Genomic surveillance uncovers a pandemic clonal lineage of the wheat blast fungus.

Wheat, one of the most important food crops, is threatened by a blast disease pandemic. Here, we show that a clonal lineage of the wheat blast fungus recently spread to Asia and Africa following two independent introductions from South America. Through a combination of genome analyses and laboratory experiments, we show that the decade-old blast pandemic lineage can be controlled by the Rmg8 disease resistance gene and is sensitive to strobilurin fungicides. However, we also highlight the potential of the pandemic clone to evolve fungicide-insensitive variants and sexually recombine with African lineages. This underscores the urgent need for genomic surveillance to track and mitigate the spread of wheat blast outside of South America and to guide preemptive wheat breeding for blast resistance.

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective.

The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.

Adopting net-zero in emerging economies.

In recent years, rapid reduction in natural resources alongside climate change has prompted industries to adopt sustainable operational practices. Globalization is arguably a boon for people and societies worldwide but has also led to significant disruptions to our natural ecosystem. Consequently, it has caused environmental concerns and issues around public health. The net-zero economy has recently emerged as a pivotal way to conserve the environment, mitigate health issues and address sustainable development goals (SDGs). The extant literature and relevant industrial reports have shown that automobiles significantly contribute to greenhouse gas emissions. Therefore, the current study is conducted to identify the critical success factors (CSFs) of net-zero adoption with respect to the automobile industry. The fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique is applied to establish a dyadic relationship (cause-effect) among the identified CSFs. The top three CSFs are found to be focus on research and development activities, International Collaborations and Strategic Planning and Effective Roadmap. Finally, this study provides theoretical and practical implications for relevant industries to implement net-zero effectively.

Benchmarking sustainable E-commerce enterprises based on evolving customer expectations amidst COVID-19 pandemic.

The 2019 coronavirus disease (COVID-19) pandemic has seriously impacted the performance of all types of businesses. It has given a tremendous structural boost to e-commerce enterprises by forcing customers to online shopping over visiting physical stores. Moreover, customer expectations of the digital and operational capabilities of e-commerce firms are also increasing globally. Thus, it has become crucial for an e-commerce enterprise to reassess and realign its business practices to meet evolving customer needs and remain sustainable. This paper presents a comprehensive performance evaluation framework for e-commerce enterprises based on evolving customer expectations due to the COVID-19 pandemic. The framework comprises seven primary criteria, which are further divided into 25 sub-criteria, including two sustainability factors, namely, environmental sustainability and carbon emissions. The evaluation approach is then practically demonstrated by analyzing the case of three Indian e-commerce firms. The results are obtained using a multi-criteria decision-making (MCDM) method, namely, Fuzzy VIKOR, to capture the fuzziness of the inherent decision-making problem. Further, numerical analysis is conducted to evaluate and rank various e-commerce enterprises based on customer expectations and satisfaction benchmarks. The findings explain the most important criteria and sub-criteria for e-commerce businesses to ensure customer expectations along with their economic and environmental sustainability.

A novel methodology for perception-based portfolio management.

Selecting and investing in stock market with right proportions is one of the major challenges. Majority of the investors end up losing their invested equity capital due to uncertainty in the market. The present study provides a novel framework for novice investors to construct portfolio based on multicriteria decision making techniques under fuzzy environment. The scores obtained from these techniques were used to introduce two non-dimensional parameters for categorization of risky and non-risky assets. Three perceptions portfolios were constructed based on the proposed non-dimensional parameters along with fractional lion clustering algorithm. In order to demonstrate the proposed framework, an illustrative application is included in equity portfolio selection. The returns and risks of these perception based portfolios are compared to major Index funds for validating the efficiency and are found to overpower the Index funds with significant margins by maintaining the risk comparable to Index funds. Further, Markowitz based efficient frontier is plotted for better understanding of optimal returns and risk for perception based investment.

Bonactin and Feigrisolide C Inhibit Magnaporthe oryzae Triticum Fungus and Control Wheat Blast Disease.

