Independent Input Research Articles

Recognition of Ground Clutter in Single-Polarization Radar Based on Gated Recurrent Unit

A new method is proposed for identifying ground clutter in single-polarization radar data based on the gated recurrent unit (GRU) neural network. This method needs five independent input variables related to radar reflectivity structure, which are the reflectivity at current tilt, the reflectivity at the upper tilt, the reflectivity at 3.5 km, the echo top height, and the texture of reflectivity at current tilt, respectively. The performance of the new method is compared with that of the traditional method used in the Weather Surveillance Radar 1988-Doppler system in four cases with different scenarios. The results show that the GRU method is more effective than the traditional method in capturing ground clutter, particularly in situations where ground clutter exists at two adjacent elevation angles. Furthermore, in order to assess the new method more comprehensively, 709 radar scans from Nanchang radar in July 2019 and 708 scans from Jingdezhen radar in June 2019 were collected and processed by the two methods, and the frequency map of radar reflectivity exceeding 20 dBZ was analyzed. The results indicate that the GRU method has a stronger ability than the traditional method to identify and remove ground clutter. Meanwhile, the GRU method can also preserve meteorological echoes well.

Is the Force-Velocity Profile for Free Jumping a Sound Basis for Individualized Jump Training Prescriptions?

Formulating individualized optimized jump training prescriptions based on the force-velocity profile has become popular, but its effectiveness has been contested. Such training programs have opposite effects on 'maximal average force' and 'maximal average shortening velocity', and we set out to investigate which training-induced changes in the neuromuscular system could cause such effects. We used a musculoskeletal simulation model with four body segments and six muscle-tendon actuators to simulate vertical squat jumps with different loads. Independent input was muscle stimulation over time, which was optimized for maximal jump height. We determined the force-velocity profile for a reference model and investigated how it changed when we modified muscle properties and initial postures. We could not reproduce the reported training effects by realistically improving muscle properties (maximal force, shortening velocity and rate of force development) or modifying initial postures of the model. However, the profile was very sensitive to gains in jump height at low-loads but not high loads, or vice versa. Reaching maximal height in force-velocity profile jumps requires skill. We argued that submaximal performance in low-load or high-load jumps caused by lack of skill could be responsible for large imbalances in profiles before training. Differential skill training promoted by the individualized optimized approach could explain quick changes toward a balanced profile. If the success of individualized optimized training studies is explained by selective skill improvements, training effects are unlikely to transfer to other tasks, and individualized optimized training will not be superior to other types of training.

Ecological health and security of the Jazmurian Wetland Endorheic Watershed, Iran.

Wetlands represent a crucial category of aquatic ecosystems that face numerous threats, such as increasing population density, alterations in land-use, climate change, excessive extraction of water resources, and inadequate construction of reservoirs. As a result of these challenges, wetlands cannot perform their essential functions, which include meeting human demands, supporting tourism, mitigating dust storms, and maintaining the biodiversity of flora and fauna. Accordingly, assessing their health and service performance is essential. However, studies on the health and security assessment of the wetland are scant. Therefore, an attempt has been made to evaluate the Jazmurian Wetland Watershed health using the cause-effect pressure (P), state (S), and response (R) conceptual approach. To this end, the problem-oriented variables were obtained through field surveying and data augmentation. Some 12 independent input variables were finalized using principal component analysis (PCA) to assess the watershed health and security at the sub-watershed scale. The study's results elucidated that P = 0.564, S = 0.368, and R = 0.643 were classified in moderate class with negative orientation, relatively desirable with negative inclination, and high with negative tendency, respectively. The overall weighted health and security indices for the Jazmurian Wetland Watershed were 0.504 and 0.446, classifying in moderate conditions with a slight positive tendency. Accordingly, an insight focused on controlling the most influential variables in the P and S indices and pertaining existing conditions on the R index-related factors in priority sub-watersheds is a vital task to take toward ecosystem sustainability. Considering the fragile health and security of the Jazmurian Wetland Watershed, management actions need to be incorporated to prevent further decline and perpetuation of situations in the region.

