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Background: Borneo, the third largest island in the world, is facing a significant burden of emerging and re-emerging vector-borne diseases due to rapid changes in primary tropical rainforests and urban landscapes. These vector-borne diseases include the endemic epidemic cycles that occur in the more populated and urbanized areas, as well as the possible transmission through enzootic and sylvatic transmission cycles that occur mainly in the overlapping landscapes or among the indigenous population in the forest. The island will be changed significantly in the future due to the increase in human activities, especially mega events such as the relocation of the Indonesian capital to Nusantara in East Kalimantan Borneo, increasing urbanization, agriculture, hydropower projects, ecotourism activities in Sabah, North Borneo, and Sarawak, Central and South Borneo. Therefore, an overview of the current situation of vector-borne diseases is crucial for the next possible epidemic preparedness., Methods: This study conducted the PRISMA-ScR scoping review and formulated a set of research questions to identify current trends in vector-borne diseases in Borneo. These questions aim to identify which diseases have been studied, what geographical areas have been covered by the research, how the One Health approach-encompassing human, animal and environmental factors-is integrated, and what gaps and challenges exist in addressing these diseases., Results: A total of 2241 references were screened for eligibility and 117 articles were selected for review. The majority of the materials focused on mosquitoes and malaria, and the One Health elements focused mainly on humans., Conclusions: This review has identified the most and least studied vector-borne diseases and highlighted some of the gaps in knowledge and research on vector-borne diseases on the island of Borneo. Future studies should particularly focus on other neglected diseases such as Zika, chikungunya, Japanese encephalitis, filariasis and tick-borne diseases. In addition, advanced surveillance systems will be developed to improve early detection and response specifically for remote regions where vector-borne diseases are endemic or emerging., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: The use of computer vision and deep learning models to automatically classify insect species on sticky traps has proven to be a cost- and time-efficient approach to pest monitoring. As different species are attracted to different colours, the variety of sticky trap colours poses a challenge to the performance of the models. However, the effectiveness of deep learning in classifying pests on different coloured sticky traps has not yet been sufficiently explored. In this study, we aim to investigate the influence of sticky trap colour and imaging devices on the performance of deep learning models in classifying pests on sticky traps., Results: Our results show that using the MobileNetV2 architecture with transparent sticky traps as training data, the model predicted the pest species on transparent sticky traps with an accuracy of at least 0.95 and on other sticky trap colours with at least 0.85 of the F1 score. Using a generalised linear model (GLM) and a Boruta feature selection algorithm, we also showed that the colour and architecture of the sticky traps significantly influenced the performance of the model., Conclusion: Our results support the development of an automatic classification of pests on a sticky trap, which should focus on colour and deep learning architecture to achieve good results. Future studies could aim to incorporate the trap system into pest monitoring, providing more accurate and cost-effective results in a pest management programme. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Studying differences in transcriptomes across various development stages of insects is necessary to uncover the physiological and molecular mechanism underlying development and metamorphosis. We here present the first transcriptome data generated under Illumina Hiseq platform concerning Zeugodacus tau (Walker) larvae from Nanchang, China. In total, 11,702 genes were identified from 9 transcriptome libraries of three development stages of Z. tau larvae. 7219 differentially expressed genes (DEGs) were screened out from the comparisons between each two development stages of Z. tau larvae, and their roles in development and metabolism were analyzed. Comparative analyses of transcriptome data showed that there are 5333 DEGs between 1-day and 7-day old larvae, consisting of 1609 up-regulated and 3724 down-regulated genes. Expressions of DEGs were more abundant in L7 than in L1 and L3, which might be associated with metamorphosis. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested the enrichment of metabolic process. KOG annotation further confirmed that 20-hydroxyecdysone (20E) pathway related genes Cyp4ac1_1, Cyp4aa1, Cyp313a4_3 were critical for the biosynthesis, transport, and catabolism of secondary metabolites and lipid transport and metabolism. Expression patterns of 8 DEGs were verified using quantitative real-time PCR (RT-qPCR). This study elucidated the DEGs and their roles underlying three development stages of Z. tau larvae, which provided valuable information for further functional genomic research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The sticky trap is probably the most cost-effective tool for catching insect pests, but the identification and counting of insects on sticky traps is very labour-intensive. When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model - the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. As far as we know, there are no available image datasets to study these two aspects in computer vision and deep learning algorithms. Therefore, this paper presents a new dataset consisting of images of two pests commonly found in post-harvest crops - the red flour beetle ( Tribolium castaneum ) and the rice weevil ( Sitophilus oryzae ) - captured with three different devices (DSLR, webcam and smartphone) on blue, yellow, white and transparent sticky traps. The images were sorted by device, colour and species and divided into training, validation and test parts for the development of the deep learning model., (© 2024 The Authors.)

Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confirmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, offering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures., (© 2024. The Author(s).)

Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic identification require well-trained personnel, are time-consuming and labour-intensive. In this study, we aim to use a deep leaning model (DL) to develop an application capable of automatically detecting mosquito vectors collected from urban and suburban areas in North Borneo, Malaysia. Specifically, a DL model called MobileNetV2 was developed using a total of 4880 images of Aedes aegypti , Aedes albopictus and Culex quinquefasciatus mosquitoes, which are widely distributed in Malaysia. More importantly, the model was deployed as an application that can be used in the field. The model was fine-tuned with hyperparameters of learning rate 0.0001, 0.0005, 0.001, 0.01 and the performance of the model was tested for accuracy, precision, recall and F1 score. Inference time was also considered during development to assess the feasibility of the model as an app in the real world. The model showed an accuracy of at least 97%, a precision of 96% and a recall of 97% on the test set. When used as an app in the field to detect mosquitoes with the elements of different background environments, the model was able to achieve an accuracy of 76% with an inference time of 47.33 ms. Our result demonstrates the practicality of computer vision and DL in the real world of vector and pest surveillance programmes. In the future, more image data and robust DL architecture can be explored to improve the prediction result.

Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V.)

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system., (© 2023. The Author(s).)

Natural history museum collections are the most important sources of information on the present and past biodiversity of our planet. Most of the information is primarily stored in analogue form, and digitization of the collections can provide further open access to the images and specimen data to address the many global challenges. However, many museums do not digitize their collections because of constraints on budgets, human resources, and technologies. To encourage the digitization process, we present a guideline that offers low-cost and technical knowledge solutions yet balances the quality of the work and outcomes. The guideline describes three phases of digitization, namely preproduction, production, and postproduction. The preproduction phase includes human resource planning and selecting the highest priority collections for digitization. In the preproduction phase, a worksheet is provided for the digitizer to document the metadata, as well as a list of equipment needed to set up a digitizer station to image the specimens and associated labels. In the production phase, we place special emphasis on the light and color calibrations, as well as the guidelines for ISO/shutter speed/aperture to ensure a satisfactory quality of the digitized output. Once the specimen and labels have been imaged in the production phase, we demonstrate an end-to-end pipeline that uses optical character recognition (OCR) to transfer the physical text on the labels into a digital form and document it in a worksheet cell. A nationwide capacity workshop is then conducted to impart the guideline, and pre- and postcourse surveys were conducted to assess the confidence and skills acquired by the participants. This paper also discusses the challenges and future work that need to be taken forward for proper digital biodiversity data management., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Background: Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19., Methods: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy., Results: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively., Conclusion: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions., (© 2023. The Author(s).)

