Seismic Hazard Evaluation for Design of San Vicente Dam Raise

The San Diego County Water Authority (Water Authority) is undertaking a raise of the existing San Vicente Dam to provide both emergency and carryover storage to increase local reservoir supplies in San Diego County, California, USA. The emergency storage is required in case of a disruption to the imported water transmission system from floods or earthquakes and the carry-over storage would be utilized to store water during “wet” seasons to carry-over to seasons of drought. The existing San Vicente Dam is a 220 foot (67 m) high concrete gravity dam completed in 1943 with 90,063 acre-feet of storage. The raised San Vicente Dam will be about 337 feet (102.3 m) high, creating an approximately 247,000 acre-foot reservoir. The dam raise will be constructed using the roller compacted concrete (RCC) method. This paper presents details of the seismic hazard evaluation that formed the basis for development of strong ground motions that were considered in final design of the dam raise. The dam is under the jurisdiction of the California Department of Water Resources, Division of Safety of Dams (DSOD) which requires the use of deterministic ground motions for design. However, the earthquake ground motions at the site are largely controlled by background (or random) earthquakes, which cannot be adequately addressed using deterministic methods alone. This prompted the development of supplemental seismic design ground motions based on a probabilistic seismic hazard analysis (PSHA). The PSHA was performed incorporating the latest information on seismic sources and recently developed Next Generation of Attenuation (NGA) relationships. Based on the results of the PSHA, ground motion parameters (response spectra and time histories) were developed for final design of the dam raise. Comparisons of the deterministic and probabilistic ground motions are provided. Application of the NGA relationships resulted in lower estimates of peak ground accelerations than what were obtained based on the previous attenuation relationships due to the use of the site-specific shear wave velocities of the foundation materials

Retired A Stars: The Effect of Stellar Evolution on the Mass Estimates of Subgiants

Doppler surveys have shown that the occurrence rate of Jupiter-mass planets appears to increase as a function of stellar mass. However, this result depends on the ability to accurately measure the masses of evolved stars. Recently, Lloyd (2011) called into question the masses of subgiant stars targeted by Doppler surveys. Lloyd argues that very few observable subgiants have masses greater than 1.5 Msun, and that most of them have masses in the range 1.0-1.2 Msun. To investigate this claim, we use Galactic stellar population models to generate an all-sky distribution of stars. We incorporate the effects that make massive subgiants less numerous, such as the initial mass function and differences in stellar evolution timescales. We find that these effects lead to negligibly small systematic errors in stellar mass estimates, in contrast to the roughly 50% errors predicted by Lloyd. Additionally, our simulated target sample does in fact include a significant fraction of stars with masses greater than 1.5 Msun, primarily because the inclusion of an apparent magnitude limit results in a Malmquist-like bias toward more massive stars, in contrast to the volume-limited simulations of Lloyd. The magnitude limit shifts the mean of our simulated distribution toward higher masses and results in a relatively smaller number of evolved stars with masses in the range 1.0-1.2 Msun. We conclude that, within the context of our present-day understanding of stellar structure and evolution, many of the subgiants observed in Doppler surveys are indeed as massive as main-sequence A stars.Comment: Accepted to ApJ, 5 pages, 3 figures; changed title, reworded introduction and conclusion

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical tech- nique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy carries a degree of morbidity and mortality and on occasion may even be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm−1. To begin development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random Forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spec- tral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Fur- thermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is import to allow future clinical translation and enables IR to achieve its potential

Introducing discrete frequency infrared technology for high-throughput biofluid screening

Accurate early diagnosis is critical to patient survival, management and quality of life. Biofluids are key to early diagnosis due to their ease of collection and intimate involvement in human function. Large-scale mid-IR imaging of dried fluid deposits offers a high-throughput molecular analysis paradigm for the biomedical laboratory. The exciting advent of tuneable quantum cascade lasers allows for the collection of discrete frequency infrared data enabling clinically relevant timescales. By scanning targeted frequencies spectral quality, reproducibility and diagnostic potential can be maintained while significantly reducing acquisition time and processing requirements, sampling 16 serum spots with 0.6, 5.1 and 15% relative standard deviation (RSD) for 199, 14 and 9 discrete frequencies respectively. We use this reproducible methodology to show proof of concept rapid diagnostics; 40 unique dried liquid biopsies from brain, breast, lung and skin cancer patients were classified in 2.4 cumulative seconds against 10 non-cancer controls with accuracies of up to 90%

Phosphorylation of centromeric histone H3 variant regulates chromosome segregation in S. cerevisiae

