Prioritization Strategies Research Articles

Integrated Multi-Criteria Decision Making Via Trapezoidal Neutrosophic Sets to Evaluate the Risks of Failure Mode

The purpose of a failure mode and effect analysis (FMEA) is to improve the safety and dependability of a system, product, procedure, or facility by identifying potential points of failure and determining the consequences of such failures. The assessment of failure modes, the weighting of risk factors, and the ranking of failure modes are all areas where the conventional FMEA falls short when put to use in the real world. To assess the hazard of failure modes in a trapezoidal neutrosophic sets environment, this research proposes a model that combines the neutrosophic sets and MCDM technique such as WASPAS. The WASPAS MCDM method is used to calculate the weights of standards and order the alternatives. Advantages of trapezoidal neutrosophic numbers in dealing with uncertainty, ambiguity, and incompleteness are combined with the benefits of WASPAS to create the suggested risk prioritization strategy.

How Can We Measure the Prioritization of Strategies for Transitioning to a Circular Economy at Macro Level? A New Approach

This paper aims to develop and validate a questionnaire to measure the prioritization of strategies to transitioning towards a circular economy and to compare different countries. We proposed a questionnaire based on a five-level ordinal scale to evaluate the 24 strategies of the circular economy under a set of ten criteria given technical issues, as well as social, environmental and economic dimensions of sustainability. Each strategy was defined and evaluated by the participants on the same scale, who are stakeholders involved in the transition towards a circular economy. The validation was conducted by two main procedures, including semantic validation through 17 experts from five countries and statistical validation by 347 survey respondents from 25 countries. The results show that the questionnaire seems a reliable and valid tool to analyze which circular economy strategies countries, regions and cities prioritize. Few studies have been conducted to validate instruments about the circular economy; they are focused at the micro level, (industry). Studies that validate measurement instruments regarding the circular economy at the macro level are lacking. This study is the first one to validate the circular economy instrument at the macro level. It can help researchers and practitioners from public and private sectors from a variety of organizations in proposing actions to enable the transition and create performance indicators.

Integrating principles and tools of decision science into value-driven watershed planning for compensatory mitigation.

Several environmental policies strive to restore impaired ecosystems and could benefit from a consistent and transparent process-codeveloped with key stakeholders-to prioritize impaired ecosystems for restoration activities. The Clean Water Act, for example, establishes reallocation mechanisms to transfer ecosystem services from sites of disturbance to compensation sites to offset aquatic resource functions that are unavoidably lost through land development. However, planning for the prioritization of compensatory mitigation areas is often hampered by decision-making processes that fall into a myopic decision frame because they are not coproduced with stakeholders. In this study, we partnered with domain experts from the North Carolina Division of Mitigation Services to codevelop a real-world decision framework to prioritize catchments by potential for the development of mitigation projects following principles of a structured decision-making process and knowledge coproduction. Following an iterative decision analysis cycle, domain experts revised foundational components of the decision framework and progressively added complexity and realism as they gained additional insights or more information became available. Through the course of facilitated in-person and remote interactions, the codevelopment of a decision framework produced three main "breakthroughs" from the perspective of the stakeholder group: (a) recognition of the problem as a multiobjective decision driven by several values in addition to biogeophysical goals (e.g., functional uplift, restoring or enhancing lost functionality of ecosystems); (b) that the decision comprises a linked and sequential planning-to-implementation process; and (c) future risk associated with land-use and climate change must be considered. We also present an interactive tool for "on-the-fly" assessment of alternatives and tradeoff analysis, allowing domain experts to quickly test, react to, and revise prioritization strategies. The decision framework described in this study is not limited to the prioritization of compensatory mitigation activities across North Carolina but rather serves as a framework to prioritize a wide range of restoration, conservation, and resource allocation activities in similar environmental contexts across the nation.