Background: We describe the epidemiology, clinical characteristics, and outcomes of multisystem inflammatory syndrome in children (MIS-C) among children from Negeri Sembilan, Malaysia., Methods: A retrospective, multicentre, observational study was performed among children ≤15 years old who were hospitalized for MIS-C between January 18, 2021 and June 30, 2023. The incidence of MIS-C was estimated using reported SARS-CoV-2 cases and census population data. Descriptive analyses were used to summarize the clinical presentation and outcomes., Results: The study included 53 patients with a median age of 5.7 years (IQR 1.8-8.7 years); 75.5% were males. The overall incidence of MIS-C was approximately 5.9 cases per 1,000,000 person-months. Pediatric intensive care unit (PICU) admission was required for 22 (41.5%) patients. No mortalities were recorded. Children aged 6-12 years were more likely to present with cardiac dysfunction/shock (odds ratio [OR] 5.43, 95% confidence interval [CI] 1.67-17.66), whereas children below 6 years were more likely to present with a Kawasaki disease phenotype (OR 5.50, 95% CI 1.33-22.75). Twenty patients (37.7%) presented with involvement of at least four organ systems, but four patients (7.5%) demonstrated single-organ system involvement., Conclusion: An age-based variation in the clinical presentation of MIS-C was demonstrated. Our findings suggest MIS-C could manifest in a spectrum, including single-organ involvement. Despite the high requirement for PICU admission, the prognosis of MIS-C was favorable, with no recorded mortalities., (© 2023 Japan Pediatric Society.)

Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Ong, Hamid. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge. Transfer learning is a type of machine learning that is viable and durable in image classification with limited training images. This protocol aims to develop step-by-step procedure in developing a classification system with transfer learning algorithm for mosquito, we demonstrate the protocol to classify two species of Aedes mosquito - Aedes aegypti L. and Aedes albopitus L, but user can adopt the protocol for higher number of species classification. We demonstrated the way of start from the scratch, fine-tuning two pre-trained model performance by using different combination of hyperparameters - batch size and learning rate, and explain the terminology in the Appendix. This protocol target on the domain expert such as entomologist and public health practices to develop their own model to solve the task of mosquito/insect classification., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Background: Public community engagement is crucial for mosquito surveillance programs. To support community participation, one of the approaches is assisting the public in recognizing the mosquitoes that carry pathogens. Therefore, this study aims to build an automatic recognition system to identify mosquitos at the public community level. We construct a customized image dataset consisting of three mosquito species in either damaged or un-damaged body conditions. To distinguish the mosquito in harsh conditions, we explore two state-of-the-art deep learning (DL) architectures: (i) a freezing convolutional base, with partial trainable weights, and (ii) training the entire model with most of the trainable weights. We project a weighted feature map on different layers of the model to visualize the morphological region used by the model in classification and compared it with the morphological key used by the expert., Result: It was found that the model with architecture two and the Adam optimizer achieves at least 98% accuracy in mosquito and conditions identification and when implemented on an independent dataset, the Xception model generalizes the best result with an accuracy of 0.7775 and 0.795 precision. Moreover, most of the morphological regions used by the model are able to match those of the human expert., Conclusion: We report a customized DL model for performing pest mosquito taxonomy identification, and through visualization, some regions using computers to discriminate mosquito species could be adopted later in systematic identification. © 2022 Society of Chemical Industry., (© 2022 Society of Chemical Industry.)

Conventional methods to study insect taxonomy especially forensic and medical dipterous flies are often tedious, time-consuming, labor-intensive, and expensive. An automated recognition system with image processing and computer vision provides an excellent solution to assist the process of insect identification. However, to the best of our knowledge, an image dataset that describes these dipterous flies is not available. Therefore, this paper introduces a new image dataset that is suitable for training and evaluation of a recognition system involved in identifying the forensic and medical importance of dipterous flies. The dataset consists of a total of 2876 images, in the input dimension (224 × 224 pixels) or as an embedded image model (96 × 96 pixels) for microcontrollers. There are three families (Calliphoridae, Sarcophagidae, Rhiniidae) and five genera (Chrysomya, Lucilia, Sarcophaga, Rhiniinae, Stomorhina), and each class of genus contained five different variants (same species) of fly to cover the variation of a species., (© 2022. The Author(s).)

