Bias Variation Research Articles

How much will new technologies lower the cost of DNA sequencing?

Sanger and his colleagues invented the DNA sequencing technology by cleverly applying the function of DNA polymerase to DNA sequence detection in 1977. Human Genome Project (HGP) launched in the early 1990s greatly facilitated the progress of the DNA sequencing technology and prompted the automation of DNA sequencing which conversely accelerate the human genome draft accomplished in 2001. For the past decade, great advancements in the speed and sequencing throughput in the next generation sequencing (NGS) technologies have dramatically reduced the cost of the whole genome sequencing to $1000. Although there is no great improvement in technique for NGS compared with the first generation sequencing in the basic principle of sequencing, the second generation sequencing system processes the quantified sequencing fragments in massive parallel way, which makes sequencing throughput increased significantly and at the same time greatly reduce the cost. Recently, the third generation sequencing technologies emerge with the advantages of very long sequencing fragment and no need for PCR amplification during the sequencing process, which significantly decrease the complex of assembling process and avoid the potential bias and false variations caused by PCR technology. Although the accuracy of the third generation sequencing technologies can only reach about 85% and the price is relatively high, the technologies still show their promising future with the gradual improvement in experiment processes and computational methods. When biomedical researches and clinical applications will benefit from the technology innovation, the enormous applications of NGS in precision medicine and biomedical research fields will further accelerate the progress and innovations in NGS. At the same time, with the vast accumulation of massive sequencing data, how to efficiently analyze and interpret those data becomes a big challenge, bioinformatics plays the key role in the process. Well trained bioinformaticians with good background of mathematics, statistics, computer skills and biology are in high demand. It can be anticipated that the continuous innovations in NGS technology will further significantly decrease the cost for the human genome sequencing and promise the advent of the era with the sequencing cost close to $100, which will push personal genome sequencing widely used in clinical applications and make the precision medicine come true in daily life.

Derivation of GPS TEC and receiver bias for Langkawi station in Malaysia

This paper presents the polynomial-type TEC model to derive total electron content (TEC) and receiver bias for Langkawi (LGKW) station in Malaysia at geographic latitude of 6.32° and longitude of 99.85°. The model uses a polynomial function of coordinates of the ionospheric piercing point to describe the TEC distribution in space. In the model, six polynomial coefficients and a receiver bias are unknown which can be solved by the least squares method. A reasonable agreement is achieved for the derivation of TEC and receiver bias for IENG station in Italy, as compared with that derived by the IGS analysis center, CODE. We process one year of LGKW data in 2010 and show the monthly receiver bias and the seasonal TEC variation. The monthly receiver bias varies between −48 and −24 TECu (1016 electrons/m2), with the mean value at −37 TECu. Large variations happen in the monthly receiver biases due to the low data coverage of high satellite elevation angle (60° < α ≤ 90°). Post-processing TEC approach is implemented which can resolve the wavy pattern of the monthly TEC baseline resulted from the large variation of the receiver bias. The seasonal TEC variation at LGKW exhibits a semi-annual variation, where the peak occurs during equinoctial months, and the trough during summer and winter months.

Spin-Polarization in Quasi-Magnetic Tunnel Junctions**Supported by the Key Natural Science Fund of Sichuan Province Education Department under Grant Nos 13ZA0149 and 16ZA0047, and the Construction Plan for Scientific Research Innovation Team of Universities in Sichuan Province under Grant No 12TD008.

Spin polarization in ferromagnetic metal/insulator/spin-filter barrier/nonmagnetic metal, referred to as quasi-magnetic tunnel junctions, is studied within the free-electron model. Our results show that large positive or negative spin-polarization can be obtained at high bias in quasi-magnetic tunnel junctions, and within large bias variation regions, the degree of spin-polarization can be linearly tuned by bias. These linear variation regions of spin-polarization with bias are influenced by the barrier thicknesses, barrier heights and molecular fields in the spin-filter (SF) layer. Among them, the variations of thickness and heights of the insulating and SF barrier layers have influence on the value of spin-polarization and the linear variation regions of spin-polarization with bias. However, the variations of molecular field in the SF layer only have influence on the values of the spin-polarization and the influences on the linear variation regions of spin-polarization with bias are slight.

