Function Of Intensity Research Articles

Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families

We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is fixed and the second layer has a single neuron, our approach resembles previous uses of squared Gaussian process or kernel methods, but allowing the hidden layer to be learnt allows for additional flexibility. In many cases of interest, the integrated intensity function admits a closed form and can be computed in quadratic time in the number of hidden neurons. We enumerate a far more extensive number of such cases than has previously been discussed. Our approach is more memory and time efficient than naive implementations of squared or exponentiated kernel methods or Gaussian processes. Maximum likelihood and maximum a posteriori estimates in a reparameterisation of the final layer of the intensity function can be obtained by solving a (strongly) convex optimisation problem using projected gradient descent. We demonstrate SNEPPPs on real, and synthetic benchmarks, and provide a software implementation.

Bayesian semi-parametric estimation of compound inhomogeneous Poisson processes for ultra-high frequency financial transaction data

Marked point processes provide a flexible framework for studying ultra-high frequency financial data that records the time and price for each transaction. This paper estimates compound, inhomogeneous Poisson processes (CIPP) where trades arrive according to an inhomogeneous Poisson process, and returns are drawn from a distribution after arrival. The intensity functions have Gaussian process priors to model time-of-day (diurnal) effects, which have bursts of activity at the open and close and a midday lull, and lagged returns from the previous trade, which are idiosyncratic and depend on the stock. The nonparametric density estimator for returns imposes a shape constraint to obtain a unimodal distribution that is asymmetric and leptokurtic. A mixture model separates trades into time sensitive and time insensitive trades with different intensity functions. The latter group tends to have larger orders and higher returns. The proposed model is compared to B-splines and geometric Brownian motion.

Sound waves alter the viability of tobacco cells via changes in cytosolic calcium, membrane integrity, and cell wall composition.

The effect of sound waves (SWs) on plant cells can be considered as important as other mechanical stimuli like touch, wind, rain, and gravity, causing certain responses associated with the downstream signaling pathways on the whole plant. The objective of the present study was to elucidate the response of suspension-cultured tobacco cells (Nicotiana tabacum L. cv Burley 21) to SW at different intensities. The sinusoidal SW (1,000 Hz) was produced through a signal generator, amplified, and beamed to the one layer floating tobacco cells inside a soundproof chamber at intensities of 60, 75, and 90 dB at the plate level for 15, 30, 45, and 60 min. Calibration of the applied SW intensities, accuracy, and uniformity of SW was performed by a sound level meter, and the cells were treated. The effect of SW on tobacco cells was monitored by quantitation of cytosolic calcium, redox status, membrane integrity, wall components, and the activity of wall modifying enzymes. Cytosolic calcium ions increased as a function of sound intensity with a maximum level of 90 dB. Exposure to 90 dB was also accompanied by a significant increase of H2O2 and membrane lipid peroxidation rate but the reduction of total antioxidant and radical scavenging capacities. The increase of wall rigidity in these cells was attributed to an increase in wall-bound phenolic acids and lignin and the activities of phenylalanine ammonia-lyase and covalently bound peroxidase. In comparison, in 60- and 75 dB, radical scavenging capacity increased, and the activity of wall stiffening enzymes reduced, but cell viability showed no changes. The outcome of the current study reveals that the impact of SW on plant cells is started by an increase in cytosolic calcium. However, upon calcium signaling, downstream events, including alteration of H2O2 and cell redox status and the activities of wall modifying enzymes, determined the extent of SW effects on tobacco cells.

Disparities in Patient-reported Outcome Measure Completion Rates and Baseline Function in Newly Presenting Spine Patients.

