Sensitive Input Research Articles

Subjectivity of visual assessments in FOCUS kinetics and acceptability of first-order fits for regulatory modelling

The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization.Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration.It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments.

SIGMA: Secure GPT Inference with Function Secret Sharing

Secure 2-party computation (2PC) enables secure inference that offers protection for both proprietary machine learning (ML) models and sensitive inputs to them. However, the existing secure inference solutions suffer from high latency and communication overheads, particularly for transformers. Function secret sharing (FSS) is a recent paradigm for obtaining efficient 2PC protocols with a preprocessing phase. We provide Sigma, the first end-to-end system for secure transformer inference based on FSS. By constructing new FSS-based protocols for complex machine learning functionalities, such as Softmax, GeLU and SiLU, and also accelerating their computation on GPUs, Sigma improves the latency of secure inference of transformers by 11 - 19x over the state-of-the-art that uses preprocessing and GPUs. We present the first secure inference of generative pre-trained transformer (GPT) models. In particular, Sigma executes Meta's Llama2 (available on HuggingFace) with 13 billion parameters in 44 seconds and GPT2 in 1.6 seconds

P-Chain: Towards privacy-aware smart contract using SMPC

Smart contract, as the representative application of blockchain, has recently fueled extensive research interests from both academia and industry. However, with its wide applications, the weaknesses of smart contract have been gradually revealed. The major barrier to the widespread adoption of smart contract involves concerns about on-chain privacy which refers to the details of input/output privacy. To address privacy concerns, we propose in this paper, P-Chain, a privacy-aware framework for smart contracts of permissioned blockchain to protect sensitive data of users based on Secure Multi-party Computation (SMPC). Unlike existing work that suffer several key drawbacks, including introducing a third party who could get the details of the deal, and high overhead for on-chain and off-chain communication, as well as lacking a privacy protection for output data, we enhance the privacy protection for smart contracts system by adding a new secure multi-party computation layer in P-Chain. Through secure multi-party computing, sensitive inputs of smart contracts are divided into multiple sub-inputs and sent to computing participants for operation respectively, which ensures that each participant can only access part of the user’s information. A stochastic strategy based on (t;n) threshold secret sharing to select calculating parties is also been proposed, which makes it difficult for an attacker to aggregate t of n participants for launching a collusive attack. In addition, we propose the output privacy protection method that makes it possible to reach a consensus without the need to know the output. The extensive experimental evaluation and analysis demonstrate that our scheme enjoys the advantages of calculation correctness, input–output privacy as well as anti-collusion.

Consensus for Heterogeneous Multiagent Systems: Output Rate-Coded Secure Control.

This article investigates an output rate-coded secure control based on backstepping for the consensus of heterogeneous multiagent systems (MASs) with output-triggering condition. Due to the nondifferentiable virtual control inputs caused by discontinuous triggering signals, it presents a technical obstacle in implementing the recursive backstepping, rendering previous results inapplicable. To address this problem, an auxiliary high-order filter is elaborately constructed to guarantee the required-order derivatives of virtual control inputs. As this filter is driven by local triggering consensus error, it allows our MASs to have different system orders and relative degrees. Besides, to reduce communication bit and strengthen communication security, we propose a novel distributed rate-coded algorithm from an encoding-decoding viewpoint. When it is triggered, agent's output is encrypted into an L -length codeword, then transmitted to neighbors without exposing any sensitive system states or real control inputs. It is shown that all the closed-loop system signals are ultimately bounded, and the mean square consensus tracking error can be reduced by appropriately selecting control parameters. Simulations illustrate our theoretical finding.