Robust Adaptive Control System of Variable Sampling Period for Cement Raw Mix Quality Control

The advanced quality control of the raw mix remains a priority for the cement industry, particularly in recent years, where large quantities of alternative fuels and raw materials are used in clinker production, aiming to reduce the CO2 footprint. This study presents an adaptive control system with a variable sampling period for regulating raw mix quality in the raw mill (RM) output in a process with four independent inputs and four outputs: the lime saturation factor (LSF), silica modulus (SM), alumina modulus (AM), and SO3. The three pillars of the system are (1) mill dynamics calculation using exclusively industrial data, (2) the design of the controllers to meet robustness criteria, and (3) performance enhancement through simulators. Our technique periodically adjusts the gains of the controllers based on the mill’s dynamic parameters, which are computed from raw mix laboratory analyses. The presented results correspond to more than 14,000 h of mill operation. The standard deviation of the LSF at the mill outlet ranged from 1.5 to 3, which is equivalent to 1 to 2 standard deviations of LSF reproducibility. The standard deviation of the other moduli was close to the corresponding reproducibility of each. The presented adaptive gain-scheduling controller for LSF can be applicable to a broad range of raw meal grinding systems.

Biosorption of cobalt and chromium from wastewater using manganese dioxide and iron oxide nanoparticles loaded on cellulose-based biochar: Modeling and optimization with machine learning (artificial neural network)

In this study, two nanomaterials with excellent adsorption capacities were developed to remove heavy metals efficiently from wastewater. Manganese dioxide MnO2 nanoparticles and iron oxide Fe2O3 nanoparticles were successfully synthesized using cassava peel carbon and characterized by different techniques. The experimental tests for the adsorption process were done in a batch system, and the influence of various parameters such as temperature (from 5 to 60 °C), initial concentration (from 10 to 60 mg/L), pH (2 to 8), and contact time (5 to 180 min) on the biosorption of cobalt (II) and chromium (VI) were fully investigated. Furthermore, the Qmax were 546.32 mg/g and 349.59 mg/g for chromium (VI) and cobalt (II) respectively. The results fitted Langmuir with the pseudo-second-order model, revealing that chemisorption controls heavy metals removal, while the thermodynamic sorption was an endothermic and spontaneous reaction. Artificial Neural Network (ANN) model was developed to predict as well as to simulate the experimental results, for this purpose, the proposed model was based on five independent inputs or variables and one output or response which is the predicted adsorbed amount, the proposed ANN model showed an appreciable prediction accuracy with high optimization ability for chromium (VI) and cobalt (II) removal.

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

Quantization‐Based Latin Hypercube Sampling for Dependent Inputs With an Application to Sensitivity Analysis of Environmental Models

ABSTRACTNumerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel‐based methods, such as the Hilbert‐Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between inputs and outputs. Implementing HSIC requires data from the original model, which leads to the need of efficient sampling strategies to limit the number of costly numerical simulations. While, for independent input variables, existing sampling methods like Latin Hypercube Sampling (LHS) are effective in estimating HSIC with reduced variance, incorporating dependence is challenging. This article introduces a novel LHS variant, quantization‐based LHS (QLHS), which leverages Voronoi vector quantization to address dependent inputs. The method provides good coverage of the range of variations in the input variables. The article outlines expectation estimators based on QLHS in various dependency settings, demonstrating their unbiasedness. The method is applied to several models of growing complexities, first on simple examples to illustrate the theory, then on more complex environmental hydrological models, when the dependence is known or not, and with more and more interactive processes and factors. The last application is on the digital twin of a French vineyard catchment (Beaujolais region) to design a vegetative filter strip and reduce water, sediment, and pesticide transfers from the fields to the river. QLHS is used to compute HSIC measures and independence tests, demonstrating its usefulness, especially in the context of complex models.

Modular Multi‐Parametric and Portable Readout System for Point‐of‐Care Applications

AbstractA portable, battery‐powered, multi‐purpose readout system for combined optical and thermal measurements is presented. The system is modular with four independent input channels for different measurement schemes, allowing simultaneous multi‐parameter measurements to detect analytes of interest. Combined optical and thermal readout for spectroscopy is implemented by installing two different analog front‐end modules in the four input channels equipped with a micro spectrometer and a high‐resolution temperature sensor, respectively. The readout system is utilized to measure fluorescence signals of a common dye functionalized on gold nanoparticle‐DNA conjugates as a function of the temperature. Gold nanoparticles are modified with Guanine‐rich DNA sequences to perform Förster Resonance Energy Transfer (FRET) measurements. Elongation of DNA sequences at higher temperatures resulted in stronger fluorescence signals, accurately recorded by the portable system. The precision electronic components are chosen for a precise, battery‐powered operation. The size of this versatile, compact, and portable instrument is only 15.6 × 10.6 cm2. An interactive graphical‐user‐interface developed for this system supports point‐of‐care usage. The measurements carried out with the portable system showed a very close match with a commercial set‐up, with deviations less than 1 nm in the optical spectra and less than 0.5 °C in temperature.

Prediction of dose distributions for non-small cell lung cancer patients using MHA-ResUNet.

The current level of automation in the production of radiotherapy plans for lung cancer patients is relatively low. With the development of artificial intelligence, it has become a reality to use neural networks to predict dose distributions and provide assistance for radiation therapy planning. However, due to the significant individual variability in the distribution of non-small cell lung cancer (NSCLC) planning target volume (PTV) and the complex spatial relationships between the PTV and organs at risk (OARs), there is still a lack of a high-precision dose prediction network tailored to the characteristics of NSCLC. To assist in the development of volumetric modulated arc therapy (VMAT) plans for non-small cell lung cancer patients, a deep neural network is proposed to predict high-precision dose distribution. This study has developed a network called MHA-ResUNet, which combines a large-kernel dilated convolution module and multi-head attention (MHA) modules. The network was trained based on 80 VMAT plans of NSCLC patients. CT images, PTV, and OARs were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks' performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics. The MAE between the predicted dose distribution and the manually planned dose distribution within the PTV is 1.43Gy, and the D95 error is less than 1Gy. Compared with the other three commonly used networks, the dose error of the MHA-ResUNet is the smallest in PTV and OARs. The proposed MHA-ResUNet network improves the receptive field and filters the shallow features to learn the relative spatial relation between the PTV and the OARs, enabling accurate prediction of dose distributions in NSCLC patients undergoing VMAT radiotherapy.

Geometric quantum states beyond the AdS/CFT correspondence

We characterize the quantum states dual to entanglement wedges in arbitrary spacetimes, in settings where the matter entropy can be neglected compared to the geometric entropy. In AdS/CFT, such states obey special entropy inequalities known as the holographic entropy cone. In particular, the mutual information of CFT subregions is monogamous (MMI). We extend this result to arbitrary spacetimes, using a recent proposal for the generalized entanglement wedge e(a) of a gravitating region a. Given independent input regions a, b, and c, we prove MMI: Area[e(a)]+Area[e(b)]+Area[e(c)]−Area[e(ab)]−Area[e(bc)]−Area[e(ca)]+Area[e(abc)]≤0. We expect that the full holographic entropy cone can be extended to arbitrary spacetimes using similar methods. Published by the American Physical Society 2024

Vertex-based graph neural network classification model considering structural topological features for structural optimization

Traditional surrogate models always face the challenge of low accuracy when dealing with high-dimensional problems in structural optimization, this study aims to overcome this problem and proposes a vertex-based graph neural network (GNN) classification model. In contrast to conventional machine learning models that treat design variables as independent inputs, the proposed model develops a vertex-based graph representation to transform structural topological features and critical physical information into the graph data. According to a message passing mechanism based on the graph convolutional, it can extract the correlations among design variables and enhance its capability in handling high-dimensional structural optimization problems. Three truss examples, including a 10-bar with 10 variables, a 600-bar with 25 variables, and a 942-bar with 59 variables, are utilized to investigate the performance of the proposed surrogate model. The results demonstrate that the GNN-based surrogate model outperforms traditional machine learning approaches, particularly in the two high-dimensional problems, showcasing its superior ability to capture complex variable correlations and handle high-dimensional structural optimization tasks. Moreover, the proposed method significantly reduces the computational expenses by over 60% compared to conventional metaheuristic algorithms, while yielding optimal designs with comparable quality. These results demonstrate the efficiency and effectiveness of the GNN-based surrogate model in tackling complex, high-dimensional structural optimization problems.

Robustness and variability in Caenorhabditis elegans dauer gene expression.

Both plasticity and robustness are pervasive features of developmental programs. The dauer in Caenorhabditis elegans is an arrested, hypometabolic alternative to the third larval stage of the nematode. Dauers undergo dramatic tissue remodeling and extensive physiological, metabolic, behavioral, and gene expression changes compared to conspecifics that continue development and can be induced by several adverse environments or genetic mutations that act as independent and parallel inputs into the larval developmental program. Therefore, dauer induction is an example of phenotypic plasticity. However, whether gene expression in dauer larvae induced to arrest development by different genetic or environmental triggers is invariant or varies depending on their route into dauer has not been examined. By using RNA-sequencing to characterize gene expression in different types of dauer larvae and computing the variance and concordance within Gene Ontologies (GO) and gene expression networks, we find that the expression patterns within most pathways are strongly correlated between dauer larvae, suggestive of transcriptional robustness. However, gene expression within specific defense pathways, pathways regulating some morphological traits, and several metabolic pathways differ between the dauer larvae. We speculate that the transcriptional robustness of core dauer pathways allows for the buffering of variation in the expression of genes involved in adaptation, allowing the dauers induced by different stimuli to survive in and exploit different niches.

Exploring Kriging metamodeling to alleviate speed reading dependence in Bridge Weigh-In-Motion predictions

Bridge Weigh-In-Motion (BWIM) systems can be installed and serviced on highway bridges to provide measurements of moving vehicle’s gross weight, axle load, axle separation, and so on, without interrupting traffic. This data can be used to provide information of the heavy traffic over the bridge to management. This article presents the application of Kriging metamodeling to BWIM through the simulation of traffic streams. The Kriging methodology, with its prediction of metamodel variance, allows for the strategic selection of vehicles to train the BWIM metamodel. The proposed approach extends the BWIM concept of influence area from response time histories to geometric input spaces from the strain response wave (RW). The simulated dynamic response of simply supported bridges in the US Interstate Highway System were performed using Finite Element Modeling (FEM) of moving loads, generating RWs captured in different sensor locations. Three independent input spaces based on geometric properties of the RWs are the input to train the Kriging metamodel of a defined training truck group. In this approach, the measurement of the vehicle speed is not needed as an improvement of traditional BWIM methods. The results show that the Kriging prediction can fulfill at least the Type II WIM Systems performance defined in the ASTM’s Standard, using a comparable number of training runs to the number of calibration runs defined in the standard.

Generative Artificial Intelligence Using Machine Learning on Wireless Ad Hoc Networks

In this article, we discuss the use of Generative Artificial Intelligence (GenAI) to improve the efficiency and performance of access to wireless points located in various spaces and specific places, which allows interaction with wireless mesh networks and enables the use of mobile devices to access all types of information in internal environments. Furthermore, we propose the use of generative neural networks, which are one of the pillars of GenAI, since they use a methodology from the perspective of Machine Learning that allows analysis of a large amount of data and detection of certain types of patterns that help in the better placement of access points for improved reception and connectivity. Images (heat maps), access point locations, positioning points, and bandwidth are analyzed, allowing new information to be created. On the other hand, to understand and model the general architecture of the wireless Ad-Hoc network, we use two processes that are part of neural networks, such as Multilayer Perceptron (MLP), and the Radial Basis Function (RBF), which is a function of predictors or independent variables or input variables that allows the prediction error in the output variables of the wireless network architecture to be reduced. Using these two processes does help reduce blind spots in those internal places where the wireless signal does not reach, resulting in a signal drop. Improving internal scenarios with wireless Ad-Hoc networks is what is required for better functioning and performance of the network infrastructure.

Optically Mediated Hydrogel-Based Ionic Diode.

Ionic diodes with environmentally modulated ion-rectifying characteristics have attracted much attention and show great promise in the construction of smart devices with environmental adaptability. One immediate challenge is to integrate stimuli responsiveness and ion rectification into one single ionic diode, which requires a close cooperation of chemical principles and device technologies. Herein, an ionic diode based on a photoresponsive hydrogel with optically mediated ion-rectifying performances is introduced. Relying on the photoresponsive concentration of proton in the hydrogel, the ionic current rectification can be prominently enhanced upon ultraviolet (UV) irradiation. A maximum ionic current rectification ratio of the optically mediated ionic diode about 4× 105 is achieved. Furthermore, the hydrogel-based diode can serve as an AND logic gate operated by UV light and voltage bias as two independent inputs. As a proof of concept, to use the optically mediated diode is achieved to modulate the feedback of a robot with logic behaviors. This work provides a novel and valuable strategy for designing functional hydrogel-based devices with the integration of stimuli-responsiveness and logic signal processing through chemical approaches.

A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements

The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O₃ concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 μg/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distribution through the established CNN model.

Predicting air quality index and fine particulate matter levels in Bagdad city using advanced machine learning and deep learning techniques

Particulate matter pollution is recognized globally as one of the most hazardous forms of air pollution, profoundly impacting environmental integrity and public health. Key metrics for assessing this pollution include the Air Quality Index (AQI) and fine particulate matter with diameters ≤2.5 μm (PM2.5). These indicators are closely associated with severe health consequences, such as premature death from chronic exposure. While traditional statistical methods have been employed in some studies to evaluate AQI and PM2.5, the application of advanced machine learning techniques has been limited. This research employs deep learning and artificial neural networks (ANN) to forecast AQI and PM2.5 levels in Baghdad, Iraq. The study utilizes an extensive dataset from July 1, 2016, to December 12, 2021, comprising over 48,000 data points for AQI and PM2.5. Time serves as an independent input variable influencing these dependent variables. The analysis employs a diverse set of machine learning algorithms, including random forest, decision tree, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory networks (LSTM). The findings demonstrate that MLP and LSTM models outperform other methods, providing the most accurate predictions. The correlation coefficients were 0.977 and 0.983 for the prediction of AQI and 0.973 and 0.985 for the prediction of PM2.5 using MLP and LSTM, respectively. In addition, the outcomes showed that both AQI and PM2.5 were within the moderate to unhealthy ranges, and their distribution levels pointed to the need for addressing air quality in Baghdad city. Furthermore, this study contributes to the burgeoning field of machine learning applications in environmental science by establishing a robust and nuanced predictive framework for evaluating air quality. It highlights the potential of deep learning in public health applications and offers actionable insights for policymaking to mitigate air pollution and its adverse effects.