COVID-19 Vaccination Prioritization Strategies in Malaysia: A Retrospective Analysis of Early Evidence

The coronavirus disease 2019 (COVID-19) that can cause extreme acute respiratory syndrome has posed a catastrophic threat to public health. The vaccines had indeed restored optimism and, after more than two years of battling the pandemic, there is renewed hope for the transition to endemicity. At the start of vaccination efforts, when supply shortages of vaccines were inevitable, every nation determined the high-risk population groups to be given priority for the COVID-19 vaccines. In this paper, the characteristics of the initial COVID-19 vaccine recipients in Malaysia are described. In line with the policies of many other countries, Malaysia firstly inoculated frontline healthcare workers, and subsequently the list of front liners grew to include defense and security personnel and those involved in the provision of essential services. People with disabilities or those with special needs and several underlying medical conditions that increased their risk of developing severe COVID-related illnesses were included in the priority categories. These included patients with severe lung disease, chronic heart disease, chronic kidney disease, chronic liver disease, neurological disease, diabetes mellitus and obesity in adults, splenic dysfunction, and severe mental illness. With little information and under circumstances of great uncertainty, the Health Ministry of a middle-income country had developed a vaccination priority-list based on the disease's epidemiology and clinical data, vaccine type, operational considerations, and risk evaluation. Early evidence was presented and suggested that the full vaccination with any of the three predominant vaccines (AZD1222, BNT162b2, and CoronaVac) in the country had been highly effective in preventing COVID-19 infections, COVID-19-related ICU admissions, and death. As many SARS-CoV-2 variants of concern (VoC), such as the Omicron BA.2/4/5, are emerging, future vaccination strategies may necessitate the need to change the immunogen of the vaccine, as well as considerations for when to give high-risk groups booster injections. These considerations are valuable for future planning by policymakers and healthcare providers to make vaccination policy and decisions, especially for the inclusion of the COVID-19 vaccines into national immunization programs.

Predicting Endocrine Disruption Using Conformal Prediction - APrioritization Strategy to Identify Hazardous Chemicals with Confidence.

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

The presence of extra-pulmonary treatable traits increases the likelihood of responding to pulmonary rehabilitation

IntroductionStudies suggest that people with chronic obstructive pulmonary disease (COPD) who are worse at baseline respond better to pulmonary rehabilitation (PR). Identifying treatable traits (TTs) may help to distinguish responders from non-responders. We explored the impact of PR on extra-pulmonary traits of people with COPD and whether the presence of TT influences the type of response to PR. MethodsA comprehensive assessment of 9 TT including symptoms (dyspnoea, fatigue, anxiety and depression), functional capacity, deconditioning, balance, impact of the disease and health-related quality of life was conducted before and after a 12-week community-based PR programme. Pre-post differences between people with or without each TT at baseline were compared with independent samples t-tests or Mann-Whitney U tests. Proportion of responders between groups were explored with chi-square tests and odds ratio. Results102 people with COPD were included (70 [65; 75] years old, 78% male, FEV1 47 [36; 60] %predicted). They had a median of 3 (out of 9) TTs per person and each patient responded on average to 5 (out of 9) outcomes of PR. People with TT were more responsive than those without them in all outcomes (p < 0.05) except for the 1-min sit-to-stand test. The presence of TT increased 4 to 20 times the likelihood of being a good responder. ConclusionsIdentification of baseline extra-pulmonary TT in people with COPD showed the potential to inform on PR responsiveness and might therefore be an important strategy for patient prioritization, treatment personalisation (i.e., activation of the most suitable components) and optimisation.

OGC P40 The RM Partners Cancer Hub approach to Upper Gastrointestinal Surgery during the COVID-19 pandemic

Abstract Background The COVID-19 pandemic led to significant cancellations of major surgery to maximise critical care capacity. Our unit instituted the ‘RM Partners Cancer Hub’ based at the Royal Marsden Hospital in London, which allowed cancer surgery from multiple NHS Trusts to continue in a ‘COVID-lite’ setting. We present the outcomes for Upper GastroIntestinal (UGI) surgery managed via this approach over the course of the pandemic. Methods From April 2020 to April 2021, the Royal Marsden Hospital formed the ‘RM Partners Cancer Hub’. This was designed to co-ordinate resources and deliver as much oncological treatment and cancer surgery as feasible for patients across the RM Partners West London Cancer Alliance. An UGI clinical case prioritisation strategy, along with strict infection control pathways and pre-operative screening protocols were adopted. Results Overall, 231 patients underwent surgery for confirmed UGI cancer via the RM Partners Cancer Hub, with 213 completed resections and combined 90-day mortality rate of 3.5%. Good short-term survival outcomes were demonstrated with 2-year Disease Free Survival (DFS) and Overall Survival (OS) for oesophageal (DFS 70.8%; OS 72.9%), gastric (DFS 66.7%; OS 83.3%) and pancreatic cancer resections (DFS 68.0%, OS 88.0%). One patient developed peri-operative COVID-19 during the RM Partners Cancer Hub operation; they made a full recovery with no lasting clinical sequelae. Conclusions The ‘RM Partners Cancer Hub’ approach provided a workable model for delivering multidisciplinary UGI cancer care and surgery with favourable 2-year DFS and OS when compared to nationally published pre- and post-pandemic data. It also established a template for cancer service during periods of marked disruption to healthcare service delivery and should be a useful guide in the future planning of safe operating pathways.

Prioritizing vaccination based on analysis of community networks

Many countries that had early access to COVID-19 vaccines implemented vaccination strategies that prioritized health care workers and the elderly. As barriers to access eased, vaccine prioritization strategies have been relaxed. However, these strategies are still an important tool for decision makers to manage new variants, plan for future booster shots, or stage mass vaccinations. This paper explores the impact of vaccine prioritization strategies using networks that represent communities with different demographics and connectivity. The impact of vaccination is compared to non-medical intervention to reduce transmission. Several sources of uncertainty are considered, including vaccine willingness and mask effectiveness. This paper finds that while prioritization strategies can have a large impact on reducing deaths and peak hospitalization, selecting the best strategy depends on community characteristics and the desired objective. Additionally, in some cases random vaccination performs as well as more targeted prioritization strategies. Understanding these trade-offs is important when planning vaccine distribution.

Public transport service quality: Policy prioritization strategy in the importance-performance analysis and the three-factor theory frameworks

• A rigorous analytical framework was utilized to identify an appropriate service quality analysis method. • The three-factor theory was superior to the IPA due to the non-linear and asymmetric relations between service quality factors and rider satisfaction. • All the service factors were found to be performance factors for captive riders. • Eight of nine factors were performance for non-captive (vehicle-owned) riders, while environment friendliness was excitement. • This is the first study using the three-factor theory in Vietnam. Service quality is a determinant of increasing public transport rider satisfaction. However, understanding of service quality and satisfaction relations has been limited. This study seeks an appropriate method to demonstrate the nature of service quality-satisfaction relations. Thereby, research results can be methodologically, theoretically, and empirically defensible. The importance-performance analysis (IPA) and the three-factor theory, the most widely preferred for public transport service quality prioritization based on the importance of service factors to rider satisfaction, will be utilized. The study takes transit service in Ho Chi Minh City (HCMC), Vietnam, to demonstrate that the three-factor theory, which has been limitedly utilized despite being more advantageous and robust, is superior to the IPA. Although the extent to which the service quality factors affect satisfaction varied across rider segments, the types of service quality factors were found consistent. For example, captive riders considered all service factors as performance. Non-captive riders considered the environment factor as excitement. No basic factor was found across rider groups. This study contributes to the limited understanding of the non-linear and asymmetric relations between service quality and satisfaction of various rider groups, which is rarely found in transit service quality literature. Furthermore, it introduced a case study of Vietnamese urban transit services for the first time. It suggests that enhancing passenger information, rider care, and comfort are recommended as the most practical and economical measures in the short run. However, the city government should improve availability and accessibility to promote public transport ridership in the long run.