Retrospective review. The purpose of this study was to evaluate disparities in patient-reported outcome measures (PROM) completion rates and baseline function scores among newly presenting spine patients. Prior studies have demonstrated that minority patients and those of low socioeconomic status may present with worse physical and mental health on PROMs. As PROMs are increasingly used in clinical care, research, and health policy, accurate assessment of health status among populations relies on the successful completion of PROM instruments. A retrospective review of 10,803 consecutive new patients presenting to a single multidisciplinary spine clinic from June 2020 to September 2022 was performed. Univariate statistics were performed to compare demographics between patients who did and did not complete PROMs. Multivariable analysis was used to compare PROM instrument completion rates by race, ethnicity, and Social Vulnerability Index (SVI) and baseline scores among responders. A total of 68.4% of patients completed PROMs at the first clinic visit. After adjusting for age, sex, body mass index, and diagnosis type, patients of non-White race (OR=0.661, 95% CI=0.599-0.729, P <0.001), Hispanic ethnicity (OR=0.569, 95% CI=0.448-0.721, P <0.001), and increased social vulnerability (OR=0.608, 95% CI=0.511-0.723, P <0.001) were less likely to complete PROMs. In the multivariable models, patients of non-White race reported lower levels of physical function (β=-6.5, 95% CI=-12.4 to -0.6, P =0.032) and higher levels of pain intensity (β=0.6, 95% CI=0.2-1.0, P =0.005). Hispanic ethnicity (β=1.5, 95% CI=0.5-2.5, P =0.004) and increased social vulnerability (β=1.1, 95% CI=0.4-1.8, P =0.002) were each associated with increased pain intensity. Among newly presenting spine patients, those of non-White race, Hispanic ethnicity, and with increased social vulnerability were less likely to complete PROMs. As these subpopulations also reported worse physical function or pain intensity, additional strategies are needed to better capture patient-reported health status to avoid bias in clinical care, outcomes research, and health policy. 4.

A finite failure software reliability model using extended log-logistic distribution

Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected number of failures over time using the mean value function. In addition, these models have an intensity function representing the failure rate over time. This article proposes a new non-homogeneous Poisson process model based on extended log-logistic (NHPP ELL) distribution with three parameters. The model properties were derived and represented graphically. Furthermore, the maximum likelihood estimation method was utilized to estimate the model parameters, which were computed numerically using the Newton–Raphson method. Three real software reliability datasets were utilized to evaluate the performance of the new NHPP ELL model based on three distinct criteria: the mean square error, the coefficient of determination (R2), the Theil statistic, the predictive-ratio risk, and the predictive power. Moreover, a comparative study was conducted between the proposed model and several well-known NHPP models regarding goodness-of-fit and predictive performance. All the evaluation measures indicate that the proposed model outperforms the other models across all the considered datasets. In addition, the graphical plots depicting the actual data and the prediction results for all the fitted models show that the proposed model performs well in all three considered software reliability datasets compared to other models.

Rehabilitation for adults with an intellectual disability and mental health and behavioural complexities: A scoping review

AbstractBackgroundThere has been significant focus in the past decade on reducing admissions to assessment and treatment units for people with intellectual disabilities experiencing mental health or behavioural concerns. This has included the development of intensive support functions of National Health Service Learning Disability services to bolster community support and treatment for people at risk of admission. However, in recent years, there has been increased consideration of service development to include rehabilitation functions which would aim to proactively provide medium‐term additional input to individuals with a complexity of need. This input would aim to improve future trajectories and support the individuals to remain living a good life in their own community. This review focuses on community and in‐patient approaches to meeting a complexity of need.MethodA scoping review was carried out according to established best practice guidelines. Papers returned from the search were screened by the following inclusion criteria (a) Models or Outcomes of Rehabilitation; (b) Intellectual Disability Population; (c) Sample being 18 or above and (d) longer than short‐term stay/treatment, defined as 6 months or longer; and (e) Mental health and/or behaviour complexities. The search was conducted in electronic databases CINAHL, PsycInfo, Medline, Embase and Social Policy &amp; Practice. Here, 3790 articles were initially identified and 27 were ultimately included in the review.FindingsThere are few studies evaluating rehabilitation for people with intellectual disabilities and mental health or behavioural concerns. There was some evidence that accessing assessment and treatment inpatient provision resulted in clinical improvements. Some studies demonstrated initial evidence that longer term rehabilitation was beneficial as a step‐down from inpatient care. Some key principles were identified in terms of rehabilitation approaches: the need for person‐centred creative approaches, suitable staff training, focus on building skills, and a focus on increasing quality of life.ConclusionThere are some indicators of what good rehabilitation services might comprise for people with an intellectual disability, who have rehabilitation needs. However, far more research and guidance are required in this area. In particular, it is unclear whether rehabilitation is best provided within an inpatient or community model, and further detail is required about optimal components of such rehabilitation.

