Breeding for improved nitrogen use efficiency in oilseed rape

Oilseed rape has a high requirement for nitrogen (N) fertiliser relative to its seed yield. This paper uses published and unpublished work to explore the extent to which the N use efficiency (seed yield ÷ N supply) of oilseed rape could be improved without reducing seed yield. It was estimated that if the concentration of N in the stem and pod wall at crop maturity could be reduced from 1.0 to 0.6%, the root length density increased to 1 cm/cm3 to 100 cm soil depth and the post flowering N uptake increased by 20 kg N/ha then the fertiliser requirement could be reduced from 191 to 142 kg N/ha and the N use efficiency could be increased from 15.2 to 22.4 kg of seed dry matter per kg N. Genetic variation was found for all of the traits that were estimated to be important for N use efficiency. This indicates that there is significant scope for plant breeders to reduce N use efficiency in oilseed rape

Quality assurance of approved out of programme psychiatry training and research over the past 5 years

Aims and method This paper intends to analyse the number of applications, trainee demographic and approval rate of those applying for out of programme training (OOPT) or out of programme research (OOPR) between January 2008 and April 2013 using the committee’s anonymised database. We also describe the process of application and approval by the Quality Assurance Committee. Results There were 90 applications, including 10 resubmissions during the 64-month period. Most applicants (77%) were higher trainees; 53% of applicants were from the London deanery; 60% of applications were for research posts and higher degrees (OOPR). Overall, 64% were approved by the committee: 70% for OOPRs and 53% for OOPTs. Clinical implications This paper shows with transparency the breakdown of applications to the Quality Assurance Committee. Around two-thirds of applications to the committee are supported (64%). Relatively few psychiatry trainees (2.5%) have applied for an OOPT or an OOPR over the past 5 years

Langevin equations with multiplicative noise: resolution of time discretization ambiguities for equilibrium systems

A Langevin equation with multiplicative noise is an equation schematically of the form dq/dt = -F(q) + e(q) xi, where e(q) xi is Gaussian white noise whose amplitude e(q) depends on q itself. Such equations are ambiguous, and depend on the details of one's convention for discretizing time when solving them. I show that these ambiguities are uniquely resolved if the system has a known equilibrium distribution exp[-V(q)/T] and if, at some more fundamental level, the physics of the system is reversible. I also discuss a simple example where this happens, which is the small frequency limit of Newton's equation d^2q/dt^2 + e^2(q) dq/dt = - grad V(q) + e^{-1}(q) xi with noise and a q-dependent damping term. The resolution does not correspond to simply interpreting naive continuum equations in a standard convention, such as Stratanovich or Ito. [One application of Langevin equations with multiplicative noise is to certain effective theories for hot, non-Abelian plasmas.]Comment: 15 pages, 2 figures [further corrections to Appendix A

How much is too much on monitoring tasks? Visual scan patterns of single air traffic controller performing multiple remote tower operations

The innovative concept of multiple remote tower operation (MRTO) is where a single air traffic controller (ATCO) provides air traffic services to two or more different airports from a geographically separated virtual Tower. Effective visual scanning by the air traffic controller is the main safety concern for human-computer interaction, as the aim of MRTO is a single controller performing air traffic management tasks originally carried out by up to four ATCOs, comprehensively supported by innovative technology. Thirty-two scenarios were recorded and analyzed using an eye tracking device to investigate the above safety concern and the effectiveness of multiple remote tower operations. The results demonstrated that ATCOs' visual scan patterns showed significant task related variation while performing different tasks and interacting with various interfaces on the controller's working position (CWP). ATCOs were supported by new display systems equipped with pan tilt zoom (PTZ) cameras allowing enhanced visual checking of airport surfaces and aircraft positions. Therefore, one ATCO could monitor and provide services for two airports simultaneously. The factors influencing visual attention include how the information is presented, the complexity of that information, and the characteristics of the operating environment. ATCO's attention distribution among display systems is the key human-computer interaction issue in single ATCO performing multiple monitoring tasks

Animal models for COVID-19:More to the picture than ACE2, rodents, ferrets and non-human 1 primates. A case for porcine respiratory coronavirus and the obese Ossabaw pig