Wheat blast caused by the Magnaporthe oryzaeTriticum (MoT) pathotype is one of the most damaging fungal diseases of wheat. During the screening of novel bioactive secondary metabolites, we observed two marine secondary metabolites, bonactin and feigrisolide C, extracted from the marine bacteria Streptomyces spp. (Act 8970 and ACT 7619), remarkably inhibited the hyphal growth of an MoT isolate BTJP 4 (5) in vitro. In a further study, we found that bonactin and feigrisolide C reduced the mycelial growth of this highly pathogenic isolate in a dose-dependent manner. Bonactin inhibited the mycelial development of BTJP 4 (5) more effectively than feigrisolide C, with minimal concentrations for inhibition being 0.005 and 0.025 µg/disk, respectively. In a potato dextrose agar (PDA) medium, these marine natural products greatly reduced conidia production in the mycelia. Further bioassays demonstrated that these secondary metabolites could inhibit the MoT conidia germination, triggered lysis, or conidia germinated with abnormally long branched germ tubes that formed atypical appressoria (low melanization) of BTJP 4 (5). Application of these natural products in a field experiment significantly protected wheat from blast disease and increased grain yield compared to the untreated control. As far as we are aware, this is the first report of bonactin and feigrisolide C that inhibited mycelial development, conidia production, conidial germination, and morphological modifications in the germinated conidia of an MoT isolate and suppressed wheat blast disease in vivo. To recommend these compounds as lead compounds or biopesticides for managing wheat blast, more research is needed with additional MoT isolates to identify their exact mode of action and efficacy of disease control in diverse field conditions.

Modeling a sustainable vaccine supply chain for a healthcare system.

This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network's economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics-namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods-are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.

A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition.

The current research aims to aid policymakers and healthcare service providers in estimating expected long-term costs of medical treatment, particularly for chronic conditions characterized by disease transition. The study comprised two phases (qualitative and quantitative), in which we developed linear optimization-based mathematical frameworks to ascertain the expected long-term treatment cost per patient considering the integration of various related dimensions such as the progression of the medical condition, the accuracy of medical treatment, treatment decisions at respective severity levels of the medical condition, and randomized/deterministic policies. At the qualitative research stage, we conducted the data collection and validation of various cogent hypotheses acting as inputs to the prescriptive modeling stage. We relied on data collected from 115 different cardio-vascular clinicians to understand the nuances of disease transition and related medical dimensions. The framework developed was implemented in the context of a multi-specialty hospital chain headquartered in the capital city of a state in Eastern India, the results of which have led to some interesting insights. For instance, at the prescriptive modeling stage, though one of our contributions related to the development of a novel medical decision-making framework, we illustrated that the randomized versus deterministic policy seemed more cost-competitive. We also identified that the expected treatment cost was most sensitive to variations in steady-state probability at the "major" as opposed to the "severe" stage of a medical condition, even though the steady-state probability of the "severe" state was less than that of the "major" state.

Marine Natural Product Antimycin A Suppresses Wheat Blast Disease Caused by Magnaporthe oryzae Triticum.

The application of chemical pesticides to protect agricultural crops from pests and diseases is discouraged due to their harmful effects on humans and the environment. Therefore, alternative approaches for crop protection through microbial or microbe-originated pesticides have been gaining momentum. Wheat blast is a destructive fungal disease caused by the Magnaporthe oryzae Triticum (MoT) pathotype, which poses a serious threat to global food security. Screening of secondary metabolites against MoT revealed that antimycin A isolated from a marine Streptomyces sp. had a significant inhibitory effect on mycelial growth in vitro. This study aimed to investigate the inhibitory effects of antimycin A on some critical life stages of MoT and evaluate the efficacy of wheat blast disease control using this natural product. A bioassay indicated that antimycin A suppressed mycelial growth (62.90%), conidiogenesis (100%), germination of conidia (42%), and the formation of appressoria in the germinated conidia (100%) of MoT at a 10 µg/mL concentration. Antimycin A suppressed MoT in a dose-dependent manner with a minimum inhibitory concentration of 0.005 µg/disk. If germinated, antimycin A induced abnormal germ tubes (4.8%) and suppressed the formation of appressoria. Interestingly, the application of antimycin A significantly suppressed wheat blast disease in both the seedling (100%) and heading stages (76.33%) of wheat at a 10 µg/mL concentration, supporting the results from in vitro study. This is the first report on the inhibition of mycelial growth, conidiogenesis, conidia germination, and detrimental morphological alterations in germinated conidia, and the suppression of wheat blast disease caused by a Triticum pathotype of M. Oryzae by antimycin A. Further study is required to unravel the precise mode of action of this promising natural compound for considering it as a biopesticide to combat wheat blast.