A novel algorithm to generate representative volume elements with cylindrical fibers and sphere particles

In the article, we propose a new method to generate representative volume element (RVE) of random spatial distributions with cylindrical fibers and spherical particles based on the Improved Artificial Bee Colony (IABC) algorithm. In this employed bee phase of IABC, the proposed algorithm adopts a new search strategy, which improves the computational efficiency. To analyze the differences between the fiber distribution generated by this algorithm and the actual fiber distribution, we conducted statistical analysis on the distribution of the generated RVE. The statistical analysis results of the nearest neighbor distance (NND), Second-order intensity function, and pair correlation function were in good agreement with the experiment and complete spatial randomness (CSR) patterns. In addition, the predicted elastic properties were compared with experimental measurements and theoretical prediction methods. The results demonstrate the accuracy and efficiency of the proposed algorithm. This algorithm can quickly generate the RVE of cylindrical fibers and spherical particles with volume fractions of 80% and 62%, and the calculation time for generating the RVE of cylindrical fibers is about 17s.

A novel point process model for neuronal spike trains

Point process provides a mathematical framework for characterizing neuronal spiking activities. Classical point process methods often focus on the conditional intensity function, which describes the likelihood at any time point given its spiking history. However, these models do not describe the central tendency or importance of the spike train observations. Based on the recent development on the notion of center-outward rank for point process, we propose a new modeling framework on spike train data. The new likelihood of a spike train is a product of the marginal probability on the number of spikes and the probability of spike timings conditioned on the same number. In particular, the conditioned distribution is calculated by adopting the well-known Isometric Log-Ratio transformation. We systematically compare the new likelihood with the state-of-the-art point process likelihoods in terms of ranking, outlier detection, and classification using simulations and real spike train data. This new framework can effectively identify templates as well as outliers in spike train data. It also provides a reasonable model, and the parameters can be efficiently estimated with conventional maximum likelihood methods. It is found that the proposed likelihood provides an appropriate ranking on the spike train observations, effectively detects outliers, and accurately conducts classification tasks in the given data.

Bayesian analysis on a natural conjugate prior for the nonhomogeneous Poisson process with a power-law intensity under time-truncated sampling

The core content in this paper discusses the Bayesian approach, which essentially estimates and predicts the reliability of a repairable system during the actual testing process. As specified in previous literature, the nonhomogeneous Poisson process (NHPP) with a power-law intensity function has often been employed as a model for describing the failure process of repairable systems since it provides a reasonable way of presenting the rate of change for the reliability of a system as a function of time. This research proposed the same time-truncated sampling assumption to mainly ensure that the joint posterior distribution of the power-law failure process has a considerably closed-form representation. This proposed approach facilitates the analysis of the actual performance, with which analysts can provide the prior information about the authentic means and variations of the shape and scale parameters of the power-law intensity. The corresponding posterior means and variations can then be significantly and easily obtained without any complex integral arithmetic. Using Monte Carlo simulation for comparison, the Bayesian estimation with the proposed prior outperforms the maximum likelihood estimation, particularly when the prior aging of the repairable is underestimated.

Efficacy of Injectable Platelet-Rich Fibrin (I-PRF) in Managing Temporomandibular Joint Pain: A Prospective Clinical Study.

Temporomandibular joint (TMJ) pain is a common condition that can significantly impact an individual's quality of life. Current treatment options often fall short of providing long-lasting relief. So, this prospective clinical study aimed to investigate the efficacy of injectable platelet-rich fibrin (I-PRF) in mitigatingTMJpain. A total of 68 participants, aged 18-60 years, clinically diagnosed with TMJ pain, were recruited from dental clinics and specialist referrals. Participants were randomly assigned to either the intervention group (I-PRF injections) or the control group (placebo). Primary outcome measures included TMJ pain intensity and jaw function, assessed using the Visual Analog Scale and functional examinations, respectively. Secondary outcomes comprised patient-reported outcomes (PROs) on quality of life and satisfaction. Data were collected at baseline and six, 12, and 24 weeks post intervention. Baseline characteristics demonstrated successful randomization, with no significant differences in age, gender, or TMJ pain duration between groups. Post intervention, the intervention group exhibited a significant and sustained reduction in TMJ pain intensity compared to controls (p<0.001). Improvements in jaw function were also notable in the intervention group at all follow-up time points (p<0.001). PROs related to quality of life and satisfaction substantially increased in the intervention group compared to controls (p<0.001). I-PRF demonstrated significant efficacy in reducing TMJ pain intensity, improving jaw function, and enhancing PROs. These findings support the consideration of I-PRF as a valuable therapeutic intervention for individuals with TMJ pain.