OP10 Outcomes Of Expanded Access To Transcatheter Aortic Valve Implantation In Ontario: A Model-Based Analysis

IntroductionTranscatheter aortic valve implantation (TAVI) is a minimally invasive therapy for patients with severe aortic stenosis. In Ontario, increases in capacity have not matched the rapidly growing demand for TAVI. As a result, wait-times for TAVI in Ontario exceed guideline targets, and waitlist morbidity is consequently considerable. The objective of this study was to evaluate the clinical implications of expanded TAVI capacity.MethodsWe performed a decision analysis using an open, parallel, resource-constrained microsimulation from the Ontario Ministry of Health perspective. Simulated patients entered the model during a five-year period, and stayed in the model until death or end of time horizon. Referral numbers increased annually according to historical trends. The additional capacity required to meet wait-time benchmarks in five years was identified by a sensitivity analysis. Clinical outcomes were estimated for three strategies: (i) current practice with annual capacity increases; (ii) accelerated capacity increases achieving benchmarks after five years; and (iii) no increase in capacity. Outcomes included pre-procedural mortality and hospitalization, and the proportion of TAVIs performed urgently.ResultsOver the five years, we estimated that TAVI referrals would increase from 1,980/year to 3,268/year. To achieve wait-time benchmarks during this period, TAVI rates must be increased by approximately 6.3 percent annually, for a total of 12,220 procedures performed over the 5 years. Compared to current TAVI capacity increase, an accelerated increase in capacity achieving wait-time benchmarks led to a reduction of 29.36 percent in pre-procedural deaths, as well as 26.38 percent in pre-procedural hospitalizations and 30.31 percent in nonelective TAVIs.ConclusionsIncreases in TAVI capacity in Ontario must be accelerated to meet wait-time benchmarks in five years. Expansion of TAVI care in Ontario would be associated with considerable reductions in mortality and hospitalizations. Without intervention, both wait-times and adverse outcomes on the waitlist are expected to continue increasing. Prioritization strategies to mitigate the adverse effects of long wait-times must be used until wait-time targets are achieved.

Prioritization strategy of horticulture for agriculture development in north Toraja regency

Horticulture is currently a prima donna commodity that has very good prospects, considering the paradigm shift in food consumption towards the consumption of nutritious, cheap, and affordable food and easy to grow (maintain). The type of horticulture plant is one type of food crop that is classified as a staple food need such as; vegetables and fruits. This plant is commonly found in the North Toraja Regency, such as; in hot peppers, onions, and potatoes. North Toraja Regency is one of the areas that have the potential to develop horticulture agriculture. The purpose of this study is to determine the priority of the horticulture agriculture development strategy in North Toraja Regency. The research method used is a quantitative method with a type of survey research. The study was conducted in North Toraja Regency, South Sulawesi Province, from July to September 2022. The types of data collected include secondary data in the form of research results and formulations of national and regional agricultural policies, as well as primary data in the form of opinions of stakeholders related to horticulture agricultural development strategies in North Toraja Regency. The data analysis method is carried out with the AHP (Analytical Hierarchy Process) technique. The results of the study obtained that the main strategy in the development of horticulture agriculture in North Toraja Regency is a strategy of improving technology and business funding assistance.

UVMS task-priority planning framework for underwater task goal classification optimization.

This paper presents a task prioritization strategy based on a generic underwater task goal classification transformation for multitasking underwater operational tasks: attitude control, floating manipulation, collision-free motion, especially optimizing trajectory of the end-effector of an underwater vehicle manipulator system (UVMS) in a complex marine environment. The design framework aims to divide the complex underwater operational tasks into UVMS executable generic task combinations and optimize the resource consumption during the whole task. In order to achieve the corresponding underwater task settings, the system needs to satisfy different task scheduling structures. We consider the actual application scenarios of the operational goals and prioritize and define each category of task hierarchy accordingly. Multiple tasks simultaneously enable fast adaptation to UVMS movements and planning to complete UVMS autonomous movements. Finally, an underwater vehicle manipulator system implements the task prioritization planning framework for a practical scenario with different constraints on different goals. We quickly and precisely realize the interconversion of different tasks under goal constraints. The autonomous motion planning and real-time performance of UVMS are improved to cope with the increasing operational task requirements and the complex and changing practical engineering application environments.