Hydrogen storage systems performance and design parameters using response surface methods and sensitivity analysis

DesignDesign of metal hydride-based hydrogen storage reactors is often performed using numerical/experimental modelling which is computationally/economically difficult. This paper investigates the applicability of Response Surface Methodology (RSM) coupled Local/Global Sensitivity Analysis (L/GSA) to investigate – i) the applicability of advanced RSMs in predicting the responses for storage systems efficiently, ii) the applicability of advanced RSMs to perform L/GSA to identify the sensitive input design parameters based on their effect on the Outputs of Interests (OIs), i.e., reaction fraction (i.e., C) and bed temperature (i.e., T), and iii) the dependence of importance ranking of design parameters on the employed L/GSA methodology. The study is conducted in two stages. In the first stage, the most accurate RSM was identified among fourteen traditional and advanced RSMs, i.e., radial basis, kriging, quadratic, moving least square, support vector machine etc., employing a measure of precision, i.e., Nash–Sutcliffe Efficiency (NSE). RSMs were constructed based on the values of OIs estimated using finite element simulation using COMSOL software for random realizations of inputs generated via Latin Hypercube Sampling (LHS). In the second stage, the importance ranking of design parameters was estimated for both OIs using six different L/GSAs based on the input-output relationships estimated in stage one. All the codes of RSMs and L/GSAs were written and validated in MATLAB. Finite element simulations of the random realizations were performed using COMSOL software. For the present study, NSEs of the considered RSMs were ranging between 0.6262-0.8544 and 0.4652–0.8081 for C and T respectively, indicating the importance of selection of appropriate RSM. RBF-augmented Compact-I and kriging were the most accurate RSMs with NSEs approximately 10%–20 % higher to those of frequently used polynomial RSM. Time (t) and mass of hydrogen to be stored (MH) were the most; and external temperature (Text) and porosity (E) were the least sensitive inputs corresponding to C and T, with differences of 80–90 % in the sensitivity indices respectively. The ranking prediction was highly dependent upon the employed L/GSA methodology, with Morris's screening observed to be the least accurate. The RSM methods described in this study help to design and investigate the metal hydride reactors for various applications (space heating, hydrogen storage, storage for vehicular application, metal hydride compressor) without undergoing detailed mathematical modelling of the system. The proposed methodology may significantly assist the designers to focus (or vary) on the sensitive inputs only during the physical modelling of systems to improve their performance. This sensitivity analysis is helpful to find out the most advance sensitivity analysis method which can be used to find out the most sensitivity parameter which can be varied according to their rankings to achieve the required performance of metal hydride reactor for the particular application. The advanced RSMs assist to identify these sensitive inputs quickly by reducing the mathematical efforts in the L/GSA by providing the accurate input-OIs relationships based on the limited numerical simulations. This will significantly save the resources and time of industries required in physical modelling/numerical simulations significantly, which otherwise would have been invested on investigating the non-sensitive inputs.

Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.

From threat to opportunity: Hydrologic uncertainty regionalization across large domains

Study regionThe study was carried out in Northern Canada (90,000 km2) and Southern Canada (18,000 km2). These basins represent case studies with a larger application target of high latitude regions. Study focusAs model domains become large, ungauged basins are inevitably encountered, and basin heterogeneity increases. This study aims to develop the computationally frugal Model Agnostic Uncertainty Transfer (MAUT) method to regionalize an uncertainty analysis from a set of donor basins to a receiver. The goal is an evaluation of the MAUT method for regionalizing over sufficiently heterogeneous domains to be applicable to high latitude data sparse regions. New hydrological insights for the regionWe propose the Model Agnostic Uncertainty Transfer (MAUT) method, a surrogate method to perform uncertainty analysis in large-domain modelling with a focus on high-latitude regions where data scarcity is especially severe. With the MAUT method, antecedent precipitation and simulated flow are related. Deviation from this relationship is assumed to represent modelling uncertainty and is quantified by quantile regression lines. These lines act as transfer functions requiring only antecedent precipitation to generate uncertainty bounds. The MAUT method requires uncertainty donors and a target receiver model. Key results suggest the method is relatively insensitive to precipitation dataset differences. Simulation length was the most sensitive input, with 15–20 years being ideal for reasonable results. Overall, the MAUT method is viable for high latitude domains.

Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar.