The ongoing COVID-19 pandemic caused by infection with SARS-CoV-2 has created an urgent need for animal models to enable study of basic infection and disease mechanisms and for development of vaccines, therapeutics, and diagnostics. Most research on animal models for COVID-19 has been directed toward rodents, transgenic rodents, and non-human primates. The primary focus has been on the angiotensin-converting enzyme 2 (ACE2), which is a host cell receptor for SARS-CoV-2. Among investigated species, irrespective of ACE2 spike protein binding, only mild (or no) disease has occurred following infection with SARS-CoV-2, suggesting that ACE2 may be necessary for infection but is not sufficient to determine the outcome of infection. The common trait of all species investigated as COVID models is their healthy status prior to virus challenge. In contrast, the vast majority of severe COVID-19 cases occur in people with chronic comorbidities such as diabetes, obesity, and/or cardiovascular disease. Healthy pigs express ACE2 protein that binds the viral spike protein but they are not susceptible to infection with SARS-CoV-2. However, certain pig breeds, such as the Ossabaw pig, can reproducibly be made obese and show most aspects of the metabolic syndrome, thus resembling the more than 80% of the critically ill COVID-19 patients admitted to hospitals. We urge considering infection with porcine respiratory coronavirus of metabolic syndrome pigs, such as the obese Ossabaw pig, as a highly relevant animal model of severe COVID-19.This work was funded by the Technical University of Denmark (DTU) and by an NIH grant to MA and MS (US-NIH-P30-DK097512)

Discovery of the bicycle gene family provides new insights into insect manipulation of plant development during gall induction

Galls are complex structures that develop from plant tissue, providing protection and food for gall-forming organisms, such as insects or mites. However, the molecules used by insects or mites to manipulate plant development have proved elusive. A landmark study has tracked down a gene in a gall-forming aphid that controls whether galls on witch hazel are green or red. The ‘green allele’ is strongly expressed in aphid salivary glands and represses plant genes used for red color formation. Excitingly, the gene product is part of a large suite of proteins that aphids may use to interact with plant biology

Proteomic analysis of urine to identify breast cancer biomarker candidates using a label-free LC-MS/MS approach

Introduction: Breast cancer is a complex heterogeneous disease and is a leading cause of death in women. Early diagnosis and monitoring progression of breast cancer are important for improving prognosis. The aim of this study was to identify protein biomarkers in urine for early screening detection and monitoring invasive breast cancer progression. Method: We performed a comparative proteomic analysis using ion count relative quantification label free LC-MS/MS analysis of urine from breast cancer patients (n = 20) and healthy control women (n = 20). Results: Unbiased label free LC-MS/MS-based proteomics was used to provide a profile of abundant proteins in the biological system of breast cancer patients. Data analysis revealed 59 urinary proteins that were significantly different in breast cancer patients compared to the normal control subjects (p3). Thirty-six urinary proteins were exclusively found in specific breast cancer stages, with 24 increasing and 12 decreasing in their abundance. Amongst the 59 significant urinary proteins identified, a list of 13 novel up-regulated proteins were revealed that may be used to detect breast cancer. These include stage specific markers associated with pre-invasive breast cancer in the ductal carcinoma in-situ (DCIS) samples (Leucine LRC36, MAST4 and Uncharacterized protein CI131), early invasive breast cancer (DYH8, HBA, PEPA, uncharacterized protein C4orf14 (CD014), filaggrin and MMRN2) and metastatic breast cancer (AGRIN, NEGR1, FIBA and Keratin KIC10). Preliminary validation of 3 potential markers (ECM1, MAST4 and filaggrin) identified was performed in breast cancer cell lines by Western blotting. One potential marker MAST4 was further validated in human breast cancer tissues as well as individual human breast cancer urine samples with immunohistochemistry and Western blotting, respectively. Conclusions: Our results indicate that urine is a useful non-invasive source of biomarkers and the profile patterns (biomarkers) identified, have potential for clinical use in the detection of BC. Validation with a larger independent cohort of patients is required in the following study

Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock