Specific Modeling Research Articles

Diagnostic accuracy of immunohistochemistry compared with molecular tests for detection of BRAF V600E mutation in ameloblastomas: Systematic review and meta-analysis.

The objective of this systematic review with meta-analysis was to critically evaluate the available data on sensitivity and specificity of IHC compared with molecular tests in the detection of BRAF V600E mutation in ameloblastomas. This systematic review was performed based on the PRISMA statement and registered in Prospero (CRD42021259117). PubMed, Web of Science, Scopus, and Cochrane Library databases were searched for observational studies to answer the question "What is the diagnostic accuracy of immunohistochemistry compared with molecular tests for the diagnosis of BRAF V600E mutation in ameloblastomas?". Methodological quality and risk of bias assessment of the selected studies were based on the QUADAS-2. Meta-analysis based on hierarchical SROC curve model and summary measures for sensitivity and specificity were computed. A total of 226 records were found, but only 05 articles met the inclusion criteria, with 277 FFPE specimens of ameloblastoma included in the quantitative analysis. The sensitivity of the IHC compared to molecular tests ranged from 0.71 to 1.00, while all of the included studies showed perfect specificity (1.00). Pooled measures for sensitivity and specificity were 0.95 [95% CI 0.89, 1.00] and 1.00 [95% CI 0.95, 1.00], respectively. The diagnostic odds ratio was 4.05, and the AUC for SROC curve was calculated as 0.979. BRAF V600E-specific IHC using VE1 antibody showed extremely high sensitivity and specificity when compared with molecular tests in the detection of the mutation in ameloblastomas.

Evolutionary landscapes derived from amino acid sequence space serve as predictive models of functional phenotypes in protein design

Exploring the extant sequence space of biomolecules has been useful to study molecular evolution and common statistical features between members of related families of proteins and RNA. When these statistical signatures are incorporated in the parameters of a global inference model of sequences, then multiple applications arise. These applications include the prediction of amino acid contacts in 3D space, the accurate inference of structural complexes as well as models for specificity in molecular recognition for signaling proteins and protein-RNA interactions.

Biofire FilmArray Meningitis/Encephalitis panel for the aetiological diagnosis of central nervous system infections: A systematic review and diagnostic test accuracy meta-analysis.

SummaryBackgroundThe FilmArray Meningitis/Encephalitis(FA/ME) panel brings benefits in clinical practice, but its diagnostic test accuracy (DTA) remains unclear. We aimed to determine the DTA of FA/ME for the aetiological diagnostic in patients with suspected central nervous system(CNS) infection.MethodsWe performed a systematic review with DTA meta-analysis (PROSPERO: CRD42020139285). We searched Embase, Medline (Ovid), and Web of Science from inception until September 1st, 2021. We assessed the study-level risk of bias with the QUADAS-2 tool and applied the GRADE approach to assess the certainty of the synthesised evidence. We included studies that simultaneously measured the reference test (CSF/blood culture for bacteria, and specific polymerase chain reaction for viruses) and the FA/ME in patients with suspected CNS infection. We performed random-effects bivariate meta-analysis models of combined sensitivity and specificity using CSF/blood cultures(reference test 1) and a final diagnosis adjudication based on clinical/laboratory criteria (reference test 2).FindingsWe included 19 studies (11,351 participants). For all bacteria with reference test 1 (16 studies/6183 patients) sensitivity was estimated at 89·5% (95%CI 81·1–94·4), and specificity at 97·4% (95%CI 94–98·9). With reference test 2 (15 studies/5,524 patients), sensitivity was estimated at 92·1%(95%CI 86·8–95·3) and specificity at 99.2(95%CI 98·3–99·6) For herpes simplex virus-2(HSV-2), enteroviruses, and Varicella-Zoster virus (VZV), we obtained sensitivities between 75·5 and 93·8%, and specificities above 99% (reference test 1). Certainty of the evidence was low.InterpretationFA/ME may have acceptable-to-high sensitivities and high specificities for identifying bacteria, especially for S.pneumoniae, and viruses, especially for HSV-2, and enteroviruses. Sensitivities for L.monocytogenes, H.influenzae, E.coli, and HSV-1 were suboptimal.FundingNone.

Two component system specificity model detects noncognate interactions for antibiotic resistance pathways and other two component pathways

Two component systems (TCS) are ubiquitous in bacteria, both intrinsically and extrinsically when encoded by plasmids, and are responsible for antibiotic resistance, nutrient signaling, quorum signaling, and other environmental responses. TCS are signal transduction pathways in which a histidine kinase (HK) senses an environmental signal and transduces the signal by phosphorylating a response regulator (RR). Once the RR is phosphorylated it can carry out several functions, but it often acts as a transcription factor.

Piston Engine Modelling for Hydrogen Fueled Composite Cycle Engines

In order to achieve aviation’s ambitious emission reduction targets specified by ACARE [1] radical changes in propulsion system concepts will be necessary. To fulfill the goal of carbon dioxide emission neutrality in 2050 and beyond, plenty propulsion system concepts are currently under investigations, mainly focusing on batteries and hydrogen as energy source. Especially for long range aircraft applications, hydrogen might by favorable due to its outstanding specific gravimetric energy density. Therefore, the Composite Cycle Engine (CCE) concept should be evaluated for hydrogen combustion. In a first step, the applied time resolved 0D piston engine performance simulation model of the CCE is adapted for hydrogen combustion. For example, the heat transfer and combustion characteristics of kerosene and hydrogen combustion differ significantly and require specific modeling approaches. The current publication illustrates the piston engine performance simulation model and the modifications needed to account for hydrogen combustion. Furthermore, results of validation case calculations as well as initial sensitivity studies of the hydrogen fueled piston engines model are presented and discussed in the CCE context. For example, sizing effects and the influence of valve timing on piston engine performance will be evaluated.

Impact of Cloud-Base Turbulence on CCN Activation: Single-Size CCN

Abstract This paper examines the impact of cloud-base turbulence on activation of cloud condensation nuclei (CCN). Following our previous studies, we contrast activation within a nonturbulent adiabatic parcel and an adiabatic parcel filled with turbulence. The latter is simulated by applying a forced implicit large-eddy simulation within a triply periodic computational domain of 643 m3. We consider two monodisperse CCN. Small CCN have a dry radius of 0.01 μm and a corresponding activation (critical) radius and critical supersaturation of 0.6 μm and 1.3%, respectively. Large CCN have a dry radius of 0.2 μm and feature activation radius of 5.4 μm and critical supersaturation 0.15%. CCN are assumed in 200-cm−3 concentration in all cases. Mean cloud-base updraft velocities of 0.33, 1, and 3 m s−1 are considered. In the nonturbulent parcel, all CCN are activated and lead to a monodisperse droplet size distribution above the cloud base, with practically the same droplet size in all simulations. In contrast, turbulence can lead to activation of only a fraction of all CCN with a nonzero spectral width above the cloud base, of the order of 1 μm, especially in the case of small CCN and weak mean cloud-base ascent. We compare our results to studies of the turbulent single-size CCN activation in the Pi chamber. Sensitivity simulations that apply a smaller turbulence intensity, smaller computational domain, and modified initial conditions document the impact of specific modeling assumptions. The simulations call for a more realistic high-resolution modeling of turbulent cloud-base activation.

Aleatory uncertainties with global resistance safety factors for non-linear analyses of slender reinforced concrete columns

The present study aims to investigate and evaluate the influence of aleatory uncertainties (i.e., material and geometric uncertainties) when non-linear numerical analyses are developed to assess the global resistance of slender reinforced concrete columns. For instance, a set of 40 experimental tests on slender reinforced concrete columns with different slenderness values known from the literature is considered. The experimental tests have been reproduced by means of non-linear numerical simulations adopting specific modelling hypotheses able to minimize the model uncertainty. Successively, the calibrated non-linear numerical models of the 40 columns are used to perform a probabilistic analysis of the global structural resistance (i.e., ultimate axial load) including material uncertainties only and, then, both geometric and material uncertainties. The two groups of the probabilistic investigation are useful to characterize the influence of both material and geometric uncertainties on the global structural response of slender RC columns. Finally, global resistance safety factors are proposed as a function of the slenderness discussing also the procedure to incorporate the proposed results within the use of the global resistance safety formats.

Decision-making under uncertainty: be aware of your priorities

Self-adaptive systems (SASs) are increasingly leveraging autonomy in their decision-making to manage uncertainty in their operating environments. A key problem with SASs is ensuring their requirements remain satisfied as they adapt. The trade-off analysis of the non-functional requirements (NFRs) is key to establish balance among them. Further, when performing the trade-offs it is necessary to know the importance of each NFR to be able to resolve conflicts among them. Such trade-off analyses are often built upon optimisation methods, including decision analysis and utility theory. A problem with these techniques is that they use a single-scalar utility value to represent the overall combined priority for all the NFRs. However, this combined scalar priority value may hide information about the impacts of the environmental contexts on the individual NFRs’ priorities, which may change over time. Hence, there is a need for support for runtime, autonomous reasoning about the separate priority values for each NFR, while using the knowledge acquired based on evidence collected. In this paper, we propose Pri-AwaRE, a self-adaptive architecture that makes use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to perform decision-making for SASs while offering awareness of NFRs’ priorities. MR-POMDP is used as a priority-aware runtime specification model to support runtime reasoning and autonomous tuning of the distinct priority values of NFRs using a vector-valued reward function. We also evaluate the usefulness of our Pri-AwaRE approach by applying it to two substantial example applications from the networking and IoT domains.

Electrochemical Approaches for CO2 Conversion to Chemicals: A Journey toward Practical Applications.

Carbon capture, utilization, and sequestration play an essential role to address CO2 emissions. Among all carbon utilization technologies, CO2 electroreduction has gained immense interest due to its potential for directly converting CO2 to a variety of valuable commodity chemicals using clean, renewable electricity as the sole energy source. The research community has witnessed rapid advances in CO2 electrolysis technology in recent years, including highly selective catalysts, larger-scale reactors, specific process modeling, as well as a mechanistic understanding of the CO2 reduction reaction. The rapid advances in the field brings promise to the commercial application of the technology and the rapid rollout of the CO2 electroreduction for chemical manufacturing.This Account focuses on our contributions in both fundamental and applied research in the field of electrocatalytic CO2 and CO reduction reactions. We first discuss (1) the development of novel electrocatalysts for CO2/CO electroreduction to enhance the product selectivity and lower the energy consumption. Specifically, we synthesized nanoporous Ag and homogeneously mixed Cu-based bimetallic catalysts for the enhanced production of CO from CO2 and multicarbon products from CO, respectively. Then, we review our efforts in (2) the field of reactor engineering, including a dissolved CO2 H-type cell, vapor-fed CO2 three-compartment flow cell, and vapor-fed CO2 membrane electrode assembly, for enhancing reaction rates and scalability. Next, we describe (3) the investigation of reaction mechanisms using in situ and operando techniques, such as surface-enhanced vibrational spectroscopies and electrochemical mass spectroscopy. We revealed the participation of bicarbonate in CO2 electroreduction on Au using attenuated total-reflectance surface-enhanced infrared absorption spectroscopy, the presence of an "oxygenated" surface of Cu under CO electroreduction conditions using surface-enhanced Raman spectroscopy, and the origin of oxygen in acetaldehyde and other CO electroreduction products on Cu using flow electrolyzer mass spectrometry. Lastly, we examine (4) the commercial potential of the CO2 electrolysis technology, such as understanding pollutant effects in CO2 electroreduction and developing techno-economic analysis. Specifically, we discuss the effects of SO2 and NOx in CO2 electroreduction using Cu, Ag, and Sn catalysts. We also identify technical barriers that need to be overcome and offer our perspective on accelerating the commercial deployment of the CO2 electrolysis technology.

Patient Specific Numerical Modeling for Renal Blood Monitoring Using Electrical Bio-Impedance.

Knowledge of renal blood circulation is considered as an important physiological value, particularly for fast detection of acute allograft rejection as well as the management of critically ill patients with acute renal failure. The electrical impedance signal obtained from kidney with an appropriate electrode system and optimal electrode system position regarding to the kidney projection on skin surface reflects the nature of renal blood circulation and tone of renal blood vessels. This paper proposes a specific numerical modelling based on prior information from MRI-data. The numerical modelling was conducted for electrical impedance change estimation due to renal blood distribution. The proposed model takes into the account the geometrical and electrophysiological parameters of tissues around the kidney as well as the actual blood distribution within the kidney. The numerical modelling had shown that it is possible to register the electrical impedance signal caused by renal blood circulation with an electrode system commensurate with the size of kidney, which makes it possible to reduce the influence of surrounding tissues and organs. Experimental studies were obtained to prove the numerical modelling and the effectiveness of developed electrode systems based on the obtained simulation results. The obtained electrical impedance signal with the appropriate electrode system shows very good agreement with the renal blood change estimated using Doppler ultrasound. For the measured electrical impedance signal, it is possible to obtain the amplitude-time parameters, which reflect the hemodynamic characteristics of the kidneys and used in diagnostics, which is the subject of further research.

Public Passenger Transport Logistics in the Context of Digital Transformation of Transportation Services Organisation Systems

Transport route specification models are used to analyse the need for combined passenger transportation on popular routes in a large urban agglomeration. The problem of managing the travel chains of passengers using public transport (PT) is revealed with the focus on the complexity of applying the principle of multimodality on the route network used by population due to the mismatch of the schemes of transport and users’ routes.The study of the logistics of passenger transportation with PT introduces the concept of «public transport user (PTU)» which has a variable status relative to the flows of people, pedestrians, passengers, and transport vehicles. The description of the registers of the main parameters of the routes under study serves to create their digital twins.To manage the travel chains of PTUs, identify related sections of transport routes, it is proposed to highlight within the passenger flow the currents of the same profile which include PTUs that have common transport behaviour.Models and algorithms of network proximity to transport infrastructure objects, visualisation of digital traces of PTUs and the results of comparing the used and the best route options according to the modelled parameters allow to identify behavioural profiles of PTUs, as well as regulators managing the travel chains.

Performance Prediction and Geometry Optimization for Application of Pump as Turbine: A Review

Pump as Turbine (PAT) is a technically and economically effective technology to utilize small/mini/micro/pico hydropower, especially in rural areas. There are two main subjects that influence the selection and application of PAT. On the one hand, manufacturers of pumps will not provide their characteristics under the turbine mode, which requires performance prediction methods. On the other hand, PAT efficiency is always slightly lower than that of pump, which requires further geometry optimization. This literature review summarized published research studies related to performance prediction and geometry optimization, aimed at guiding for selection and optimization of PAT. Currently, there exist four categories of performance prediction methods, namely, using BEP (Best Efficiency Point), using specific speed, loss modeling, and polynomial fitting. The using BEP and loss modeling methods are based on theoretical analysis, while using specific speed and polynomial fitting methods require statistical fitting. The prediction errors of published methods are within ±10% mostly. For geometry optimization, investigations mainly focus on impeller diameter and blade geometry. The influence of impeller trimming, blade rounding, blade wrap angle, blade profile, blade number, blade trailing edge position, and guide vane number has been studied. Among published methods, the blade rounding and forward-curved impellers are the most effective and feasible techniques.

Retrospective validation of the postnatal Growth and Retinopathy of Prematurity (G-ROP) criteria in a Swiss cohort

BackgroundCurrently used screening criteria for retinopathy of prematurity (ROP) show high sensitivity for predicting treatment-requiring ROP but low specificity; over 90% of examined infants do not develop ROP that requires treatment (type 1 ROP). A novel weight gain-based prediction model was developed by the G-ROP study group to increase the specificity of the screening criteria and keep the number of ophthalmic examinations as low as possible. This retrospective cohort study aimed to externally validate the G-ROP screening criteria in a Swiss cohort.MethodsData from 645 preterm infants in ROP screening at Inselspital Bern between January 2015 and December 2019 were retrospectively retrieved from the screening log and analysed. The G-ROP screening criteria, consisting of 6 trigger parameters, were applied in infants with complete data. To determine the performance of the G-ROP prediction model for treatment-requiring ROP, sensitivity and specificity were calculated.ResultsComplete data were available for 322 infants who were included in the analysis. None of the excluded infants had developed type 1 ROP. By applying the 6 criteria in the G-ROP model, 214 infants were flagged to undergo screening: among these, 14 developed type 1 ROP, 9 developed type 2 ROP, and 43 developed milder stages of ROP. The sensitivity for predicting treatment-requiring ROP was 100% (CI, 0.79–1.00), and the specificity was 41% (CI, 0.35 –0.47). Implementing the novel G-ROP screening criteria would reduce the number of infants entering ROP screening by approximately one third.ConclusionsThe overall prevalence of treatment-requiring ROP was low (2.15%). Previously published performance parameters for the G-ROP algorithm were reproducible in this Swiss cohort. Importantly, all treatment-requiring infants were correctly identified. By using these novel criteria, the burden of screening examinations could be significantly reduced.

Higher oil prices, are they good or bad for renewable energy consumption: The case of Iran?

The transition to renewable energy is key for sustainable development. Therefore, one of the sustainable development goals (SDG) of United Nations (UN) is to shift towards clean energy. However, in many resource-rich countries several factors can stand as barriers towards the transition process. In this regard, this paper examines the influence of higher oil price, CO2 emissions, and income on renewable energy consumption in Iran from 1980 to 2019, employing General to Specific (Gets) modeling approach. Estimation results indicated that oil price and CO2 emissions both have statistically significant and negative impact on renewable energy consumption. The negative influence of higher oil prices on renewable energy consumption may be interpreted as sign of satisfaction coming from higher oil prices, which postpones the transition from traditional energy sources to renewable for Iran. Furthermore, we concluded that income does not have a statistically significant influence on renewable energy use. The paper discusses alternative policies to promote renewable energy consumption.

Modeling aquaponics: a review on available models and simulation tools

Introduction. Aquaponics is quickly expanding. Specific models and modeling tools have been developed within different studies. However, no study has yet attempted nor succeeded in bringing the aquaponic community around a common modeling project to centralize knowledge and develop an effective tool for users and professional growers. Literature. This paper reviews the relevant literature to provide an overview of the available simulation models and associated modeling tools. Furthermore, it identifies the current needs to lead further modeling developments. Conclusions. Several powerful models and modeling tools have been developed but are highly specific to their research scope and are often inaccessible. The modeling knowledge specific to aquaponics is at an advanced stage but is scattered among many different works. Therefore, it is evident that a shared and accessible modeling tool, which is currently missing, would greatly accelerate the development of aquaponics.

University: national features, models of transformation and regional specificity

University: national features, models of transformation and regional specificity

Lessons from a transcription factor: Alx1 provides insights into gene regulatory networks, cellular reprogramming, and cell type evolution.