This paper introduces a new mosquito images dataset that is suitable for training and evaluating a recognition system on mosquitoes in normal or smashed conditions. The images dataset served mainly for the development a machine learning model that can recognize the mosquito in the public community, which commonly found in the smashed/damaged form by human. Especially the images of mosquito in hashed condition, which to the best of our knowledge, a dataset that fulfilled such condition is not available. There are three mosquito species in the dataset, which are Aedes aegypti, Aedes albopictus and Culex quinquefasciatus, and the images were annotated until species level due to the specimen was purely bred in a WHO accredited breeding laboratory. The dataset consists of seven root files, six root files that composed of six classes (each species with either normal landing, or random damaged conditions) with a total of 1500 images, and one pre-processed file which consists of a train, test and prediction set, respectively for model construction., (© 2022. The Author(s).)

Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.g., disease-related knowledge discovery, disease surveillance, early detection system, etc. However, a recent review of text mining in the domain of mosquito-borne disease was not available to the best of our knowledge. In this review, we survey the recent works in the text mining techniques used in combating mosquito-borne diseases. We highlight the corpus sources, technologies, applications, and the challenges faced by the studies, followed by the possible future directions that can be taken further in this domain. We present a bibliometric analysis of the 294 scientific articles that have been published in Scopus and PubMed in the domain of text mining in mosquito-borne diseases, from the year 2016 to 2021. The papers were further filtered and reviewed based on the techniques used to analyze the text related to mosquito-borne diseases. Based on the corpus of 158 selected articles, we found 27 of the articles were relevant and used text mining in mosquito-borne diseases. These articles covered the majority of Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with extremely low numbers or none of the other crucial mosquito-borne diseases like chikungunya, yellow fever, West Nile fever. Twitter was the dominant corpus resource to perform text mining in mosquito-borne diseases, followed by PubMed and LexisNexis databases. Sentiment analysis was the most popular technique of text mining to understand the discourse of the disease and followed by information extraction, which dependency relation and co-occurrence-based approach to extract relations and events. Surveillance was the main usage of most of the reviewed studies and followed by treatment, which focused on the drug-disease or symptom-disease association. The advance in text mining could improve the management of mosquito-borne diseases. However, the technique and application posed many limitations and challenges, including biases like user authentication and language, real-world implementation, etc. We discussed the future direction which can be useful to expand this area and domain. This review paper contributes mainly as a library for text mining in mosquito-borne diseases and could further explore the system for other neglected diseases., (Copyright © 2022. Published by Elsevier B.V.)

Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of "az", "pfizer", "sinovac", and "mix" for determinants' assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and "pfizer" and "mix" were the strongest determinants of the tweet's sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media.

Background: The application of computer vision and deep learning to pest monitoring has recently received much attention. Although several studies have demonstrated the application of object detection to the number of pests on a substrate, for house flies (Musca domestica L.), in which the larvae were aggregated and overlapped together, the object detection technique was difficult to implement. We demonstrate a novel method for estimating larval abundance by using computer vision on larval breeding substrate, in which the reflective color and topography are affected by the size of the population., Results: We demonstrate a method using a web-based tool to construct a deep learning model and later export the model for deployment. We train the model by using breeding substrate images with different spectra of illumination on known densities of larvae and evaluate the training model in both the test set and field-collected samples. In general, the model was able to predict the larval abundance by the laboratory-prepared breeding substrate with 87.56% to 94.10% accuracy, precision, recall, and F-score on the unseen test set, and white and green illumination performed significantly higher compared to other illuminations. For field samples, the model was able to obtain at least 70% correct predictions by using white and infrared illumination., Conclusion: Larval abundance can be monitored with computer vision and deep learning, and the monitoring can be improved by using more biochemistry parameters as the predictors and examples of field samples included building a more robust model. © 2021 Society of Chemical Industry., (© 2021 Society of Chemical Industry.)

Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

Megaselia scalaris (Loew) is one of the best-known diets for the swiftlet. Previous studies have addressed the problem of some mass rearing conditions for this insect; unfortunately, the details of the nutritional composition of the life stages and cost of the breeding materials were insufficiently reported, even though this information is crucial for farming the edible-nest swiftlet. We aimed to investigate the nutritional composition of the life stages of M scalaris on a cost basis using 3 common commercial breeding materials: chicken pellets (CPs), fish pellets (FPs), and mouse pellets (MPs). Modified Association of Official Analytical Chemists (AOAC) proximate and mineral analyses were carried out on the insect's third instar larvae, pupal, and adult stages to determine the nutritional composition. Regardless of the breeding materials, the adult stage of M scalaris had significantly higher crude protein than the other stages; the pupae were rich in calcium, which is required for egg production; and the third instar larvae had the highest amount of crude fat compared with the other stages. Regarding the energy content, there were no significant differences among the stages according to the breeding materials. In terms of nutritional cost, CP was the most economic breeding material and generated the highest amount of nutrients per US dollar (US $). Different life stages of M scalaris were used by the swiftlets by supplying the required nutrients, and future studies should focus on effective diet feeding methods., Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Megaselia scalaris (Loew) (Diptera: Phoridae) provides great evidential value in estimating the postmortem interval (PMI) compared with other dipterans due to its common occurrence on human corpses both indoors and in concealed environments. Studies have focused on the effect of temperature, larval diet, and photoperiod on the development of the species; however, knowledge of M. scalaris development at different moisture levels is insufficient. This study aimed to investigate the effects of substrate moisture on the larval development time, pupal recovery, pupal weight, adult emergence, and adult head width of M. scalaris. The larvae were reared in five replicates on substrates with six moisture levels ranging from 50 to 90%. Larvae and puparia were sampled daily, and the collection time, number, and weight were recorded, measured, and then compared using multivariate analysis of variance with a post hoc least significant difference test. Larvae developed most quickly (3.75 ± 0.04 d) at 50% substrate moisture; the larvae were able to survive in extremely wet substrates (90% moisture), but the development time was significantly longer (6.48 ± 0.19 d). Moisture greatly influenced the pupation rate and adult emergence but showed a weak effect on the pupae weight and adult head width. Due to the significance of moisture on the development of M. scalaris, PMI estimation using M. scalaris with cadavers of different moisture content must be carefully conducted to avoid inaccuracy.

Larval age and nutrition significantly affected the insect's physiology. These influences are important when rearing a population of vectors that is used to monitor the resistance level, in which standardized conditions are crucial for a more harmonized result. Little information has been reported on the effects of larval age and nutrition on the susceptibility of insects to insecticides, and therefore, we studied the effects on the susceptibility of Culex quinquefasciatus Say's (Diptera: Culicidae) larvae to temephos by comparing the median lethal concentration (LC50) after 24 hr between the second and fourth instar larvae and between the larvae that fed on protein-based and carbohydrate-based larval diets. The susceptibility of the larvae was significantly affected by the larval diets, as the larvae that fed on protein-based beef food and milk food demonstrated significantly higher LC50 value compared with the larvae that fed on carbohydrate-based food: lab food and yeast food. The larval diet interacted significantly with the larval age: while the second instar larvae were susceptible to temephos when supplied with carbohydrate-based food, the second and fourth instar larvae had no significant effect when supplied with protein-based diets, implying that a protein-rich environment may cause the mosquito to be less susceptible to temephos. This study suggested the importance of standardizing nutrition when rearing a vector population in order to obtain more harmonized dosage-response results in an insecticide resistance monitoring program. Future research could focus on the biochemical mechanism between the nutrition and the enzymatic activities of the vector.

The potential of integrating the mycoinsecticide, Metarhizium anisopliae (Met.), into house fly control programs is tremendous. However, the interaction between the fungus and insecticide, when applied at poultry farms, remains poorly understood. This study investigated the interaction between M. anisopliae and two selected insecticides, cyromazine and ChCy (a mixture of chlorpyrifos and cypemethrin), with three objectives: to assess the compatibility of M. anisopliae and the insecticides by measuring fungal vegetative growth and conidia production in the presence of insecticides; to evaluate the effect of M. anisopliae on these insecticides by analyzing insecticidal residue using ultra performance liquid chromatography; and to study the synergistic effects of M. anisopliae and the insecticides by applying sublethal concentrations of insecticides with M. anisopliae to house fly larvae. Metarhizium anisopliae was more tolerant to ChCy than to cyromazine, as M. anisopliae showed significantly more growth when grown with this insecticide. The M. anisopliae + ChCy combination resulted in significantly less chlorpyrifos residues compared to the ChCy plate, and 62-72% house fly larva mortality occurred when M. anisopliae and sublethal concentrations of ChCy were combined, implicating synergistic effects of the fungus with low concentrations of ChCy. Integrating M. anisopliae with compatible chemical at right concentration is crucial for poultry farm house fly control programs., (© The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