Initial assessment of the COMPASS/BeiDou-3: new-generation navigation signals

The successful launch of five new-generation experimental satellites of the China’s BeiDou Navigation Satellite System, namely BeiDou I1-S, I2-S, M1-S, M2-S, and M3-S, marks a significant step in expanding BeiDou into a navigation system with global coverage. In addition to B1I (1561.098 MHz) and B3I (1269.520 MHz) signals, the new-generation BeiDou-3 experimental satellites are also capable of transmitting several new navigation signals in space, namely B1C at 1575.42 MHz, B2a at 1176.45 MHz, and B2b at 1207.14 MHz. For the first time, we present an initial characterization and performance assessment for these new-generation BeiDou-3 satellites and their signals. The L1/L2/L5 signals from GPS Block IIF satellites, E1/E5a/E5b signals from Galileo satellites, and B1I/B2I/B3I signals from BeiDou-2 satellites are also evaluated for comparison. The characteristics of the B1C, B1I, B2a, B2b, and B3I signals are evaluated in terms of observed carrier-to-noise density ratio, pseudorange multipath and noise, triple-frequency carrier-phase ionosphere-free and geometry-free combination, and double-differenced carrier-phase and code residuals. The results demonstrate that the observational quality of the new-generation BeiDou-3 signals is comparable to that of GPS L1/L2/L5 and Galileo E1/E5a/E5b signals. However, the analysis of code multipath shows that the elevation-dependent code biases, which have been previously identified to exist in the code observations of the BeiDou-2 satellites, seem to be not obvious for all the available signals of the new-generation BeiDou-3 satellites. This will significantly benefit precise applications that resolve wide-lane ambiguity based on Hatch–Melbourne–Wubbena linear combinations and other applications such as single-frequency precise point positioning (PPP) based on the ionosphere-free code–carrier combinations. Furthermore, with regard to the triple-frequency carrier-phase ionosphere-free and geometry-free combination, it is found that different from the BeiDou-2 and GPS Block IIF satellites, no apparent bias variations could be observed in all the new-generation BeiDou-3 experimental satellites, which shows a good consistency of the new-generation BeiDou-3 signals. The absence of such triple-frequency biases simplifies the potential processing of multi-frequency PPP using observations from the new-generation BeiDou-3 satellites. Finally, the precise relative positioning results indicate that the additional observations from the new-generation BeiDou-3 satellites can improve ambiguity resolution performance with respect to BeiDou-2 only positioning, which indicates that observations from the new-generation BeiDou-3 satellites can contribute to precise relative positioning.

Neural Correlates of the False Consensus Effect: Evidence for Motivated Projection and Regulatory Restraint.

The false consensus effect (FCE), the tendency to project our attitudes and opinions on to others, is a pervasive bias in social reasoning with a range of ramifications for individuals and society. Research in social psychology has suggested that numerous factors (anchoring and adjustment, accessibility, motivated projection, etc.) may contribute to the FCE. In this study, we examine the neural correlates of the FCE and provide evidence that motivated projection plays a significant role. Activity in reward regions (ventromedial pFC and bilateral nucleus accumbens) during consensus estimation was positively associated with bias, whereas activity in right ventrolateral pFC (implicated in emotion regulation) was inversely associated with bias. Activity in reward and regulatory regions accounted for half of the total variation in consensus bias across participants (R2 = .503). This research complements models of the FCE in social psychology, providing a glimpse into the neural mechanisms underlying this important phenomenon.

Modeling bias and variation in the stochastic processes of small RNA sequencing.

The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data.