The integration of optimization techniques and deep learning models, which offer a promising avenue for improving the efficiency and sustainability of biodiesel production processes from baobab seed oil (BSO), is rare. This study utilized a multi-input-multioutput (MIMO) deep learning technique and the most recent central composite design (CCD) optimization tool to model and optimize the yield and properties of biodiesel produced from BSO. First, the baobab seed oil was extracted using a solvent extraction method. BSO was subsequently analyzed and converted to biodiesel by reacting CH3OH catalyzed by waste banana bunch stalk biochar activated by KOH. Multiobjective optimization and prediction of the biodiesel yield (Y) and several key fuel properties, including the cetane number (CN), kinematic viscosity (VS), and purity (P), were achieved. With better correlation coefficients of 0.9709, 0.9464, and 0.9714 for response training, response testing, and response validation, respectively, and a root-mean-square error of 0.00755, the MIMO model on the logsig transfer function accurately predicted the biodiesel yield and properties more than did the MISO and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature of 56 °C, a reaction time of 115 min, a CH3OH/BSO molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis revealed that the CH3OH/BSO ratio was the most sensitive (50.9%) input predictor among the other input variables studied.

Eisenia fetida-driven vermitechnology for the eco-friendly transformation of steel waste slag into organic amendment: An insight through microbial diversity and multi-model approach

The processing of steel waste slag from the black metallurgical sector seriously threatened the ecology. To counter these dangers, appropriate detoxification methods were required. Vermitechnology was one such strategy that could successfully convert this industrial waste into nutrient-rich products suitable for use in agriculture. This research primarily focuses on employing vermitechnology for the transformation of waste steel slag into vermicompost and to determine changes in microbial composition, nutrient cycling, and metal detoxification facilitated by earthworms (Eisenia fetida). Earthworm populations in steel waste vermibeds (sw-vermibeds) increased by 2.87–3.07 folds. T1(SW + CD-1:1) comparatively showed increased levels of nutrients such as nitrogen, phosphorus, and potassium. Microbial and enzymatic parameters were more pronounced in treatment T1. The findings of phospholipid fatty acid (PLFA) diversity demonstrate microbial diversity and fatty acid composition. Based on PLFA Sobol Sensitivity Analysis (SSA), PUFA and cyclo were the most sensitive inputs to the presence of heavy metal (HMs) concentrations in SW. In accordance with Taylor-based modelling, R-tree, and Mars were the most trusted regression models for predicting HMs toxicity on microbes. The bioavailable metal fractions of HMs (Fe, Ni, Cd, Cu, Pb, and Cr) decreased by 61–83%. The correlation was performed for 0 and 90 days for metal microbial interactions r (0 days), [BSR vs Fe, Cd, Cu, Ni = −0.99, −0.82, −0.43, −0.99] and r (90 days), [FDA vs Fe, Cu, Ni = −0.97, −0.47, −0.95]. Overall, the results indicated that T1(1:1 SW + CD) provided more favorable conditions for the development of microbes and Eisenia fetida. This research presents a new perspective to the world community on the transformation of harmful steel waste slag into advantageous biological resources by introducing a novel method of employing Eisenia fetida to remediate hazardous steel waste slag.

The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) – sensitivity studies and evaluation using airborne measurements of power plant emissions