Spatiotemporal response of ecosystem services to tourism activities in urban forests

Tourism in urban forests is rapidly becoming an increasing trend; however, rather few studies have used quantitative measurement to describe the relationship between tourism intensity and ecological functions. This study provides a practical framework that integrates ecosystem service value (ESV) assessment, Internet big data mining and spatial regression analysis to identify the spatial response of ESV and land use/land cover change to tourism activities from 2009 to 2019 in the Siming Mountain Region (SMR), a famous tourist resort located in the eastern coastal China. Results showed that between 2009 and 2019 total ESV increased by 7.1%. Nevertheless, there have been drastic transitions in land use types with function adjustments from traditional agricultural production to diversified tourism-oriented services. Significant spatial autocorrelation was identified for the patterns of ESV changes. GWR further highlighted that the relationship between ESV change and rural tourism indicators varied in space. ESV change in the core zone was negatively correlated with changes in catering service spots and recreational venues, whereas it was positively correlated with local lodgings. Ultimately, targeted recommendations and countermeasures for spatial planning and sustainable tourism development of urban forests under new circumstances were discussed.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Steel Fiber Orientation Efficiency Factor Model for a Magnetically Treated Cement-Based Composite

In typical steel fiber-reinforced concrete, the fibers are randomly distributed and oriented throughout the matrix, and a magnetic field can effectively align these randomly oriented fibers. To predict the extent to which the steel fibers contained in mortar can be aligned by a magnetic field, an analytical steel fiber orientation efficiency factor model was proposed as a function of the magnetic induction intensity and exposure time. To verify the applicability of the proposed model, experiments were conducted for various magnetic induction intensities and exposure times with normal mortars and mortars in which some or all the sand was replaced with steel slag. The experimental results demonstrate that the proposed model allows predicting the degree of alignment of steel fibers under magnetic fields. However, this model can only be applied to a normal mortar. In the case of mortar containing steel slag, it is confirmed that the steel slag, which is a ferrous material, reduces the magnetic induction intensity, reducing the degree of alignment of steel fibers in the mortar.

Flood prediction using nonlinear instantaneous unit hydrograph and deep learning: A MATLAB program

In this study, we developed a MATLAB program for flood prediction in a watershed. The program consists of three modules. The instantaneous unit hydrograph (IUH) generation module utilizes a power-law based interpolation method to generate IUHs. The generated IUH is a function of the rainfall excess intensity and therefore considers nonlinearity. The long short-term memory (LSTM) module employs “lstmLayer” from the MATLAB deep learning toolbox to predict total rainfall excess; this is then used to estimate the curve number (CN) value for each flood event. The LSTM module uses a land surface modeling dataset and rainfall-runoff data as inputs. The flood hydrograph generation module calculates effective rainfall hyetographs and then predicts flood hydrographs using a convolution integration. A detailed description of the program is provided along with an application example for real watersheds. The application results demonstrated that our program can be effectively used for flood prediction in practice, particularly for large flood events.

An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients

If scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n = 12 981, 53.4% women and 46.6% men) and 7th (n = 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61–0.57). Vigorous physical activity (exercising to exhaustion ≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56–0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.

40 Hz light flickering promotes sleep through cortical adenosine signaling

Flickering light stimulation has emerged as a promising non-invasive neuromodulation strategy to alleviate neuropsychiatric disorders. However, the lack of a neurochemical underpinning has hampered its therapeutic development. Here, we demonstrate that light flickering triggered an immediate and sustained increase (up to 3 h after flickering) in extracellular adenosine levels in the primary visual cortex (V1) and other brain regions, as a function of light frequency and intensity, with maximal effects observed at 40 Hz frequency and 4000 lux. We uncovered cortical (glutamatergic and GABAergic) neurons, rather than astrocytes, as the cellular source, the intracellular adenosine generation from AMPK-associated energy metabolism pathways (but not SAM-transmethylation or salvage purine pathways), and adenosine efflux mediated by equilibrative nucleoside transporter-2 (ENT2) as the molecular pathway responsible for extracellular adenosine generation. Importantly, 40 Hz (but not 20 and 80 Hz) light flickering for 30 min enhanced non-rapid eye movement (non-REM) and REM sleep for 2–3 h in mice. This somnogenic effect was abolished by ablation of V1 (but not superior colliculus) neurons and by genetic deletion of the gene encoding ENT2 (but not ENT1), but recaptured by chemogenetic inhibition of V1 neurons and by focal infusion of adenosine into V1 in a dose-dependent manner. Lastly, 40 Hz light flickering for 30 min also promoted sleep in children with insomnia by decreasing sleep onset latency, increasing total sleep time, and reducing waking after sleep onset. Collectively, our findings establish the ENT2-mediated adenosine signaling in V1 as the neurochemical basis for 40 Hz flickering-induced sleep and unravel a novel and non-invasive treatment for insomnia, a condition that affects 20% of the world population.