Overestimated Effective Spreads: Implications for Investors

The effective bid-ask spread measured relative to the spread midpoint overstates the true effective bid-ask spread in markets with discrete prices and elastic liquidity demand. The average bias is 13%-18% for S&P 500 stocks in general, depending on the estimator used as benchmark, and up to 97% for low-priced stocks. Cross-sectional bias variation across stocks, trading venues, and investor groups can influence research inference. The use of the midpoint also undermines liquidity timing and trading performance evaluations, and can lead non-sophisticated investors to overpay for liquidity. To overcome these problems, the paper proposes new estimators of the effective bid-ask spread.

Evaluation of Five Grid Datasets against Radiosonde Data over the Eastern and Downstream Regions of the Tibetan Plateau in Summer

In this study, horizontal wind (U and V), air temperature (T), and relative humidity (RH) modelled by the European Centre for Medium-Range Weather Forecasts Reanalysis Interim (ERA-Interim), the National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications (MERRA), the Japanese 55-year Reanalysis (JRA-55), the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2), and the NCEP Final Operational Global Analysis data and the NCEP Final Operational Global Analysis data (NCEP-FNL) products have been compared with observations at 11 radiosonde stations over the eastern and downstream regions of the Tibetan Plateau (TP) from late June until the end of July during 2011 to 2015. The mean bias of all variables for the five gridded datasets (GDs) in the Sichuan Basin (SCB) is larger than that for the TP. The mean values of U, V, and T from each grid dataset are generally consistent with the radiosonde values, whereas considerable bias in the mean RH exists at upper levels. The diurnal variation of the mean bias and root-mean-square (RMS) error in the basin are stronger than those in the TP and the negative/positive peak usually occurs at 06:00 UTC and 18:00 UTC in the basin or at 12:00 UTC in the TP. The inter-annual variations in the basin are significantly stronger, and the maximum values of the variations usually occur at upper levels or near the surface, except for V. The weather conditions have a crucial influence on the performance of the gridded datasets. The mean bias and RMS error of T in the TP on cloudy days are obviously larger than those during sunny conditions. Considerable but unsteady differences occur in the mean bias and RMS error of U and V in different weather conditions. On average, the four variables in the TP are more sensible to the weather conditions.

Visuospatial Asymmetries Arise from Differences in the Onset Time of Perceptual Evidence Accumulation.

Healthy subjects tend to exhibit a bias of visual attention whereby left hemifield stimuli are processed more quickly and accurately than stimuli appearing in the right hemifield. It has long been held that this phenomenon arises from the dominant role of the right cerebral hemisphere in regulating attention. However, methods that would enable more precise understanding of the mechanisms underpinning visuospatial bias have remained elusive. We sought to finely trace the temporal evolution of spatial biases by leveraging a novel bilateral dot motion detection paradigm. In combination with electroencephalography, this paradigm enables researchers to isolate discrete neural signals reflecting the key neural processes needed for making these detection decisions. These include signals for spatial attention, early target selection, evidence accumulation, and motor preparation. Using this method, we established that three key neural markers accounted for unique between-subject variation in visuospatial bias: hemispheric asymmetry in posterior α power measured before target onset, which is related to the distribution of preparatory attention across the visual field; asymmetry in the peak latency of the early N2c target-selection signal; and, finally, asymmetry in the onset time of the subsequent neural evidence-accumulation process with earlier onsets for left hemifield targets. Our development of a single paradigm to dissociate distinct processing components that track the temporal evolution of spatial biases not only advances our understanding of the neural mechanisms underpinning normal visuospatial attention bias, but may also in the future aid differential diagnoses in disorders of spatial attention.SIGNIFICANCE STATEMENT The significance of this research is twofold. First, it shows that individual differences in how humans direct their attention between left and right space reflects physiological differences in how early the brain starts to accumulate evidence for the existence of a visual target. Second, the novel methods developed here may have particular relevance to disorders of attention, such as unilateral spatial neglect. In the case of spatial neglect, pathological inattention to left space could have multiple underlying causes, including biased attention, impaired decision formation, or a motor deficit related to one side of space. Our development of a single paradigm to dissociate each of these components may aid in supporting more precise differential diagnosis in such heterogeneous disorders.