Abstract. The Atmospheric Radionuclide Transport Model (ARTM) operates at the meso-γ scale and simulates the dispersion of radionuclides originating from nuclear facilities under routine operation within the planetary boundary layer. This study presents the extension and validation of this Lagrangian particle dispersion model and consists of three parts: (i) a sensitivity study that aims to assess the impact of key input parameters on the simulation results, (ii) the evaluation of the mixing properties of five different turbulence models using the well-mixed criterion, and (iii) a comparison of model results to airborne observations of carbon dioxide (CO2) emissions from a power plant and the evaluation of related uncertainties. In the sensitivity study, we analyse the effects of the stability class, roughness length, zero-plane displacement factor, and source height on the three-dimensional plume extent as well as the distance between the source and maximum concentration at the ground. The results show that the stability class is the most sensitive input parameter as expected. The five turbulence models are the default turbulence models of ARTM 2.8.0 and ARTM 3.0.0, one alternative built-in turbulence model of ARTM, and two further turbulence models implemented for this study. The well-mixed condition tests showed that all five turbulence models are able to preserve an initially well-mixed atmospheric boundary layer reasonably well. The models deviate only 6 % from the expected uniform concentration below 80 % of the mixing layer height, except for the default turbulence model of ARTM 3.0.0 with deviations of up to 18 %. CO2 observations along a flight path in the vicinity of the lignite power plant Bełchatów, Poland, measured by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Cessna aircraft during the Carbon Dioxide and Methane Mission (CoMet) campaign in 2018 allowed for evaluation of model performance for the different turbulence models under unstable boundary layer conditions. All simulated mixing ratios are of the same order of magnitude as the airborne in situ data. An extensive uncertainty analysis using probability distribution functions, statistical tests, and direct spatio-temporal comparisons of measurements and model results help to quantify the model uncertainties. With the default turbulence setups of ARTM versions 2.8.0 and 3.0.0, the plume widths are underestimated by up to 50 %, resulting in a strong overestimation of the maximum plume CO2 mixing ratios. The comparison of the three alternative turbulence models shows good agreement of the peak plume CO2 concentrations, the CO2 distribution within the plumes, and the plume width, with a 30 % deviation in the peak CO2 concentration and a less than 25 % deviation in the measured CO2 plume width. Uncertainties in the simulations may arise from the different spatial and temporal resolutions of simulations and measurements in addition to the turbulence parametrisation and boundary conditions. The results of this work may help to improve the accurate representation of real plumes in very unstable atmospheric conditions through the selection of distinct turbulence models. Further comparisons at different stability regimes are required for a final assessment of model uncertainties.

Mouse Data Attack Technique Using Machine Learning in Image-Based User Authentication: Based on a Defense Technique Using the WM_INPUT Message

Recently, as the non-face-to-face society persists due to the coronavirus (COVID-19), the Internet usage rate continues to increase, and input devices, such as keyboards and mice, are mainly used to authenticate users in non-face-to-face environments. Due to the nature of the non-face-to-face environment, important personal data are processed, and since these personal data include authentication information, it is very important to protect them. As such, personal information, including authentication information, is entered mainly from the keyboard, and attackers use attack tools, such as keyloggers, to steal keyboard data in order to grab sensitive user information. Therefore, to prevent disclosure of sensitive keyboard input, various image-based user authentication technologies have emerged that allow sensitive information, such as authentication information, to be entered via mouse. To address mouse data stealing vulnerabilities via GetCursorPos() function or WM_INPUT message, which are representative mouse data attack techniques, a mouse data defense technique has emerged that prevents attackers from classifying real mouse data and fake mouse data by the defender generating fake mouse data. In this paper, we propose a mouse data attack technique using machine learning against a mouse data defense technique using the WM_INPUT message. The proposed technique uses machine learning models to classify fake mouse data and real mouse data in a scenario where the mouse data defense technique, utilizing the WM_INPUT message in image-based user authentication, is deployed. This approach is verified through experiments designed to assess its effectiveness in preventing the theft of real mouse data, which constitute the user’s authentication information. For verification purposes, a mouse data attack system was configured, and datasets for machine learning were established by collecting mouse data from the configured attack system. To enhance the performance of machine learning classification, evaluations were conducted based on data organized according to various machine learning models, datasets, features, and generation cycles. The results, highlighting the highest performance in terms of features and datasets were derived. If the mouse data attack technique proposed in this paper is used, attackers can potentially steal the user’s authentication information from various websites or services, including software, systems, and servers that rely on authentication information. It is anticipated that attackers may exploit the stolen authentication information for additional damages, such as voice phishing. In the future, we plan to conduct research on defense techniques aimed at securely protecting mouse data, even if the mouse data attack technique proposed in this paper is attempted.

