Efficient Experimental Design Research Articles

Development of an Untargeted Method for the Analysis of the Volatile Profile of Onions via Solid-Phase Microextraction Arrow-Headspace-Gas Chromatography-Mass Spectrometry Using Design of Experiments.

The distinctive aroma of onions, consisting primarily of sulfur-containing compounds, is one of the reasons for the popularity of the vegetable. The rapid enzymatic and chemical reactions that occur after the destruction of onion bulb tissue render the analysis of the volatile profile challenging. Therefore, sample preparation is a crucial step in the analysis of the onion volatilome, but it often does not receive the necessary attention in the literature. In this work, we focused on two aspects: Firstly, we compared different sample preparation approaches to maximize the volatile yield and to enable the description of the onion volatile profile as it would emerge after a solid-phase microextraction (SPME) Arrow sampling procedure. For headspace (HS)-gas chromatography-mass spectrometry analysis, onion juice with the addition of an ammonium sulfate solution proved to be the best option. Secondly, we optimized the HS sampling and desorption parameters (enrichment time, enrichment temperature, agitator speed, desorption time) for onion volatiles using the efficient design of experiments (DoE) approach. The optimal conditions for the analysis with HS-SPME Arrow were an enrichment time of 75min at 60°C with an agitator speed of 713rpm and a desorption time of 120s.

Optimizing germination conditions of Ghaf seed using ZnO nanoparticle priming through Taguchi method analysis

Ghaf, a resilient tree in arid environments, plays a critical role in ecological restoration, desertification mitigation, and cultural heritage preservation. However, the seeds’ inherent challenges, notably their hard outer coating restricting germination, emphasize the pressing need for innovative strategies. This work aimed to investigate the optimization of Ghaf seed germination process through seed priming with ZnO nanoparticles treatment (duration (t), concentration (c), temperature (T), and agitation (a), employing the Taguchi method for efficient experimental design. Furthermore, the study includes Analysis of Variance (ANOVA), analysis for the regression model to assess the significance of predictor variables and their interactions, thereby strengthening the statistical validity of our optimization approach. Notably, it revealed that concentration is a pivotal influencer in optimization of Ghaf seed germination. The results showed that the concentration of ZnO nanoparticles has no linear relation with any other parameters. To verify the outcomes, validation tests were performed utilizing the predicted optimal parameters. The observed low error ratio, falling within the range of 1 to 6%, confirmed the success of the Taguchi methodology in identifying optimal levels of the factors chosen. Significantly, ZnO-primed seeds showcased a remarkable enhancement in Ghaf seed germination, increasing from 15 to 88%. This study introduces a novel approach utilizing ZnO nanoparticle treatment optimized through the Taguchi method, significantly enhancing seed germination rates of Ghaf seeds and offering a promising avenue for sustainable agricultural practices in arid environments.

Optimization of an efficient and environmentally friendly drying program for cotton textiles in clothes dryers

Motivated by the imperative need to mitigate the environmental burden imposed by substantial energy consumption in clothes drying, this study hypothesizes that implementing a staged drying program, which dynamically adjusts parameters in accordance with the varying temperature and humidity demands throughout the drying process, can significantly enhance energy efficiency, minimize fabric damage, and reduce the overall environmental footprint of cotton textile drying in domestic clothes dryers. To identify key parameters in each drying stage, a thorough analysis of the time-variability and stage characteristics of the drying process was conducted. Thus, an efficient experiment design was implemented to balance the heater power, relative humidity dividing points between adjacent stages and air circulation. Results indicated that a staged drying program, utilizing the temperature and humidity characteristics of each drying stage, consumed less electricity during tumble-drying. Specifically, energy consumption was reduced by approximately one-third compared to the original program. Fabric smoothness improved by 1.0 grade, and over-drying is efficiently prevented. Furthermore, the environmental and economic impacts were reduced by applying the optimized program. Assuming a total usage of 1000 times for a dryer and an average drying load size of 3.5 kg per drying cycle, employing the optimized program can result in a reduction of 148 kg in CO2 emissions and a savings of $26 throughout the dryer’s entire lifespan. This finding provides insights for dryer manufacturers to optimize drying procedures based on cloth types, and it will significantly reduce the environmental impact of drying machines.

Identification of congenital aortic valve malformations in juvenile natriuretic peptide receptor 2-deficient mice using high-frequency ultrasound.