Natural Protein Kinase Inhibitors, Staurosporine, and Chelerythrine Suppress Wheat Blast Disease Caused by Magnaporthe oryzae Triticum.

Protein kinases (PKs), being key regulatory enzymes of a wide range of signaling pathways, are potential targets for antifungal agents. Wheat blast disease, caused by Magnaporthe oryzae Triticum (MoT), is an existential threat to world food security. During the screening process of natural metabolites against MoT fungus, we find that two protein kinase inhibitors, staurosporine and chelerythrine chloride, remarkably inhibit MoT hyphal growth. This study further investigates the effects of staurosporine and chelerythrine chloride on MoT hyphal growth, conidia production, and development as well as wheat blast inhibition in comparison to a commercial fungicide, Nativo®75WG. The growth of MoT mycelia is significantly inhibited by these compounds in a dose-dependent manner. These natural compounds greatly reduce conidia production in MoT mycelia along with suppression of conidial germination and triggered lysis, resulting in deformed germ tubes and appressoria. These metabolites greatly suppress blast development in artificially inoculated wheat plants in the field. This is the first report of the antagonistic effect of these two natural PKC inhibitory alkaloids on MoT fungal developmental processes in vitro and suppression of wheat blast disease on both leaves and spikes in vivo. Further research is needed to identify their precise mechanism of action to consider them as biopesticides or lead compounds for controlling wheat blast.

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item.

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

Evaluating factors contributing to the failure of information system in the banking industry.

The increasing use of Information Technology (IT) has led to many security and other related failures in the banks and other financial institutions in Bangladesh. In this paper, we investigated the factors contributing to the failurein the IT system of the banking industry in Bangladesh. Based on the experts' opinions and weight on the specified evaluating criteria, an empirical test was conducted using a rough set theory to produce a framework for the IT system failure factors. In this study, an extended approach involving the integration of rough set theory based flexible Failure Mode and Effect Analysis (FMEA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has beenapplied to help the managers of the corresponding field to identify the factors responsible for the failure of the IT system in the banking industries and then prioritize them accordingly, for the ease of decision-making.In this research, eleven such failure factors were identified, which were then quantitatively analyzed to facilitate managers in crucial decision-making. It was observed that cyber-attack, database hack risks, server failure, network interruption, broadcast data error, and virus effect were the most significant factors for the failure of the IT system. The framework developed in this research can be utilized to assist in efficient decision-makingin other serviceindustries where IT systems play a key role. To the best of the knowledge, this is the first study thatempirically tested key failure factors of the IT system for the banking sector using an integrated method.

Modeling the blockchain readiness challenges for product recovery system.

Product Recovery System (PRS) transfers products from their typical final place to their source to arrest some value on the product. There are obstructions, such as costs, associated with the modification of accounts and assessment of products and refunds associated with the implementation of PRS. Blockchain Technology (BCT) emerged as an innovative approach to constructing trust in a trust less environment and assures the availability, traceability, and security in data management. It also presents a valuable solution to PRS. This study aims to analyze the Blockchain Readiness Challenges (BRCs) to PRS in the context of manufacturing industries. The study observes 20 readiness challenges linked with the implementation of BCT in PRS. The BRCs are identified from the literature survey and confirmed after consequent examinations with industry experts and researchers. The study employed a Multi-Criteria Decision-Making (MCDM) i.e., the Decision-Making Trial And Evaluation Laboratory (Fuzzy DEMATEL) approach to find the cause-and-effect interactions to prioritize BRCs. The Maximum Mean De-Entropy (MMDE) algorithm was adopted to establish the threshold value based on the information entropy of the interactions among the BRCs for PRS. The fuzzy set theory was adopted to tackle the uncertainty and vagueness of personnel biases and data deficiency problems. The findings from this study reveal that inadequate financing for PRS exercises, lack of governance and standards, and security challenges to BCT implementation are the most influential readiness challenges for the adoption of blockchain in PRS. The study is useful to manufacturing organizations for identifying the potential BRCs to implement PRS among all existing readiness challenges so that they can take suitable measures before proceeding to adopt blockchain in PRS. The managers are suggested to eliminate the readiness challenges and widen the blockchain technology adoption in PRS.