Parameter Identification of a Large-Scale Vibroacoustic Finite Element Model with a One-Dimensional Convolutional Neural Network

Uncertainties are significant in the early vibroacoustic development, e.g., of a car body, to prevent costly modifications close to the start of production. First, engineers must know which uncertain parameters are sensitive: Our previous work identified 170 uncertain parameters being sensitive out of a complex finite element model with 1,300 uncertain parameters – a parameter reduction of approximately 87%. Second, engineers aim to find reliable distributions of these sensitive input parameters for finite element simulations. Finding these distributions is very demanding in a large-scale vibroacoustic model with several connecting parameters, as research already acknowledges regarding simplified connections. In this paper, we address this challenge with neural networks. For this, we use data in the frequency domain. Due to the curse of dimensionality, it is difficult to determine the parameter set of 170 parameters with a neural network. Therefore, we examine the influence of the number of parameters on the performance of neural networks. Furthermore, we train a fully connected feed-forward neural network and compare this to a one-dimensional convolutional neural network. The latter exhibits a better performance. Finally, we show how to determine distributions of the analyzed parameters based on artificial measurement data. Due to this process, we can significantly improve our finite element simulations and show how to deal with the challenge of determining uncertain parameters in a large-scale vibroacoustic finite element model based on data in the frequency domain.

Recharge and vulnerability assessment of groundwater resources in North west India: Insights from isotope-geospatial modelling approach

Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India, raising concern over freshwater availability to about 182 million population residing in this region. Widespread agricultural activities have resulted severe groundwater pollution in this area, demanding a systematic vulnerability assessment for proactive measures. Conventional vulnerability assessment models encounter drawbacks due to subjectivity, complexity, data-prerequisites, and spatial–temporal constraints. This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model. The isotope methodology provides crucial insights on groundwater recharge mechanisms (18O and 2H) and dynamics (3H) - often ignored in vulnerability assessment. Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line (LMWL) as well as inferring contrasting recharge mechanisms operating in different aquifers. Shallow aquifer (depth < 60 m) showed significant evaporative signature with evaporation loss accounting up to 18.04% based on Rayleigh distillation equations. Inter-aquifer connections were apparent from Kernel Density Estimate (KDE) and isotope correlations. A weighted overlay isotope-geospatial model was developed combining 18O, 3H, aquifer permeability, and water level data. The central and northern parts of study area fall under least (0.29%) and extremely (1.79%) vulnerable zones respectively, while majority of the study area fall under moderate (42.71%) and highly vulnerable zones (55.20%). Model validation was performed using groundwater NO3– concentration, which showed an overall accuracy up to 82%. Monte Carlo Simulation (MCS) was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter, followed by 3H, 18O, and water level. Comparing the vulnerability map with Land Use Land Cover (LULC) and population density maps helped in precisely identifying the high-risk sites, warranting a prompt attention. The model developed in this study integrates isotopic information with vulnerability assessment and resulted in model output with good accuracy, scientific basis, and widespread relevance, which highlights its crucial role in formulating proactive water resource management plans, especially in less explored data-scarce locations.

Cetacean audiogram models

Modelling is an established alternative to in vivo audiometry. We produced finite element models (FEM) of hearing in minke (Balaenoptera acutorostrata) and humpback (Megaptera novaeangliae) whales and frequency place maps (FPMs; total hearing ranges) for these species plus right (Eubalaeglacialis) and blue (Balaenoptera musculus) whales with control data for bottlenose dolphins (Tursiops truncatus) and harbor porpoises (Phocoena phocoena) Anatomically derived FPMs were obtained from microCT scans, basilar membrane histology, and nanoindentation stiffness measurements. FEM simulations of frequency response peak sensitivities used middle ear transfer function (METF) and laser Doppler measurements of stapes footplate velocities. Frequency responses were measured for stimuli at bone versus tympanic membrane/glove finger sites to assess most sensitive input locations. Odontocetes had a high pass response with corner frequencies from 10 kHz to 30 kHz. Response frequencies extended to 125 kHz, the limit of our measurement capability. Mysticetes had a bandpass response characterized by peak sensitivity from 0.5 kHz to 2 kHz for stimuli at the glove finger. Preliminary audiograms show peak responses differ among baleen whale species but were generally between 20 Hz and 5 kHz with functional hearing ranges of 50 Hz–35 kHz for tested species. [Work supported by the Joint Industry Programme on Sound and Marine Life.]