Disability-Adjusted Life-Years for Drug Overdose Crisis and COVID-19 Are Comparable During the Two Years of Pandemic in the United States

ObjectivesThe drug overdose crisis with shifting patterns from primarily opioid to polysubstance uses and COVID-19 infections are 2 concurrent public health crises in the United States, affecting the population of sizes in different magnitudes (approximately < 10 million for substance use disorder [SUD] and drug overdoses vs 80 million for COVID-19 within 2 years of the pandemic). Our objective is to compare the relative scale of disease burden for the 2 crises within a common framework, which could help inform policy makers with resource allocation and prioritization strategies. MethodsWe calculated disability-adjusted life-years (DALYs) for SUD (including opioids and stimulants) and COVID-19 infections, respectively. We collected estimates for SUD prevalence, overdose deaths, COVID-19 cases and deaths, disability weights, and life expectancy from multiple publicly available sources. We then compared age distributions of estimated DALYs. ResultsWe estimated a total burden of 13.83 million DALYs for SUD and drug overdoses and 15.03 million DALYs for COVID-19 in 2 years since March 2020. COVID-19 burden was dominated by the fatal burden (> 95% of total DALYs), whereas SUD burden was attributed to both fatal (53%) and nonfatal burdens (47%). The highest disease burden was among individuals aged 30 to 39 years for SUD (27%) and 50 to 64 years for COVID-19 (31%). ConclusionsDespite the smaller size of the affected population, SUD and drug overdoses resulted in comparable disease burden with the COVID-19 pandemic. Additional resources supporting evidence-based interventions in prevention and treatment may be warranted to ameliorate SUD and drug overdoses during both the pandemic and postpandemic recovery.

Prioritization of climate change adaptation strategies by smallholder farmers in semi-arid savannah agro-ecological zones: insights from the Talensi District, Ghana

Prioritization of climate change adaptation strategies by smallholder farmers in semi-arid savannah agro-ecological zones: insights from the Talensi District, Ghana

Environmental and economic optimization and prioritization tool-kit for residential building renovation strategies with life cycle approach

The most recent regulations, as well as the scientific studies, remark the importance of the evaluation of the entire life cycle on building renovations, relative to the environmental impact and economic feasibility, making the Life Cycle Assessment (LCA) the prioritizing analysis. The objective of the study is to develop a simplified methodology for the environmental and economic assessment of residential building renovations with life cycle approach. For this, a script-based tool-kit is developed: the first tool optimizes the thickness of envelope insulation based strategies; the second tool is for the prioritization of strategies by assessing their environmental performance and economic feasibility. In order to follow the objective, the development of the two tools is presented: both tools follow a parallel scheme where the input parameters are required by an excel file and the calculation script provides the results automatically by exporting the results excel file. The evaluation provides the quantification of the relative environmental improvement with the net energy ratio (NER), and the economic feasibility by the financial indicator of internal rate of return (IRR). The tool-kit is applied in a case study of a multifamily residential building. The results show, on the one hand, that the usability of the tool-kit can be determinant in the decision-making of stakeholders; and in the other hand, the importance of carrying out a dynamic assessment, taking into account the variation of the results caused by the uncertain parameters that differ in time. Moreover, the tool-kit can assist the development of cost-effective decarbonisation strategies.

An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials

Background and ObjectivesAdaptive features may increase flexibility and efficiency of clinical trials, and improve participants’ chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials. MethodsWe provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials. ResultsWe provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs. ConclusionThis overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.