An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability.

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier's reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

A combined approach for modeling multi-echelon multi-period decentralized supply chain.

In this paper, a multi-echelon, multi-period, decentralized supply chain (SC) with a single manufacturer, single distributor and single retailer is considered. For this setting, a two-phase planning approach combining centralized and decentralized decision-making processes is proposed, in which the first-phase planning is a coordinated centralized controlled, and the second-phase planning is viewed as independent decentralized decision-making for individual entities. This research focuses on the independence and equally powerful behavior of the individual entities with the aim of achieving the maximum profit for each stage. A mathematical model for total SC coordination as a first-phase planning problem and separate ones for each of the independent members with their individual objectives and constraints as second-phase planning problems are developed. We introduce a new solution approach using a goal programming technique in which a target or goal value is set for each independent decision problem to ensure that it obtains a near value for its individual optimum profit, with a numerical analysis presented to explain the results. Moreover, the proposed two-phase model is compared with a single-phase approach in which all stages are considered dependent on each other as parts of a centralized SC. The results prove that the combined two-phase planning method for a decentralized SC network is more realistic and effective than a traditional single-phase one.

Managing panic buying-related instabilities in supply chains: A COVID-19 pandemic perspective.

Global supply chains (SCs) have been severely impacted by the COVID-19 pandemic on several levels. For example, SCs suffered from panic buying-related instabilities and multiple disruptions of supply, demand, and capacity during the pandemic. This study developed an agent-based model (ABM) to predict the effects of panic buying-related instabilities in SCs and offered strategies to improve them. The ABM model includes a simulation and optimization model of a typical SC of an essential product manufacturer (i.e., toilet paper SC) for the analysis of scenarios and strategies to observe improvements in SCs. Among the four strategies identified, the findings suggest boosting production capacity to the maximum and ensuring optimal reorder points, order sizes, and trucks helped the essential product manufacturers reduce panic buying-related instabilities in their SCs.

Supply chain recovery challenges in the wake of COVID-19 pandemic.

The COVID-19 pandemic has revealed the fragility of global supply chains arising from raw material scarcity, production and transportation disruption, and social distancing. Firms need to carefully anticipate the difficulties during recovery and formulate appropriate strategies to ensure the survival of their businesses and supply chains. To enhance awareness of the issues, this research aims to identify and model recovery challenges in the context of the Bangladeshi ready-made garment industry. A Delphi-based grey decision-making trial and evaluation laboratory (DEMATEL) methodology was used to analyze the data. While the Delphi method helped identify the major supply chain recovery challenges from the impacts of the COVID-19 pandemic, the grey DEMATEL approach helped categorize the causal relationships among these challenges. Of the 23 recovery challenges finalized, 12 are causal challenges. The study's findings can assist decision-makers in developing strategic policies to overcome the recovery challenges in the post-COVID-19 era.

COVID-19 pandemic related supply chain studies: A systematic review.

The global spread of the novel coronavirus, also known as the COVID-19 pandemic, has had a devastating impact on supply chains. Since the pandemic started, scholars have been researching and publishing their studies on the various supply-chain-related issues raised by COVID-19. However, while the number of articles on this subject has been steadily increasing, due to the absence of any systematic literature reviews, it remains unclear what aspects of this disruption have already been studied and what aspects still need to be investigated. The present study systematically reviews existing research on the COVID-19 pandemic in supply chain disciplines. Through a rigorous and systematic search, we identify 74 relevant articles published on or before 28 September 2020. The synthesis of the findings reveals that four broad themes recur in the published work: namely, impacts of the COVID-19 pandemic, resilience strategies for managing impacts and recovery, the role of technology in implementing resilience strategies, and supply chain sustainability in the light of the pandemic. Alongside the synthesis of the findings, this study describes the methodologies, context, and theories used in each piece of research. Our analysis reveals that there is a lack of empirically designed and theoretically grounded studies in this area; hence, the generalizability of the findings, thus far, is limited. Moreover, the analysis reveals that most studies have focused on supply chains for high-demand essential goods and healthcare products, while low-demand items and SMEs have been largely ignored. We also review the literature on prior epidemic outbreaks and other disruptions in supply chain disciplines. By considering the findings of these articles alongside research on the COVID-19 pandemic, this study offers research questions and directions for further investigation. These directions can guide scholars in designing and conducting impactful research in the field.