Non-Hebbian plasticity transforms transient experiences into lasting memories.

The dominant models of learning and memory, such as Hebbian plasticity, propose that experiences are transformed into memories through input-specific synaptic plasticity at the time of learning. However, synaptic plasticity is neither strictly input-specific nor restricted to the time of its induction. The impact of such forms of non-Hebbian plasticity on memory has been difficult to test, and hence poorly understood. Here, we demonstrate that synaptic manipulations can deviate from the Hebbian model of learning, yet produce a lasting memory. First, we established a weak associative conditioning protocol in mice, where optogenetic stimulation of sensory thalamic input to the amygdala was paired with a footshock, but no detectable memory was formed. However, when the same input was potentiated minutes before or after, or even 24 hr later, the associative experience was converted into a lasting memory. Importantly, potentiating an independent input to the amygdala minutes but not 24 hr after the pairing produced a lasting memory. Thus, our findings suggest that the process of transformation of a transient experience into a memory is neither restricted to the time of the experience nor to the synapses triggered by it; instead, it can be influenced by past and future events.

FEATURES OF FORMING WATER AVAILABILITY FOR WINTER WHEAT IN THE SOUTH OF UKRAINE

The article examines the importance of considering several factors affecting the soil water regime in the context of developing models for forecasting productive soil moisture reserves. It was established that the main factors, such as the type of soil, its density, and mechanical composition, remain constant in different soil and climatic regions of Ukraine. The second group of factors, such as air temperature, precipitation, and soil moisture, are subject to changes throughout the growing season and even over short periods. Therefore, the dynamics of soil moisture as a function of the main variables, such as average air temperature and precipitation, was considered using calculations. Initial moisture reserves are used in the calculations for more accurate forecasting of moisture reserves at the end of a specified period. Hydrothermal conditions considered as predictors in the regression equation are also used. The reliability of the conducted research is confirmed by the analysis of independent input and output information from the Kherson weather station in the period from 2018 to 2021 regarding the actual reserves of productive moisture in the soil at a depth of 0 - 20,0 - 50 and 0 - 100 cm. The average error between actual and calculated data did not exceed +13,5%. It confirms the reliability and precision of the conducted research making it the basis for further analyses and conclusions. The conclusions noted the need for accurately determining soil moisture to effectively manage agrometeorological conditions and optimize crop yield. The authors believe that the research work presented in this article can significantly contribute to developing modern approaches to water availability in agriculture and agrometeorology. That will contribute to the gradual and improved development of soil moisture forecasting methodology, which is key to ensuring sustainable and productive development in agriculture.

Canadian Expert Consensus on the Use of Halobetasol Propionate/Tazarotene Lotion for Plaque Psoriasis.

An expert panel of Canadian dermatologists was assembled to develop consensus statements regarding the current landscape of topical therapies for plaque psoriasis and the place in therapy of the recently approved fixed-dose combination halobetasol propionate (HP)/tazarotene (TAZ) lotion (HP/TAZ) in the treatment algorithm for plaque psoriasis. A modified nominal group technique, which combined both independent and group input from the expert panel, was used to develop the consensus statements. The expert panel completed surveys to elicit their independent views on the current landscape of topical therapies for plaque psoriasis in Canada. The first expert panel session was held to discuss the existing body of literature and develop draft consensus statements about topical therapies and the place in therapy of HP/TAZ. Independent feedback on the draft consensus statements was solicited from expert panel members prior to another expert panel session where the amended consensus statements were further discussed, edited and, finally, voted on. The expert panel reached consensus on 20 statements. Expert panel members agreed, based on the existing body of literature, that there is a place in therapy for HP/TAZ to address several current unmet treatment needs of patients with plaque psoriasis. Studies have shown that HP/TAZ is an effective and safe first-line treatment for moderate-to-severe plaque psoriasis. Due to its cosmetically pleasing vehicle and once-daily administration, HP/TAZ may improve patient acceptance and treatment adherence.