Mouse models of congenital aortic valve malformations are useful for studying disease pathobiology, but most models have incomplete penetrance [e.g., ∼2 to 77% prevalence of bicuspid aortic valves (BAVs) across multiple models]. For longitudinal studies of pathologies associated with BAVs and other congenital valve malformations, which manifest over months in mice, it is operationally inefficient, economically burdensome, and ethically challenging to enroll large numbers of mice in studies without first identifying those with valvular abnormalities. To address this need, we established and validated a novel in vivo high-frequency (30 MHz) ultrasound imaging protocol capable of detecting aortic valvular malformations in juvenile mice. Fifty natriuretic peptide receptor 2 heterozygous mice on a low-density lipoprotein receptor-deficient background (Npr2+/-;Ldlr-/-; 32 males and 18 females) were imaged at 4 and 8 wk of age. Fourteen percent of the Npr2+/-;Ldlr-/- mice exhibited features associated with aortic valve malformations, including 1) abnormal transaortic flow patterns on color Doppler (recirculation and regurgitation), 2) peak systolic flow velocities distal to the aortic valves reaching or surpassing ∼1,250 mm/s by pulsed-wave Doppler, and 3) putative fusion of cusps along commissures and abnormal movement elucidated by two-dimensional (2-D) imaging with ultrahigh temporal resolution. Valves with these features were confirmed by ex vivo gross anatomy and histological visualization to have thickened cusps, partial fusions, or Sievers type-0 bicuspid valves. This ultrasound imaging protocol will enable efficient, cost effective, and humane implementation of studies of congenital aortic valvular abnormalities and associated pathologies in a wide range of mouse models.NEW & NOTEWORTHY We developed a high-frequency ultrasound imaging protocol for diagnosing congenital aortic valve structural abnormalities in 4-wk-old mice. Our protocol defines specific criteria to distinguish mice with abnormal aortic valves from those with normal tricuspid valves using color Doppler, pulsed-wave Doppler, and two-dimensional (2-D) imaging with ultrahigh temporal resolution. This approach enables early identification of valvular abnormalities for efficient and ethical experimental design of longitudinal studies of congenital valve diseases and associated pathologies in mice.

Research on the efficient process-oriented structural optimization method of the large-scale vacuum cryostat for fusion reactors

An efficient optimization design for the large and complex components of fusion reactors is crucial to address the engineering design requirements and further promote technical standardization. Based on research status, current engineering designs for fusion reactors have some deficiencies, such as time and energy wastage, inefficiency, and difficulties in covering the typical ‘multi-variable multi-objective’ design requirements. These are pressing and common problems that urgently need to be overcome. To deal with the aforementioned technical challenges, it is vitally important to design an efficient, precise, and normalized approach that is tailored for the development of future fusion reactors. Therefore, this paper proposes a process-oriented optimization design method, which involves Coupled external parameterized modeling, Experimental points design, Response surface optimization, and Structural integrity validation (CERS), to improve the currently inefficient design methods. And the vacuum cryostat, the largest and complex component of a tokamak, is taken as an example to present the basic procedures of CERS. Firstly, the functions, basic structures, load types, analysis methods, and verification criteria of the cryostat are presented in detail. Then, real-time data interaction between external global parametric variables and ANSYS via coupling is established by CERS, which achieves parametric modeling of the cryostat and efficient experimental point design and optimization analysis with multi-variables and multi-objectives in an automatic way. Subsequently, this study demonstrates the significance and sensitivity of various structural parameters of the cryostat from such objectives as maximum deformation, maximum equivalent stress, and total mass. And the optimal set of its structural parameters is obtained by establishing a mathematical optimization model. Finally, the structural integrity is verified. The results indicate that the optimized cryostat maintains a minimum safety margin of 23% and will not suffer fatigue damage under various load events during its service. Moreover, the nonlinear buckling load multiplier ∅ is 5.4, obtained by analyzing the load-displacement curve of the cryostat according to the zero-curvature criterion. This shows that the designed cryostat is stable enough. The proposed method is simple, efficient, and reliable, and can be applied to both the cryostat and other complex components of fusion reactors in engineering design fields. It has great value of practical technical reference and can further promote the standardization of engineering design technology for future fusion reactors.