Spatially-explicit environmental assessment of bioethanol from miscanthus and switchgrass in France

Bioethanol is promoted as a means of tackling climate change, diversifying energy sources and securing energy supply. However, there also concerns that their wider deployment could lead to unintended environmental consequences. Life cycle assessment (LCA) is a widely used methodology to assess the environmental performance of biofuels. However, its outcomes strongly depend on the inventory data and modeling assumptions. Agronomic variables such as crop yields, nitrogen fertilizer rates or field emissions of nitrous oxide are very sensitive inputs, as are soil carbon dynamics in response to land use changes (LUC) entailed by the deployment of energy crops. Models simulating agroecosystem processes and the economics of agricultural farms are promising tools to predict such variables and improve the reliability of LCA.Here, we combined the agro-ecosystem model CERES-EGC, the farm economic model AROPAj and the LCA approach to investigate the effect of local drivers on the environmental impacts of bioethanol from miscanthus and switchgrass over France.Overall, lignocellulosic bioethanol achieved GHG abatement targets in the 74 %–94 % range compared to gasoline, and complied with the 50 % minimum imposed by European regulations. Miscanthus-based ethanol achieved up to twice lower environmental impacts than switchgrass due to 50 % higher biomass yields overall. Low fertilizer N input rates (in the 0-30 kg N ha-1 yr-1 range) proved the most efficient strategy to optimize energy return. Significant inter-regional variability occurred, especially in terms of soil C sequestration rates, which weighed in substantially on GHG budgets. Some regions were more efficient than others as a result, which advocates a site-specific approach and a potential prioritization when planning biorefineries, taking into account local production and environmental performance potentials. Compared to previous studies, ours provided high-resolution data in terms of crop yields, nitrous oxide emissions and soil C dynamics, factoring in LUC effects at local to regional scales.

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification

Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the “entrapment database” strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%–61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.

Machine learning-enabled calibration of river routing model parameters

Abstract Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.

An analytical study of coupled convective heat and mass transfer with volumetric heating describing sublimation of a porous body under most sensitive temperature inputs: Application of freeze-drying

Sublimation heat-mass transfer has great applications in the pharmaceutical and food industries, such as the preservation of biological products, accelerated freeze-drying (AFD), energy storage systems, microwave freeze-drying, and enabling long-time active covid like vaccines. The current technological demand encourages investigators to provide new knowledge for freeze-drying so that it reduces the high economic cost, prevents materials from being denatured, and remains stable for a long time. In connection with this, it is of key interest to analyze the impact of convective heat and mass transfer, the rate of water vaporization, and the volumetric heating source under the most realistic temperature inputs. Despite the available works on sublimation, there is still a lack of mathematical modeling that accounts for these informations together and is presently being considered. This paper presents a heat and mass transfer problem describing sublimation in a half-porous space. The mathematical model accounts for convective heat/mass transfer and a volumetric heat source within dried and frozen regions. In addition, convection driven by the mass transfer of ice crystals within the dried region is also considered. Three different types of temperature input are placed at the surface x=0 to obtain the rapid sublimation process without harming the material properties. The exact solution to the problem is obtained successfully by using similarity transformation. The impact of various problem parameters on sublimation is comprehensively studied. In this study, it is found that with a volumetric heating source term G0, material sublimates faster than without one. Furthermore, convective heat transfer in terms of Pe1 and Pe2, enhances the temperature within the porous medium, and as a result, material sublimates faster than usual. As the value of the convective term β goes up, a reduction in the temperature field is observed. The temperature of the medium rises as the value of the Kirpichev-like number Ki increases. Similar observation is found in the case of Biot like number Bi. The concentration profile decreases as the value of the Luikov number Lu increases. It is also found that Newton-type temperature input offers a faster sublimation rate in comparison to constant and flux-type temperature input. The analytical results obtained in this study show excellent agreement with previous available results. These results provide a comprehensive theoretical and mathematical understanding of sublimation heat/mass transfer and are expected to be useful in energy storage systems, food technology, and accelerated freeze drying.