Effects of drain bias on the statistical variation of double-gate tunnel field-effect transistors

The effects of drain bias on the statistical variation of double-gate (DG) tunnel field-effect transistors (TFETs) are discussed in comparison with DG metal–oxide–semiconductor FETs (MOSFETs). Statistical variation corresponds to the variation of threshold voltage (Vth), subthreshold swing (SS), and drain-induced barrier thinning (DIBT). The unique statistical variation characteristics of DG TFETs and DG MOSFETs with the variation of drain bias are analyzed by using full three-dimensional technology computer-aided design (TCAD) simulation in terms of the three dominant variation sources: line-edge roughness (LER), random dopant fluctuation (RDF) and workfunction variation (WFV). It is observed than DG TFETs suffer from less severe statistical variation as drain voltage increases unlike DG MOSFETs.

The transparency of Cd(0001) films grown on Si(111)-7&amp;times;7: Imaging the interface at atomic scale

The interfacial structure plays a crucial role in controlling the growth of epitaxial film. However, it is still a formidable task to observe the interface structure at the atomic scale, because the detected signals from the interface become very weak due to the strong attenuation and decoherence when the incident wave passes through the films. With the advent of scanning tunneling microscopy (STM), significant progress has been made in observing the interface structure of thin films. Many studies have demonstrated the typical Si(111)-7×7 superstructure below the metal films in the past. Although the 7×7 superstructures are visible, the 12 Si adatoms in the unit cells can not be resolved. Hence, imaging the 7×7 superstructures below thin films at the atomic scale has not been achieved. In this work, we have grown the Cd epitaxial films with high quality on Si(111)-7×7 substrate, and realized the imaging of interface structure of Cd(0001) at atomic scale by using low temperature scanning tunneling microscopy. Under low bias voltage, we can clearly observe the atomic image of Si(111)-7×7, indicating the Si (111)-7×7 substrate remains intact during the growth process of Cd films. Moreover, due to the quantum size effect, the Cd films with even or odd monolayers exhibit distinct lateral resolution as well as surface roughness, even at the same bias voltage. With the bias variation, such kind of diversities can be reversed. We attribute the highly transparency of Cd(0001) films to the anisotropic effective mass of the electrons in Cd films, i.e. the transverse motion is much faster than the in-plane motion.

A New Photovoltaic System Architecture of Module-Integrated Converter with a Single-sourced Asymmetric Multilevel Inverter Using a Cost-effective Single-ended Pre-regulator

In this paper, a new architecture for a cost-effective power conditioning systems (PCS) using a single-sourced asymmetric cascaded H-bridge multilevel inverter (MLI) for photovoltaic (PV) applications is proposed. The asymmetric MLI topology has a reduced number of parts compared to the symmetrical type for the same number of voltage level. However, the modulation index threshold related to the drop in the number of levels of the inverter output is higher than that of the symmetrical MLI. This problem results in a modulation index limitation which is relatively higher than that of the symmetrical MLI. Hence, an extra voltage pre-regulator becomes a necessary component in the PCS under a wide operating bias variation. In addition to pre-stage voltage regulation for the constant MLI dc-links, another auxiliary pre-regulator should provide isolation and voltage balance among the multiple H-bridge cells in the asymmetrical MLI as well as the symmetrical ones. The proposed PCS uses a single-ended DC-DC converter topology with a coupled inductor and charge-pump circuit to satisfy all of the aforementioned requirements. Since the proposed integrated-type voltage pre-regulator circuit uses only a single MOSFET switch and a single magnetic component, the size and cost of the PCS is an optimal trade-off. In addition, the voltage balance between the separate H-bridge cells is automatically maintained by the number of turns in the coupled inductor transformer regardless of the duty cycle, which eliminates the need for an extra voltage regulator for the auxiliary H-bridge in MLIs. The voltage balance is also maintained under the discontinuous conduction mode (DCM). Thus, the PCS is also operational during light load conditions. The proposed architecture can apply the module-integrated converter (MIC) concept to perform distributed MPPT. The proposed architecture is analyzed and verified for a 7-level asymmetric MLI, using simulation results and a hardware implementation.