Guidelines for descriptor assignments for the solvation parameter model by separation techniques

The solvation parameter model uses six compound descriptors to model equilibrium properties in biphasic systems formally defined as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L. The V descriptor can be assigned from structure and the E descriptor for compounds liquid at 20 °C can be calculated from its refractive index and characteristic volume. The E descriptor for compounds solid at 20 °C and the S, A, B, and L descriptors are assigned from experimental properties traditionally obtained by chromatographic, liquid-liquid partition, and solubility measurements. Here I report an efficient experimental design using the Solver method for the accurate assignment of descriptors for neutral compounds that simultaneously minimizes laboratory resources. This multi-technique approach requires 3 retention factor measurements in a 60 °C temperature range per compound on four columns by gas chromatography, 3 retention factor measurements in a 30 % (v/v) acetonitrile composition range per compound on two columns by reversed-phase liquid chromatography, and eight partition constant measurements by liquid-liquid partition in totally organic and aqueous biphasic systems for a total of 26 experimental measurements. The accuracy of the descriptor assignments was validated by comparison with the values in the Wayne State University (WSU) descriptor database taken as the best estimate of the true descriptor values. The E, S, A, B and L descriptors were assigned simultaneously by the Solver method using the above approach without significant bias and with an average absolute deviation (AAD) of 0.054, 0.018, 0.015, 0.013, and 0.040, respectively, compared with the WSU database values, corresponding to a relative absolute average deviation in percent (RAAD) of 7.2, 1.9, 3.6, 5.1, and 0.84 %, respectively, for 32 varied compounds. This streamlined approach represents a significant improvement on earlier single-technique approaches used as the starting point for the development of the multi-technique approach. For compounds of variable hydrogen-bond basicity modifications to the multi-technique approach were implemented while maintaining the same number of experimental measurements. Acceptable descriptor assignments for B/B° were obtained for compounds liquid at 20 °C for which the E descriptor was available by calculation. For solid compounds at 20 °C the E and B/B° descriptors are restricted to qualitative application where approximate values may be acceptable.

Super-resolution proximity labeling with enhanced direct identification of biotinylation sites

Promiscuous labeling enzymes, such as APEX2 or TurboID, are commonly used in in situ biotinylation studies of subcellular proteomes or protein–protein interactions. Although the conventional approach of enriching biotinylated proteins is widely implemented, in-depth identification of specific biotinylation sites remains challenging, and current approaches are technically demanding with low yields. A novel method to systematically identify specific biotinylation sites for LC-MS analysis followed by proximity labeling showed excellent performance compared with that of related approaches in terms of identification depth with high enrichment power. The systematic identification of biotinylation sites enabled a simpler and more efficient experimental design to identify subcellular localized proteins within membranous organelles. Applying this method to the processing body (PB), a non-membranous organelle, successfully allowed unbiased identification of PB core proteins, including novel candidates. We anticipate that our newly developed method will replace the conventional method for identifying biotinylated proteins labeled by promiscuous labeling enzymes.

Systematic quantification of modeling uncertainties in tank–foundation coupled systems

Traditionally, uncertainty quantification in the context of liquid storage tanks has primarily focused on ground motion record-to-record variability, along with partial consideration of material randomness. This paper presents a comprehensive framework for addressing “modeling uncertainty” in the seismic analysis of rectangular tanks, including the influence of foundation interactions. The framework encompasses uncertainties inherent in both general finite element and mathematical modeling techniques, as well as uncertainty in modeling parameters (e.g., hydrodynamic mass and concrete constitutive model). This is achieved through the implementation of an efficient design of experiments. By employing an innovative intensifying artificial acceleration, over 26,000 transient simulations are conducted, spanning the full spectrum of structural behavior from linear to nonlinear regimes. Through a series of uncertainty quantification and sensitivity analyses, this study assesses bias and dispersion arising from diverse model assumptions. Furthermore, fragility analysis and machine learning post-processing techniques are employed to extract latent insights from the generated data. The outcomes underscore that numerical and mathematical modeling uncertainties can bear comparable significance to ground motion record-to-record variability. The adoption of distinct modeling strategies introduces biases in fragility curves, particularly within higher damage states. Consequently, it is advised to account for modeling randomness directly (via simulation) or approximately (leveraging the insights from this study) in any uncertainty quantification related to storage tanks.