Single-stranded linear amplification library preparation method for highly sensitive and low input amount ctDNA detection.

e15096 Background: Detection of circulating tumor DNA (ctDNA) or minimum residual disease (MRD) through somatic variant identifications in plasma samples has emerged as a promising approach for monitoring the clinical outcomes after surgical resections or other therapies. However, due to very low ctDNA levels in the early-stage cancers and tumor remissions after therapies, or low plasma volumes and DNA degradation caused low qualities in clinical practice, to sensitively identify somatic mutations with low allele frequencies in those samples remains a challenging technology task. Methods: In this study, we developed a single-stranded, linear amplification based library preparation sequencing (SLA-Seq) method. In the experimental workflow, double stranded molecular templates will be converted into single strands to construct libraries, thus minimize the loss of irregular double stranded DNA molecules. Adapter ligation was carried out in two steps. During the first step, P7 adapters with unique molecular identifiers(UMI) were ligated to the 3’ end of the molecular templates, and the DNA molecules were amplified by linear amplification to improve the DNA utilization efficiency. In the second step, P5 adapters were ligated to the linear amplified PCR products, then amplified by the pre-PCR. The pre-library was enriched by hybridization and magnetic-beads capturing and then subjected to high-throughput sequencing. 30ng of the cfDNA reference standard samples containing mutations of 0.3% and 0.1% allele frequencies were used to validate the performance. Results: In the SLA-Seq method, adapter sequences were carefully optimized to eliminate adapter dimers and thus improved the DNA ligation efficiency. Compared with the double-stranded library preparation method, SLA-seq showed overall higher effective depths after removing duplications if the same start amount of the DNA was used, which indicated that the higher DNA utilization rate was achieved. To evaluate the performance, we applied SLA-Seq on 11 reference standard samples with only 30ng start amount DNA. Among them, 6 samples were diluted to 0.3% allele frequency and 5 samples were diluted to 0.1% allele frequency. Totally they had 24 and 35 mutations, respectively. The sensitivity at the 0.3% allele frequency was 100% (24/24) and it was 94.29% (33/35) at the 0.1% mutation level, which also showed the higher sensitivity achieved by SLA-Seq than double-stranded library preparation method. Conclusions: We developed a single-stranded, linear amplification based library preparation method to improve difficult sample situations such as low input amount and low qualities, and achieved high single site sensitivity at the 0.1% allele frequency. Such features imply SLA-Seq has important clinical significance in the ctDNA/MRD detection.

Assessing the effects of unit cost uncertainty on flexible road pavement economics under the influence of climate change

Pavement researchers typically adopt life cycle cost analysis (LCCA) to quantify changes in the economic performance of road pavements due to the effects of climate change. As uncertainty exists in the unit cost of materials, fuels, and machinery operation, the assessment of climate change-induced pavement costs invariably involves uncertainty. If such uncertainties remain unaddressed, the assessment of pavement costs will not be accurate. Therefore, this study develops a stochastic pavement LCCA framework to account for the effects of such uncertainties on climate change-induced pavement life cycle cost. This is achieved by integrating a sensitivity analysis methodology and Monte Carlo simulation. To demonstrate the applicability of the framework case studies are performed for standard interstate and standard primary road pavement sections in four climate zones in the United States under a high climate change Representative Concentration Pathway (RCP8.5) for four different periods between 1981 and 2100. The results show that pavement maintenance, end-of-life (EOL), and transportation costs are most affected by climate change. To assess climate change-induced pavement costs more accurately, it is important to improve the accuracy of gasoline, diesel, and hot mix asphalt (HMA) unit costs, as they are the most sensitive input to the pavement LCCA model.