Drivers of change in the relative abundance of dugongs in New Caledonia

Context Sound understanding of temporal changes in the abundance of wildlife species is required for assessing their status and for effective conservation and management. In New Caledonia, a single baseline aerial survey of dugongs in 2003 estimated a population of 2026 (± 553 s.e.) individuals. A second, similar survey in 2008 produced a lower estimate of 606 (± 200 s.e.) individuals, leading to concerns that the dugong population was experiencing a decline. Aims This study used data collected from additional aerial surveys with the aim of updating information on the current size of the dugong population in New Caledonia and investigating the drivers of change in the estimates. Methods Four additional surveys were conducted: one in each of the cool (June) and warm (November) seasons of 2011 and 2012 around the main Island of New Caledonia. Dugong relative abundance and density were calculated and compared among survey years and survey regions. Drivers of change in the dugong population size were then investigated. Key results The abundance estimates obtained from our four surveys ranged from 649 (± 195 s.e.) to 1227 (± 296 s.e.) dugongs. These new results were not significantly different to the 2008 estimate but were significantly lower than the 2003 estimate. There was no significant variation in the proportion of calves throughout the entire time series of surveys. Conclusions The dugong population of New Caledonia was relatively stable between 2008 and 2012. We could not find sufficient evidence to show whether the discrepancy between 2003 and the remainder of the time series is due to a real decline in the population or the result of the confounding effects of variation in environmental conditions, animal behaviour and sampling biases. Implications The stability of the dugong population between 2008 and 2012 is a positive outcome for local conservation and management of dugongs. This study also highlights the advisability of replicating baseline surveys to enable robust interpretation of temporal variation in population size estimates, and in turn, to improve the management of wildlife species.

Tunable hygromorphism: structural implications of low molecular weight gels and electrospun nanofibers in bilayer composites.

This investigation highlights the potential for electrospun nanofiber mats and self-assembled nanofiber networks to be interfaced synergistically to induce hygromorphic behaviour. Control poly(vinyl alcohol) (PVA) electrospun active layers and 1,3:2,4-di-p-methylbenyliedene sorbitol (MDBS) self-assembled passive layers encapsulated in an ethylene oxide-epichlorohydrin (EO-EPI) copolymer matrix were fabricated to examine the influence of composition on the properties guiding hygromorphism, such as water transport, layer thickness, and layer modulus. Experimentally determined material constants were utilized in conjunction with mathematical modeling to determine ideal layer properties. It was revealed that the active layer with the highest PVA content exhibited the fastest water transport, and the passive layer with the highest MDBS content displayed the slowest water transport. However, the hygromorphic bilayer fabricated utilizing the lowest PVA content and the highest MDBS fraction was predicted to induce the highest change in curvature due to the lower modulus and thickness of the PVA nanofiber active layer. Decreasing the MDBS content reduced the passive layer modulus while increasing water transport, which theoretically reduced the overall bilayer curvature. The hygromorphic bilayer composites fabricated using these ideal control layers exhibited folding bias and response variations dependent upon active layer composition and imposed folding directions. By utilizing the favorable force balances between the active layer with the lower PVA content and the passive layer with the highest MDBS amount in conjunction with folding bias in a non-preferential direction, it was possible to achieve hygromorphic unfolding and refolding with hydration. Through modelling and individual layer examination, a unique platform built on two independent fiber networks has been designed to achieve biomimetic hygromorphism in synthetic bilayer composites.