On the impact of decision rule assumptions in experimental designs on preference recovery: An application to climate change adaptation measures

Efficient experimental designs aim to maximise the information obtained from stated choice data to estimate discrete choice models' parameters statistically efficiently. Almost without exception efficient experimental designs assume that decision-makers use a Random Utility Maximisation (RUM) decision rule. When using such designs, researchers (implicitly) assume that the decision rule used to generate the design has no impact on respondents' choice behaviour. This study investigates whether the decision rule assumption underlying an experimental design affects respondents' choice behaviour. We use four stated choice experiments on coastal adaptation to climate change: Two are based on experimental designs optimised for utility maximisation and two are based on experimental designs optimised for a mixture of RUM and Random Regret Minimisation (RRM). Generally, we find that respondents place value on adaptation measures (e.g., dykes and beach nourishments). We evaluate the models' fits and investigate whether some choice tasks particularly invoke RUM or RRM decision rules. For the latter, we develop a new sampling-based approach that avoids the confounding between preference and decision rule heterogeneity. We find no evidence that RUM-optimised designs invoke RUM-consistent choice behaviour. However, we find a relationship between some of the attributes and decision rules, and compelling evidence that some choice tasks invoke RUM consistent behaviour while others invoke RRM consistent behaviour. This implies that respondents’ choice behaviour and choice modelling outcomes are not exogenous to the choice tasks, which can be particularly critical when information on preferences is used to inform actual decision-making on a sensitive issue of common interest as climate change.

A person-centred approach to help services and commissioners to improve the experience of people who are homeless and secure better sustainability.

Successful implementation of evidence-based services in health and social care depends largely on the fit of the services with the values and priorities of users who are shaping and participating in their delivery and use. When looking at the implementation of innovative specialist hospital discharge schemes for people who are homeless, there is a lack of knowledge on homeless people’s preferences and perspectives on receiving out-of-hospital care.
 In England, the Department of Health and Social Care, Ministry for Housing and Local Government and Ministry of Justice allocated £16 million to further develop and test out hospital care for people experiencing homelessness. The out-of-hospital care model programme is seeking to scale successful hospital discharge models that were shown to be effective and cost-effective in an earlier pilot programme adapting these for new contexts and circumstances post Covid-19. Evaluation of the implementation of the out-of-hospital care model programme is currently underway in 17 local authority test sites across England. A specific component of the larger evaluation is aimed at capturing service users’ preferences for different types of specialist hospital discharge schemes.
 This is done using a flexible health economics tool for measuring choices in health and social care-related settings named discrete choice experiments (DCE). It measures preferences from individual decision-makers over alternative scenarios (or service provisions). Each alternative is described by several attributes (or characteristics) and the choices made between two or more competing scenarios subsequently determine how preferences are influenced by each attribute (eg, which attributes are valued as well as their relative importance). It can also provide a measure of the overall value attached to different alternatives (and identify optimal service provision that meets stakeholder requirements and have the best chance of sustainability in the long term). Hence a DCE survey has been developed to measure people’s preferences for what out-of-hospital care provides in terms of types of location of care, the professional who delivers most of their care, how often they receive care, how long they receive care after hospital discharge and rules about behaviour where they live.
 The attributes (characteristics) and their various levels were informed by our previous research. Webinar discussions regarding what stakeholders value about specialist services were used to validate and refine the attributes and levels. PPI stakeholders were also consulted on the development of the presentation, wording and format of the survey. Efficient experimental design techniques were applied to create the DCE questions. Due to the cognitive load involved in completing the survey, respondents are assisted in the completion of the questionnaire by members of the research team to secure responses from more than 250 people who are homeless. Data will be modelled using logit techniques and results will be ready for presentation in May 2023.
 Information on how service users value specialist hospital discharge schemes for people who are homeless is needed to better inform the implementation and development of the innovative out-of-hospital care model programme currently proposed in England at a national level. Identified learning will be shared at the conference.

An interactive tool for designing efficient toxicology experiments.

The design of dose-response experiments is an important part of toxicology research. Efficient design of these experiments requires choosing optimal doses and assigning the correct number of subjects to those doses under a given criterion. Optimal design theory provides the tools to find the most efficient experimental designs in terms of cost and statistical efficiency. However, the mathematical details can be distracting and make these designs inaccessible to many toxicologists. To facilitate use of these designs, we present an easy to use web-app for finding two types of optimal designs for models commonly used in toxicology. We include tools for checking the optimality of a given design and for assessing efficiency of any user-supplied design. Using state-of-the-art nature-inspired metaheuristic algorithms, the web-app allows the user to quickly find optimal designs for estimating model parameters or the benchmark dose.

Parametric Optimizing Green Sand-Casting Process Parameters using hybrid Taguchi Grey Relational Analyses and Principal Component Analyses

The Green Sand-casting technique is a very ancient method of casting that has many different uses. The increased rate of errors and rejection in this process is a key drawback that reduces output and profits. It’s challenging to develop a good link between the many different parameters and defects since the process is so complicated. This article describes a hybrid approach to find the co-relation for sand casting process’s variables. This approach mixes the Taguchi method (TM) with Grey Relational Analysis (GRA) paired with Principal Component Analysis (PCA). Moisture content, Permeability, Loss of Ignition, Pouring Time & Pouring Temperature selected as input parameters while types of defects (Shrinkage, Blow holes, Cracks, Porosity) as responses for proposed study. The L27 OA from Taguchi is used to plan the tests. TM implemented to analyse individual responses. GRA is applied to find optimal solutions for a set of replies, whereas PCA is used to determine how much weight each response should be given. Using proposed methodology, 4% moisture content, 160% permeability, 5% loss of ignition, 60 seconds of pouring time, and 1400°C found as optimum set of parameters. The findings demonstrate that the hybrid approach, which makes use of both a cost-effective and efficient experimental design strategy, was successful in resolving the complexity trade-off experienced throughout the judgment process of multi-response optimization.

Machine Learning Driven Channel Thickness Optimization in Dual-Layer Oxide Thin-Film Transistors for Advanced Electrical Performance.

Machine learning (ML) provides temporal advantage and performance improvement in practical electronic device design by adaptive learning. Herein, Bayesian optimization (BO) is successfully applied to the design of optimal dual-layer oxide semiconductor thin film transistors (OS TFTs). This approach effectively manages the complex correlation and interdependency between two oxide semiconductor layers, resulting in the efficient design of experiment (DoE) and reducing the trial-and-error. Considering field effect mobility (𝜇) and threshold voltage (Vth ) simultaneously, the dual-layer structure designed by the BO model allows to produce OS TFTs with remarkable electrical performance while significantly saving an amount of experimental trial (only 15 data sets are required). The optimized dual-layer OS TFTs achieve the enhanced field effect mobility of 36.1cm2 V-1 s-1 and show good stability under bias stress with negligible difference in its threshold voltage compared to conventional IGZO TFTs. Moreover, the BO algorithm is successfully customized to the individual preferences by applying the weight factors assigned to both field effect mobility (𝜇) and threshold voltage (Vth ).

Understanding Relapsed/Refractory Acute Leukemia Patients' Treatment Preferences: Insights from Qualitative Research

Introduction Evidence on patient preferences is increasingly being used to inform regulatory and reimbursement decisions. In acute leukemia, the number of treatment options is expanding but there is limited quantitative evidence on patients' treatment preferences, especially in the event of relapse. The Acute Leukemia Advocates Network (ALAN) in collaboration with The Office of Health Economics are running a quantitative patient preference study using a discrete choice experiment (DCE) to 1/ Elicit adult acute leukemia patients' preferences for treatment outcomes and characteristics in the event of a future relapse 2/ Explore the tradeoffs that patients are willing to make between different hypothetical treatments 3/ Describe preference heterogeneity in the patient population and 4/ Provide useful insights that could demonstrate patient value for a range of upcoming health technology assessments of relapsed/refractory treatments and guide innovation and development throughout the medical product lifecycle. Here we report on two stages of qualitative research: 1/ Online bulletin boards (OBBs) to identify potential attributes for the DCE and 2/ Cognitive ‘think aloud’ pilot interviews to test participant understanding of the draft DCE survey. Methods Two structured OBBs were conducted - one each for acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Each day, participants were asked up to four open questions on the following topics: “your diagnosis and expectation of treatment”, “your first experience of treatment”, “treatment following a relapse”, and “your treatment priorities”. Participants could see and respond to each other's answers. Three researchers acted as moderators and engaged in discussions where appropriate. Following the design of the DCE survey, a series of online one-on-one think-aloud interviews with AML and ALL participants was subsequently conducted to pilot the DCE survey. Interviewees were asked to verbally reflect on their responses, with occasional interviewer prompts. The pilots were conducted in two separate weeks, with a week in between to allow for changes to be made. All participants were recruited via ALAN and its members. Results A total of 12 AML and nine ALL patients took part in the OBBs, of which 15 were female. Age (range: 26-71; average: 51.1) and years of living with the disease (range: <3 to > 10 years) were varied. Both the AML and ALL groups agreed that the effectiveness of the treatment in achieving long-term stable remission would be most important. Severe long-term side effects would also be an important concern but many participants said they could put up with potentially severe short-term side effects for a good chance of long-term survival. Additional concerns were the length of hospital stays, availability of psychological/mental/emotional support, catheter-related pain and infections, and mode of administration (MoA). Several participants argued that an individualized approach to treatment is preferred. Thematic analysis of the OBB data led to the identification of five attributes for the DCE: chance of response (20-95%), duration of response (6-18 months), quality of life (QoL) during treatment (0-50%), QoL during response (25-75%), and MoA (tablets taken at home, injections requiring a hospital stay, injections as outpatient appointments). The DCE was designed using an efficient experimental design and coded as part of an online survey, and subsequently piloted (see Figure 1). In week 1 of the pilots (n=5 interviews), ‘duration of response’, was often misinterpreted as ‘duration of treatment’. Interviewees also interpreted treatment and response as potentially overlapping, and therefore struggled to separate the two QoL attributes. Therefore, we made several changes including explicitly structuring the choice tasks to distinguish treatment and response phases. The changes improved understanding in week 2 (n=5 interviews) and raised some additional minor issues for consideration. Conclusion People with acute leukemia have a range of concerns about treatments in the context of a relapse. However, the primary issues identified in the qualitative research relate to the chance and duration of treatment success, QoL during and after treatment, and the MoA. The pilot interviews were valuable in improving the wording and overall quality of the final survey, which has since been fully launched.