An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic.

The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers' skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand-supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19.

First report of basal rot of dragon fruit caused by Fusarium oxysporum in Bangladesh.

Dragon fruit (Hylocereus polyrhizus) is a high value newly introduced fruit crop in Bangladesh. It has drawn considerable public attention due to its appealing flesh color, sweet taste and fruit qualities. Recently, basal rot of dragon fruit plants was observed in several farmer's fields, nurseries and in the research field of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU) where about 10-15% of plants were infected in each location. Initially, the symptoms appeared in the basal part near the soil as brown lesions which gradually extended to the upper stem and finally becoming soft and watery (Figure 1a). Infected plants were collected from Kapasia of Gazipur district (Latitude 24.266 and Longitude 90.633) to isolate the causal organism. Isolations were carried out following the procedure reported by Briste et al. (2019). Briefly, infected plant parts were surface sterilized in 2% NaOCl for 1 min followed by 70% ethanol for 5 min and rinsed 3 times with sterile double distilled water. A large piece of a surface sterilized plant was cut into small pieces (2 mm × 2 mm) from the margin of the necrotic lesion and placed on half strength potato dextrose agar (PDA) and incubated for 7 days at 25 °C. The BTFD1 and BTFD4 isolates were purified from single spores resulting in white colonies with a growth rate of 1cm/day on PDA (Figure 1b). Colonies produced single celled microconidia from unbranched, short monophialidic conidiophores and septate macroconidia as well as chlamydospores in PDA which is consistent with Fusarium oxysporum (Figure 1c). To confirm the identity of the isolates, the internal transcribed spacer (ITS1, 5.8S rRNA and ITS2) and translation elongation factor-1alpha (EF-1α) were amplified using primers ITS-1/ ITS-4 and EF1-728F/ EF1-986R, respectively (Surovy et al. 2018). The ITS sequences of the isolates BTFD1 and BTFD4 (GenBank accession # MN727096 and MN727095, respectively) showed 100% similarity with the sequence from F. oxysporum strain JJF2 (MN626452). Sequence identity for EF-1α (GenBank accession # MN752123 and MN752124, respectively) was 100% with the sequence from F. oxysporum strain CAV041_EO (MK783088). The isolates (BTFD1 and BTFD4) were identified as F. oxysporum based on the aligned sequences of ITS and EF-1α, molecular phylogenetic analyses by maximum likelihood tree (Figure 2a) and maximum parsimony tree methods (Figure 2b). The isolates were stored at 4°C on dried filter paper as well as in an ultra-low temperature freezer (-80°C) at IBGE, BSMRAU, Bangladesh and are available on request. To ensure pathogenicity, isolate BTFD1 was grown on PDA, incubated at 25°C for 7 days and 250 ml conidial suspension (with 1 × 105 conidia/ml) was prepared. Twelve,three-month-old healthy dragon fruit plants were inoculated. Pathogenicity tests were carried out in two sets using three replications in each set. In one set, only the basal part of the plants was dipped into the conidial suspension and in another set the whole plant was dipped into the conidial suspension for two hours. Sterile distilled water was also used in another set of plants as a control. The inoculated plants were placed on wet tissue in a plastic box (31cm × 24cm × 8cm) covered and incubated at 25°C. After 10 days, all inoculated plants in both sets developed rot symptoms similar to those observed in the field, while the control plants remained healthy (Figure 1d). The pathogen was successfully re-isolated from the inoculated symptomatic parts on half strength PDA medium and had morphology as characterized before, thus fulfilling Koch's postulates. This disease has been reported in Argentina and Malaysia (Wright et al. 2007; Hafifi et al. 2019). To the bet of our knowledge, this is the first report of Fusarium basal rot of dragon fruit in Bangladesh caused by F. oxysporum.