronic conditions, met 2.5 ± 1.5 complexity criteria, and presented functional decline (Barthel index 55 (25-90)). Compared to polypathological patients, the subgroup of PCC without polypathology were younger, with a different pattern of major diseases and comorbidities, a better functional status, and lower 12-months mortality rates ((36.2% vs 46.8%; p = .003; OR 0.7(0.48-0.86). The PROFUND index obtained adequate calibration and discrimination power (AUC-ROC 0.67 (0.63-0.69)) in predicting 12-month mortality of PCC. Conclusion: Patients with complex chronic conditions are highly prevalent in internal medicine areas; their clinical pattern has changed in parallel to socio-epidemiological modifications, but their death-risk is still adequately predicted by PROFUND index

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics

Dystrophinopathy Phenotypes and Modifying Factors in Exon 45-55 Deletion

Duchenne muscular dystrophy (DMD) exon 45-55 deletion (del45-55) has been postulated as a model that could treat up to 60% of DMD patients, but the associated clinical variability and complications require clarification. We aimed to understand the phenotypes and potential modifying factors of this dystrophinopathy subset. This cross-sectional, multicenter cohort study applied clinical and functional evaluation. Next generation sequencing was employed to identify intronic breakpoints and their impact on the Dp140 promotor, intronic long noncoding RNA, and regulatory splicing sequences. DMD modifiers (SPP1, LTBP4, ACTN3) and concomitant mutations were also assessed. Haplotypes were built using DMD single nucleotide polymorphisms. Dystrophin expression was evaluated via immunostaining, Western blotting, reverse transcription polymerase chain reaction (PCR), and droplet digital PCR in 9 muscle biopsies. The series comprised 57 subjects (23 index) expressing Becker phenotype (28%), isolated cardiopathy (19%), and asymptomatic features (53%). Cognitive impairment occurred in 90% of children. Patients were classified according to 10 distinct index-case breakpoints; 4 of them were recurrent due to founder events. A specific breakpoint (D5) was associated with severity, but no significant effect was appreciated due to the changes in intronic sequences. All biopsies showed dystrophin expression of >67% and traces of alternative del45-57 transcript that were not deemed pathogenically relevant. Only the LTBP4 haplotype appeared associated the presence of cardiopathy among the explored extragenic factors. We confirmed that del45-55 segregates a high proportion of benign phenotypes, severe cases, and isolated cardiac and cognitive presentations. Although some influence of the intronic breakpoint position and the LTBP4 modifier may exist, the pathomechanisms responsible for the phenotypic variability remain largely unresolved. ANN NEUROL 2022;92:793-80

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE

The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at arXiv:1802.0870

Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts the raw digitized TPC waveform to the number of ionization electrons passing through a wire plane at a given time. A robust recovery of the number of ionization electrons from both induction and collection anode wire planes will augment the 3D reconstruction, and is particularly important for tomographic reconstruction algorithms. A number of building blocks of the overall procedure are described. The performance of the signal processing is quantitatively evaluated by comparing extracted charge with the true charge through a detailed TPC detector simulation taking into account position-dependent induced current inside a single wire region and across multiple wires. Some areas for further improvement of the performance of the charge extraction procedure are also discussed.Comment: 60 pages, 36 figures. The second part of this work can be found at arXiv:1804.0258

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples

Highlights from the Pierre Auger Observatory

The Pierre Auger Observatory is the world's largest cosmic ray observatory. Our current exposure reaches nearly 40,000 km$^2$ str and provides us with an unprecedented quality data set. The performance and stability of the detectors and their enhancements are described. Data analyses have led to a number of major breakthroughs. Among these we discuss the energy spectrum and the searches for large-scale anisotropies. We present analyses of our X$_{max}$ data and show how it can be interpreted in terms of mass composition. We also describe some new analyses that extract mass sensitive parameters from the 100% duty cycle SD data. A coherent interpretation of all these recent results opens new directions. The consequences regarding the cosmic ray composition and the properties of UHECR sources are briefly discussed.Comment: 9 pages, 12 figures, talk given at the 33rd International Cosmic Ray Conference, Rio de Janeiro 201