Co-designing person-centred quality indicator implementation for primary care in Alberta: a consensus study

BackgroundWe aimed to contribute to developing practical guidance for implementing person-centred quality indicators (PC-QIs) for primary care in Alberta, Canada. As a first step in this process, we conducted stakeholder-guided prioritization of PC-QIs and implementation strategies. Stakeholder engagement is necessary to ensure PC-QI implementation is adapted to the context and local needs.MethodsWe used an adapted nominal group technique (NGT) consensus process. Panelists were presented with 26 PC-QIs, and implementation strategies. Both PC-QIs and strategies were identified from our extensive previous engagement of patients, caregivers, healthcare providers, and quality improvement leaders. The NGT objectives were to: 1. Prioritize PC-QIs and implementation strategies; and 2. Facilitate the participation of diverse primary care stakeholders in Alberta, including patients, healthcare providers, and quality improvement staff. Panelists participated in three rounds of activities. In the first, panelists individually ranked and commented on the PC-QIs and strategies. The summarized results were discussed in the second-round face-to-face group meeting. For the last round, panelists provided their final individual rankings, informed by the group discussion. Finally, we conducted an evaluation of the consensus process from the panelists’ perspectives.ResultsEleven primary care providers, patient partners, and quality improvement staff from across Alberta participated. The panelists prioritized the following PC-QIs: ‘Patient and caregiver involvement in decisions about their care and treatment’; ‘Trusting relationship with healthcare provider’; ‘Health information technology to support person-centred care’; ‘Co-designing care in partnership with communities’; and ‘Overall experience’. Implementation strategies prioritized included: ‘Develop partnerships’; ‘Obtain quality improvement resources’; ‘Needs assessment (stakeholders are engaged about their needs/priorities for person-centred measurement)’; ‘Align measurement efforts’; and ‘Engage champions’. Our evaluation suggests that panelists felt that the process was valuable for planning the implementation and obtaining feedback, that their input was valued, and that most would continue to collaborate with other stakeholders to implement the PC-QIs.ConclusionsOur study demonstrates the value of co-design and participatory approaches for engaging stakeholders in adapting PC-QI implementation for the primary care context in Alberta, Canada. Collaboration with stakeholders can promote buy-in for ongoing engagement and ensure implementation will lead to meaningful improvements that matter to patients and providers.

Genetic insights into smoking behaviours in 10,558 men of African ancestry from continental Africa and the UK

Smoking is a leading risk factor for many of the top ten causes of death worldwide. Of the 1.3 billion smokers globally, 80% live in low- and middle-income countries, where the number of deaths due to tobacco use is expected to double in the next decade according to the World Health Organization. Genetic studies have helped to identify biological pathways for smoking behaviours, but have mostly focussed on individuals of European ancestry or living in either North America or Europe. We performed a genome-wide association study of two smoking behaviour traits in 10,558 men of African ancestry living in five African countries and the UK. Eight independent variants were associated with either smoking initiation or cessation at P-value < 5 × 10–6, four being monomorphic or rare in European populations. Gene prioritisation strategy highlighted five genes, including SEMA6D, previously described as associated with several smoking behaviour traits. These results confirm the importance of analysing underrepresented populations in genetic epidemiology, and the urgent need for larger genomic studies to boost discovery power to better understand smoking behaviours, as well as many other traits.

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

A resilient network recovery framework against cascading failures with deep graph learning

Because of the increasing importance and dependencies of infrastructure networks and the potential for massive cascading failures in real-world network systems, maintenance optimization to effectively reduce system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. In this work, a new recovery framework was developed to rapidly identify important system components for maintenance to improve network resilience against cascading failures. This work provides distinct advantages to determine an optimal maintenance priority by combining real-time network structure importance with other maintenance prioritization based on customer preference. This approach adopts structural graph embedding and deep reinforcement learning to extract real-time network topology information (such as minimum vertex cover) to update the maintenance priority during the recovery process. Based on the case studies on synthetic networks and a US airport network, the proposed recovery framework with real-time network topology awareness shows better performance than other maintenance prioritization strategies regarding resilience enhancement. This work improves the understanding of how the changing network structure influences maintenance effects. It also provides insights of the practical usefulness of advanced deep learning on helping optimal maintenance prioritization to effectively reduce the intensity and extent of cascading failures.