Mathematical modeling of thin-layer drying kinetics and moisture diffusivity study of apple slices using infrared conveyor-belt dryer.

The drying features of apples at different infrared drying settings were investigated. The drying time, moisture-effective diffusion, and activation energy of infrared dried apples were measured experimentally and statistically as a function of slice thicknesses, radiation intensity, and air velocity. The infrared intensity of 0.225, 0.130, and 0.341W/cm2 , slice thicknesses of 6, 4, and 2mm, and airflow of 0.5, 1.0, and 1.5m/s were used to dry apple slices. The data shows that the drying time reduced as IR increased, but airflow and slice thickness increased. Eight statistical factors were used to compare 11 alternative mathematical drying models. The experimentally acquired drying curves were matched to the thin-layer drying equations. According to the calculations, the Midilli etal. equation had the greatest (efficiency and R2 ) and lowest (χ2 , sum of squared errors, standard error of estimate, standard error, standard deviation of difference) values. As a result, this equation is the best for modeling the drying curves of apple slices across all drying circumstances. The optimum moisture diffusivity value varied from 2.59 to 9.07 × 10-10 m2 /s. The mean activation energy was determined to be 19.02-29.83kJ/mol under various experimental conditions.

Ligand Desorption and Fragmentation in Oleate-Capped CdSe Nanocrystals under High-Intensity Photoexcitation.

Semiconductor nanocrystals (NCs) offer prospective use as active optical elements in photovoltaics, light-emitting diodes, lasers, and photocatalysts due to their tunable optical absorption and emission properties, high stability, and scalable solution processing, as well as compatibility with additive manufacturing routes. Over the course of experiments, during device fabrication, or while in use commercially, these materials are often subjected to intense or prolonged electronic excitation and high carrier densities. The influence of such conditions on ligand integrity and binding remains underexplored. Here, we expose CdSe NCs to laser excitation and monitor changes in oleate that is covalently attached to the NC surface using nuclear magnetic resonance as a function of time and laser intensity. Higher photon doses cause increased rates of ligand loss from the particles, with upward of 50% total ligand desorption measured for the longest, most intense excitation. Surprisingly, for a range of excitation intensities, fragmentation of the oleate is detected and occurs concomitantly with formation of aldehydes, terminal alkenes, H2, and water. After illumination, NC size, shape, and bandgap remain constant although low-energy absorption features (Urbach tails) develop in some samples, indicating formation of substantial trap states. The observed reaction chemistry, which here occurs with low photon to chemical conversion efficiency, suggests that ligand reactivity may require examination for improved NC dispersion stability but can also be manipulated to yield desired photocatalytically accessed chemical species.

Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics.

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS. The variance of data-independent acquisition (SWATH-MS) data was isolated from sample preparation error by modelling the variance as a function of peptide signal intensity. Our SDS-assisted cartridge workflow yield a coefficient of variance (CV) of 13%-14%. By comparison, conventional (detergent-free) in-solution digestion increased the CV to 50%; in-gel digestion provided lower CVs between 14% and 20%. By filtering peptides predicting to display lower precision, we further enhance the validity of data in global comparative proteomics. These results demonstrate the detergent-based precipitation workflow is a reliable approach for in depth, label-free quantitative proteome analysis.

Nonparametric estimation of aging intensity function for right-censored dependent data

Aging Intensity (AI) function is a quantitative measure of hazard function (hazard rate/failure rate), which is used for evaluating the aging behaviour of a component/system. Although variety of research are now available on various properties such as modelling and analysis of AI function; however, a detailed theoretical study on the estimation of the same has not been considered. Accordingly, in the present study, we propose two nonparametric estimators for aging intensity function based on right-censored dependent data scheme and study their properties. Asymptotic properties of the estimators are established under suitable regularity conditions. A simulation study and real data analysis have been carried out to illustrate the performance of the estimators.