Learning, Visualizing, and Assessing a Model for the Intrinsic Value of a Batted Ball

We present a multidisciplinary approach for learning, visualizing, and assessing a model for the intrinsic value of a batted ball in baseball. The new methodology addresses one of the most fundamental problems in baseball analytics. Traditional outcome-based statistics for representing player skill on batted balls have been shown to have a low degree of repeatability due to the effects of multiple confounding variables, such as the defense, weather, and ballpark. New sensors have created the opportunity to define batted-ball descriptors that are invariant to these variables. We exploit this opportunity by using a Bayesian model to construct a continuous mapping from a vector of batted-ball parameters to an intrinsic value defined using a linear weights representation for run value. A kernel method is used to learn nonparametric estimates for the component probability density functions in Bayes theorem using a set of over 100 000 batted-ball measurements, while cross validation enables the model to adapt to the size and structure of the data. Properties of the mapping are visualized by considering reduced-dimension subsets of the batted-ball parameter space. The approach separates the intrinsic value of a batted ball at contact from its outcome and, as a result, allows the definition of batted-ball statistics for batters and pitchers that are less subject to systematic bias and random variation than traditional statistics. We use Cronbach’s alpha to show that statistics derived from batted-ball intrinsic values have a higher reliability than the traditional outcome-based statistics and that this leads to more accurate estimates of player talent level that can be used for performance forecasting.

Maximum Likelihood Estimation of Model Uncertainty in Predicting Soil Nail Loads Using Default and Modified FHWA Simplified Methods

Accuracy evaluation of the default Federal Highway Administration (FHWA) simplified equation for prediction of maximum soil nail loads under working conditions is presented in this study using the maximum likelihood method and a large amount of measured lower and upper bound nail load data reported in the literature. Accuracy was quantitatively expressed as model bias where model bias is defined as the ratio of measured to predicted nail load. The maximum likelihood estimation was carried out assuming normal and lognormal distributions of bias. Analysis outcomes showed that, based on the collected data, the default FHWA simplified nail load equation is satisfactorily accurate on average and the spread in prediction accuracy expressed as the coefficient of variation of bias is about 30%, regardless of the distribution type. Empirical calibrations were proposed to the default FHWA simplified nail load equation for accuracy improvement. The Bayesian Information Criterion was adopted to perform a comparison of suitability between the competing normal and lognormal statistical models that were intended for description of model bias. Example of reliability-based design of soil nail walls against internal pullout limit state of nails is provided in the end to demonstrate the benefit of performing model calibration and using calibrated model for design of soil nails.

On the short-term temporal variations of GNSS receiver differential phase biases

As a first step towards studying the ionosphere with the global navigation satellite system (GNSS), leveling the phase to the code geometry-free observations on an arc-by-arc basis yields the ionospheric observables, interpreted as a combination of slant total electron content along with satellite and receiver differential code biases (DCB). The leveling errors in the ionospheric observables may arise during this procedure, which, according to previous studies by other researchers, are due to the combined effects of the code multipath and the intra-day variability in the receiver DCB. In this paper we further identify the short-term temporal variations of receiver differential phase biases (DPB) as another possible cause of leveling errors. Our investigation starts by the development of a method to epoch-wise estimate between-receiver DPB (BR-DPB) employing (inter-receiver) single-differenced, phase-only GNSS observations collected from a pair of receivers creating a zero or short baseline. The key issue for this method is to get rid of the possible discontinuities in the epoch-wise BR-DPB estimates, occurring when satellite assigned as pivot changes. Our numerical tests, carried out using Global Positioning System (GPS, US GNSS) and BeiDou Navigation Satellite System (BDS, Chinese GNSS) observations sampled every 30 s by a dedicatedly selected set of zero and short baselines, suggest two major findings. First, epoch-wise BR-DPB estimates can exhibit remarkable variability over a rather short period of time (e.g. 6 cm over 3 h), thus significant from a statistical point of view. Second, a dominant factor driving this variability is the changes of ambient temperature, instead of the un-modelled phase multipath.