Preferences in the Design and Delivery of Neurodevelopmental Follow-Up Care for Children: A Systematic Review of Discrete Choice Experiments.

Neurodevelopmental disorders are a significant cause of morbidity. Early detection of neurodevelopmental delay is essential for timely diagnosis and intervention, and it is therefore important to understand the preferences of parents and clinicians for engaging with neurodevelopmental surveillance and follow-up care. Discrete choice experiment (DCE) may be an appropriate method for quantifying these preferences. This review systematically examined how DCEs have been designed and delivered in studies examining neurodevelopmental care of children and identified the preferred attributes that have been reported. PubMed, Embase, CINAHL, and Scopus databases were systematically searched. Studies were included if they used DCE to elicit preferences for a neurodevelopmental follow-up program for children. Two independent reviewers conducted the title and abstract and full-text screening. Risk of bias was assessed using a DCE-specific checklist. Findings were presented using a narrative synthesis. A total of 6618 records were identified and 16 papers were included. Orthogonal (n=5) and efficient (n=5) experimental designs were common. There was inconsistent reporting of design-related features. Analysis was primarily completed using mixed logit (n=6) or multinomial logit (n=3) models. Several key attributes for neurodevelopmental follow-up care were identified including social, behavioral and emotional support, therapy, waiting time, and out-of-pocket costs. DCE has been successfully used as a preference elicitation method for neurodevelopmental-related care. There is scope for improvement in the design and analysis of DCE in this field. Nonetheless, attributes identified in these studies are likely to be important considerations in the design and implementation of programs for neurodevelopmental care.

Efficient sampling-based Bayesian Active Learning for synaptic characterization.

Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters. However, the applicability of BAL to experiments is limited as it requires performing high-dimensional integrations and optimizations in real time. Current methods are either too time consuming, or only applicable to specific models. Here, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is efficient enough to be used in real-time biological experiments. We apply our method to the problem of estimating the parameters of a chemical synapse from the postsynaptic responses to evoked presynaptic action potentials. Using synthetic data and synaptic whole-cell patch-clamp recordings, we show that our method can improve the precision of model-based inferences, thereby paving the way towards more systematic and efficient experimental designs in physiology.

Computationally Efficient Adaptive Design of Experiments for Global Metamodeling through Integrated Error Approximation and Multicriteria Search Strategies