The skeleton-forming cells of sea urchins and other echinoderms have been studied by developmental biologists as models of cell specification and morphogenesis for many decades. The gene regulatory network (GRN) deployed in the embryonic skeletogenic cells of euechinoid sea urchins is one of the best understood in any developing animal. Recent comparative studies have leveraged the information contained in this GRN, bringing renewed attention to the diverse patterns of skeletogenesis within the phylum and the evolutionary basis for this diversity. The homeodomain-containing transcription factor, Alx1, was originally shown to be a core component of the skeletogenic GRN of the sea urchin embryo. Alx1 has since been found to be key regulator of skeletal cell identity throughout the phylum. As such, Alx1 is currently serving as a lens through which multiple developmental processes are being investigated. These include not only GRN organization and evolution, but also cell reprogramming, cell type evolution, and the gene regulatory control of morphogenesis. This review summarizes our current state of knowledge concerning Alx1 and highlights the insights it is yielding into these important developmental and evolutionary processes.

Implied price processes anchored in statistical realizations

<p style='text-indent:20px;'>It is observed that statistical and risk neutral densities of compound Poisson processes are unconstrained relative to each other. Continuous processes are too constrained and generally not consistent with market data. Pure jump limit laws deliver operational models simultaneously consistent with both data sets with the additional imposition of no measure change on the arbitrarily small moves. The measure change density must have a finite Hellinger distance from unity linking the two worlds. Models are constructed using the bilateral gamma and the CGMY models for the risk neutral specification. They are linked to the physical process by measure change models. The resulting models simultaneously calibrate statistical tail probabilities and option prices. The resulting models have up to eight or ten parameters permitting the study of risk reward relations at a finer level. Rewards measured by power variations of the up and down moves are observed to value negatively(positively) the even(odd) variations of their own side with the converse holding for the opposite side.</p>

Vehicle Load Spectrum and Fatigue Vehicle Model of a Long‐Span Concrete‐Filled Steel Tube (CFST) Arch Bridge Based on Measured Data of Weight‐In‐Motion System

Highway traffic load, speed, and volume have been increasing continuously over the years. Because of its special structural form, the fatigue problem of a long‐span concrete‐filled steel tube arch bridge becomes more and more serious. To research the vehicle load spectrum and fatigue vehicle model of a long‐span concrete‐filled steel tubular arch bridge, the traffic data of the arch bridge were collected using the weight‐in‐motion system. The vehicle type and vehicle load in the actual traffic flow have strong stochastic characteristics, which cannot be directly applied. Therefore, according to the measured data, 10 representative models are proposed to facilitate the classification and screening of vehicle data. The wheelbase, mass, axle load, and overload data of the representative vehicle types were analysed, and the axle load distribution characteristics of vehicles in different lanes were studied. It is found that the vehicle load is not uniformly distributed in different lanes but concentrated in one lane. Moreover, a vehicle load spectrum for the fatigue assessment of the long‐span concrete‐filled steel tubular arch bridge is proposed. Based on the fatigue damage equivalence principle, a fatigue vehicle model and a simplified fatigue vehicle model of bridge heavy‐duty vehicles are proposed. Compared with the model in the AASHTO specification, it is found that the weight of the local fatigue vehicle load model is 15.1 t heavier than the vehicle model given in the specification. This study could be further referenced in bridge‐fatigue life prediction, management and maintenance, etc.

Towards a Mathematical Definition of Skin Model for Geometrical Product Specification and Verification and its Physical Interpretation

The concept of skin model is a foundation stone for ISO standards in Geometrical Product Specifications and Verification. At the conceptual level, it provides an abstraction of the physical part denoting the boundary which separates the part's physical shape from its environment. It conveys the designer's perception of potential defects and deviations from a nominal geometry, which can only be hypothesized. Considered as a surface on the boundary of a solid model of non-ideal geometry, which can only be imagined, the ‘skin’ can take an unrestricted variety of forms and representations at different scales. Despite its conceptual relevance, only a few research efforts have been devoted to a formal characterization of the skin model based on a sound mathematical definition supporting an appropriate and relevant physical interpretation. The work presented in this paper proposes new scientific foundations of the skin model based on the structure of solids as a compact packing of spheres characterized by Van der Waals radii. This physics-based description is extended and supported by mathematical models based on regular sets and morphological operations, which provide a set of conditions and properties that enable a physical interpretation in the context of mechanical parts and their measurements.