The centromeric histone H3 variant (CenH3) is essential for chromosome segregation in eukaryotes. We have identified posttranslational modifications of S. cerevisiae CenH3, Cse4. Functional characterization of cse4 phosphorylation mutants showed growth and chromosome segregation defects when combined with kinetochore mutants okp1 and ame1. Using a phosphoserine-specific antibody we showed that the association of phosphorylated Cse4 with centromeres is increased in response to defective microtubule attachment or reduced cohesion. We determined that evolutionarily conserved Ipl1/Aurora B contributes to phosphorylation of Cse4, as levels of phosphorylated Cse4 were reduced at centromeres in ipl1 strains in vivo and in vitro assays showed phosphorylation of Cse4 by Ipl1. Consistent with these results we observed that a phosphomimetic cse4-4SD mutant suppressed the temperature sensitive growth of ipl1-2 and Ipl1 substrate mutants dam1 spc34 and ndc80 that are defective for chromosome biorientation. Furthermore, cell biology approaches using a GFP labeled chromosome showed that cse4-4SD suppressed chromosome segregation defects in dam1 spc34 strains. Based these results we propose that phosphorylation of Cse4 destabilizes defective kinetochores to promote biorientation and ensure faithful chromosome segregation. Taken together, our study provides a detailed analysis, in vivo and in vitro, of Cse4 phosphorylation and its role in promoting faithful chromosome segregation

Linking Animals Aloft with the Terrestrial Landscape

Despite using the aerosphere for many facets of their life, most flying animals (i.e., birds, bats, some insects) are still bound to terrestrial habitats for resting, feeding, and reproduction. Comprehensive broad-scale observations by weather surveillance radars of animals as they leave terrestrial habitats for migration or feeding flights can be used to map their terrestrial distributions either as point locations (e.g., communal roosts) or as continuous surface layers (e.g., animal densities in habitats across a landscape). We discuss some of the technical challenges to reducing measurement biases related to how radars sample the aerosphere and the flight behavior of animals. We highlight a recently developed methodological approach that precisely and quantitatively links the horizontal spatial structure of birds aloft to their terrestrial distributions and provides novel insights into avian ecology and conservation across broad landscapes. Specifically, we present case studies that (1) elucidate how migrating birds contend with crossing ecological barriers and extreme weather events, (2) identify important stopover areas and habitat use patterns of birds along their migration routes, and (3) assess waterfowl response to wetland habitat management and restoration. These studies aid our understanding of how anthropogenic modification of the terrestrial landscape (e.g., urbanization, habitat management), natural geographic features, and weather (e.g., hurricanes) can affect the terrestrial distributions of flying animals

Linking Animals Aloft with the Terrestrial Landscape

Despite using the aerosphere for many facets of their life, most flying animals (i.e., birds, bats, some insects) are still bound to terrestrial habitats for resting, feeding, and reproduction. Comprehensive broad-scale observations by weather surveillance radars of animals as they leave terrestrial habitats for migration or feeding flights can be used to map their terrestrial distributions either as point locations (e.g., communal roosts) or as continuous surface layers (e.g., animal densities in habitats across a landscape). We discuss some of the technical challenges to reducing measurement biases related to how radars sample the aerosphere and the flight behavior of animals. We highlight a recently developed methodological approach that precisely and quantitatively links the horizontal spatial structure of birds aloft to their terrestrial distributions and provides novel insights into avian ecology and conservation across broad landscapes. Specifically, we present case studies that (1) elucidate how migrating birds contend with crossing ecological barriers and extreme weather events, (2) identify important stopover areas and habitat use patterns of birds along their migration routes, and (3) assess waterfowl response to wetland habitat management and restoration. These studies aid our understanding of how anthropogenic modification of the terrestrial landscape (e.g., urbanization, habitat management), natural geographic features, and weather (e.g., hurricanes) can affect the terrestrial distributions of flying animals

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients’ average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy

Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies >80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients

The Time-Domain Spectroscopic Survey: Understanding the Optically Variable Sky with SEQUELS in SDSS-III

The Time-Domain Spectroscopic Survey (TDSS) is an SDSS-IV eBOSS subproject primarily aimed at obtaining identification spectra of ~220,000 optically-variable objects systematically selected from SDSS/Pan-STARRS1 multi-epoch imaging. We present a preview of the science enabled by TDSS, based on TDSS spectra taken over ~320 deg^2 of sky as part of the SEQUELS survey in SDSS-III, which is in part a pilot survey for eBOSS in SDSS-IV. Using the 15,746 TDSS-selected single-epoch spectra of photometrically variable objects in SEQUELS, we determine the demographics of our variability-selected sample, and investigate the unique spectral characteristics inherent in samples selected by variability. We show that variability-based selection of quasars complements color-based selection by selecting additional redder quasars, and mitigates redshift biases to produce a smooth quasar redshift distribution over a wide range of redshifts. The resulting quasar sample contains systematically higher fractions of blazars and broad absorption line quasars than from color-selected samples. Similarly, we show that M-dwarfs in the TDSS-selected stellar sample have systematically higher chromospheric active fractions than the underlying M-dwarf population, based on their H-alpha emission. TDSS also contains a large number of RR Lyrae and eclipsing binary stars with main-sequence colors, including a few composite-spectrum binaries. Finally, our visual inspection of TDSS spectra uncovers a significant number of peculiar spectra, and we highlight a few cases of these interesting objects. With a factor of ~15 more spectra, the main TDSS survey in SDSS-IV will leverage the lessons learned from these early results for a variety of time-domain science applications.Comment: 17 pages, 14 figures, submitted to Ap