Gaussian processes (GPs) are a popular technique for global metamodeling applications. Their objective in such settings is to establish an efficient and globally accurate approximation of the response surface of computationally expensive simulation models. When developing such GPs, the design of (simulation) experiments (DoE) plays an important role in reducing the required number of model runs for obtaining accurate approximations. Sequential (adaptive) selection of experiments can provide significant advantages, especially when the response surface is characterized by localized nonlinearities. Such adaptive DoE strategies for global metamodeling applications typically focus on minimizing the predictive GP variance, representing an exploration strategy, while recent developments have additionally considered the reduction of the GP bias obtained through cross validation, representing an exploitation strategy. While significant focus has been placed on the definition of appropriate adaptive DoE criteria, computational challenges still exist that limit the widespread adoption of adaptive DoE techniques—for example, related to the additional computational demand for identifying the optimal new experiment(s) or the necessity to establish proper schemes to combine exploration and exploitation strategies. To address these specific challenges, this research investigates two new adaptive DoE formulations. The first one focuses on the approximation of the popular integrated mean square error (IMSE) DoE criterion. The computationally demanding GP predictive variance update (after addition of each candidate experiment), required in the original IMSE formulation, is replaced by an approximation based on the current predictive variance and the domain of influence that surrounds each new experiment. The approximation is established through a parametric formulation that leverages the GP kernel to describe the aforementioned domain, with characteristics that are progressively calibrated across the GP training stages, to minimize the discrepancy between the actual and the approximated IMSE. The second formulation establishes a multicriteria search for simultaneously identifying multiple Pareto optimal experiments that balance exploration and exploitation objectives, replacing conventional strategies that establish a weighted combination of these objectives to promote a single DoE selection criterion.

Optimization of smartphone psychotherapy for depression and anxiety among patients with cancer using the multiphase optimization strategy (MOST) framework and decentralized clinical trial system (SMartphone Intervention to LEssen depression/Anxiety and GAIN resilience: SMILE AGAIN project): a protocol for a randomized controlled trial

BackgroundCancer patients experience various forms of psychological distress. Their distress, mainly in the form of depression and anxiety, leads to poor quality of life, increased medical spending due to frequent visits, and decrease in treatment adherence. It is estimated that 30–50% among them would require support from mental health professionals: in reality, much less actually receive such support partly due to a shortage of qualified professionals and also due to psychological barriers in seeking such help. The purpose of the present study is to develop the easily accessible and the most efficient and effective smartphone psychotherapy package to alleviate depression and anxiety in cancer patients.MethodsBased on the multiphase optimization strategy (MOST) framework, the SMartphone Intervention to LEssen depression/Anxiety and GAIN resilience project (SMILE-AGAIN project) is a parallel-group, multicenter, open, stratified block randomized, fully factorial trial with four experimental components: psychosocial education (PE), behavioral activation (BA), assertion training (AT), and problem-solving therapy (PS). The allocation sequences are maintained centrally. All participants receive PE and then are randomized to the presence/absence of the remaining three components. The primary outcome of this study is the Patient Health Questionnaire-9 (PHQ-9) total score, which will be administered as an electronic patient-reported outcome on the patients’ smartphones after 8 weeks. The protocol was approved by the Institutional Review Board of Nagoya City University on July 15, 2020 (ID: 46-20-0005). The randomized trial, which commenced in March 2021, is currently enrolling participants. The estimated end date for this study is March 2023.DiscussionThe highly efficient experimental design will allow for the identification of the most effective components and the most efficient combinations among the four components of the smartphone psychotherapy package for cancer patients. Given that many cancer patients face significant psychological hurdles in seeing mental health professionals, easily accessible therapeutic interventions without hospital visits may offer benefits. If an effective combination of psychotherapy is determined in this study, it can be provided using smartphones to patients who cannot easily access hospitals or clinics.Trial registrationUMIN000041536, CTR. Registered on 1 November 2020 https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000047301.

Predicting cellular responses to complex perturbations in high-throughput screens.

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

D-Optimal Designs for Binary and Weighted Linear Regression Models: One Design Variable

D-optimality is a well-known concept in experimental design that seeks to select an optimal set of design points to estimate the unknown parameters of a statistical model with a minimum variance. In this paper, we focus on proving a conjecture made by Ford, Torsney and Wu regarding the existence of a class of D-optimal designs for binary and weighted linear regression models. Our concentration is on models with one design variable. The conjecture states that, for any given level of precision, there exists a two-level factorial design that is D-optimal for these models. To prove this conjecture, we use an intuitive approach that explores various link functions in the generalised linear model context to establish the veracity of the conjecture. We also present explicit and clear plots of various functions wherever deemed necessary and appropriate to further strengthen the proofs. Our results establish the existence of D-optimal designs for binary and weighted linear regression models with one design variable, which have important implications for the efficient design of experiments in various fields. These findings contribute to the development of optimal experimental designs for studying binary and weighted linear regression models and provide a foundation for future research in this area.