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm.

BackgroundProteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios (m/z), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel ‘continuous’ line segment algorithm that efficiently operates over data with a transformed m/z-axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m/z scale.ResultsThe automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Several of the transformations investigated were able to reduce, if not entirely remove, the peak width and peak location relationship resulting in near-optimal baseline subtraction using the automated pipeline. The proposed novel ‘continuous’ line segment algorithm is shown to far outperform naive sliding window algorithms with regard to the computational time required. The improvement in computational time was at least four-fold on real MALDI TOF-MS data and at least an order of magnitude on many simulated datasets.ConclusionsThe advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.Electronic supplementary materialThe online version of this article (doi:10.1186/s12953-016-0107-8) contains supplementary material, which is available to authorized users.

Swarm accelerometer data processing from raw accelerations to thermospheric neutral densities 2. Aeronomy Swarm Science Results after two years in Space

The Swarm satellites were launched on November 22, 2013, and carry accelerometers and GPS receivers as part of their scientific payload. The GPS receivers do not only provide the position and time for the magnetic field measurements, but are also used for determining non-gravitational forces like drag and radiation pressure acting on the spacecraft. The accelerometers measure these forces directly, at much finer resolution than the GPS receivers, from which thermospheric neutral densities can be derived. Unfortunately, the acceleration measurements suffer from a variety of disturbances, the most prominent being slow temperature-induced bias variations and sudden bias changes. In this paper, we describe the new, improved four-stage processing that is applied for transforming the disturbed acceleration measurements into scientifically valuable thermospheric neutral densities. In the first stage, the sudden bias changes in the acceleration measurements are manually removed using a dedicated software tool. The second stage is the calibration of the accelerometer measurements against the non-gravitational accelerations derived from the GPS receiver, which includes the correction for the slow temperature-induced bias variations. The identification of validity periods for calibration and correction parameters is part of the second stage. In the third stage, the calibrated and corrected accelerations are merged with the non-gravitational accelerations derived from the observations of the GPS receiver by a weighted average in the spectral domain, where the weights depend on the frequency. The fourth stage consists of transforming the corrected and calibrated accelerations into thermospheric neutral densities. We present the first results of the processing of Swarm C acceleration measurements from June 2014 to May 2015. We started with Swarm C because its acceleration measurements contain much less disturbances than those of Swarm A and have a higher signal-to-noise ratio than those of Swarm B. The latter is caused by the higher altitude of Swarm B as well as larger noise in the acceleration measurements of Swarm B. We show the results of each processing stage, highlight the difficulties encountered, and comment on the quality of the thermospheric neutral density data set.

Effect of the Resolution of Tipping-Bucket Rain Gauge and Calculation Method on Rainfall Intensities in an Andean Mountain Gradient

A laser-optical disdrometer served as reference to assess the absolute percent bias of calculated rainfall intensity using the data of different-resolution tipping-bucket rain gauges classically applied by climatologists and hydrologists in the Andean region. Additionally, the impact of the calculation method (tip counting versus cubic spline interpolation) was examined. The combined effect was assessed for different rainfall intensity classes (0–0.99, 1–1.99, 2–4.99, and 5–10 mm·h−1) and timescales (5, 10, 30, and 60 min). Additionally, the variation in percent absolute bias of the Davis rain gauge, the collector most widely used in the study region, was defined with respect to the Texas rain gauge along an elevation gradient between 3300 and 4000 m a.s.l. Results reveal that the value of the percent absolute bias is largest for small rainfall intensities (≤2 mm·h−1) and short timescales (≤10 min), and decreases when the cubic spline interpolation is used. No relation was found between the error, the elevation, and rainfall depth along the gradient. Based on the research findings, it is recommended to measure precipitation in the high Andean mountain region with a high-resolution sensor and to consider cubic spline for the computation of intensities.