In this study, bifenthrin (Maxxthor SC, Ensystex Australasia Pty Ltd), imidacloprid (Prothor SC, Ensystex Australasia Pty Ltd) and fipronil (Regent ® 50SC, Bayer) were applied on the natural infest manures according to the manufacturer rate during a broiler breeding cycle. Solvent direct-immersion extraction (SDIE) was used in detecting the target compound and later, quantification of the insecticide residues in field condition was investigated. The samples were prior cleaned up by solid-phase extraction (SPE) and analysed by Ultra-Performance Liquid Chromatography (UPLC) - photodiode array (PDA) system. In the field trial, three insecticides were showed accumulation during the broiler breeding period and it is suggested that they acted as adulticides when applied on the poultry manures, this is supported by the significant correlation between the increment of insecticide residues to the reduction percentage of adult flies (<0.05). Fipronil showed significantly greater reduction on the adult fly compared to the other insecticides, in which the reduction rate compared to control population at the end of the broiler breeding period; fipronil, imidaclopril and bifenthrin reduced 51.51%, 28.30% and 30.84% of adult flies, respectively.

Determining the control threshold for a pest is common prior to initiating a pest control program; however, previous studies related to the house fly control threshold for a poultry farm are insufficient for determining such a threshold. This study aimed to predict the population changes of house fly population by comparing the intrinsic rate of increase (r m ) for different house fly densities in a simulated system. This study first defined the knee points of a known population growth curve as a control threshold by comparing the r m of five densities of house flies in a simulated condition. Later, to understand the interactions between the larval and adult populations, the correlation between larval and adult capacity rate (r c ) was studied. The r m values of 300- and 500-fly densities were significantly higher compared with the r m values at densities of 50 and 100 flies. This result indicated their representative indices as candidates for a control threshold. The r c of larval and adult populations were negatively correlated with densities of fewer than 300 flies; this implicated adult populations with fewer than 300 flies as declining while the larval population was growing; therefore, control approaches should focus on the immature stages. The results in the present study suggest a control threshold for house fly populations. Future works should focus on calibrating the threshold indices in field conditions., (© The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

It is crucial to understand the degradation pattern of insecticides when designing a sustainable control program for the house fly, Musca domestica (L.), on poultry farms. The aim of this study was to determine the half-life and degradation rates of cyromazine, chlorpyrifos, and cypermethrin by spiking these insecticides into poultry manure, and then quantitatively analyzing the insecticide residue using ultra-performance liquid chromatography. The insecticides were later tested in the field in order to study the appropriate insecticidal treatment intervals. Bio-assays on manure samples were later tested at 3, 7, 10, and 15 d for bio-efficacy on susceptible house fly larvae. Degradation analysis demonstrated that cyromazine has the shortest half-life (3.01 d) compared with chlorpyrifos (4.36 d) and cypermethrin (3.75 d). Cyromazine also had a significantly greater degradation rate compared with chlorpyrifos and cypermethrin. For the field insecticidal treatment interval study, 10 d was the interval that had been determined for cyromazine due to its significantly lower residue; for ChCy (a mixture of chlorpyrifos and cypermethrin), the suggested interval was 7 d. Future work should focus on the effects of insecticide metabolites on targeted pests and the poultry manure environment.

In this study, the toxicology of two commercial larvicides--cyromazine (Neporex 50SP) and ChCy (combination of chlorpyrifos and cypermethrin, Naga 505)--and five commercial adulticides--thiamethoxam (Agita 10WG), cyfluthrin (Responsar WP), lambda-cyhalothrin (Icon 2.8EC), fipronil (Regent 50SC), and imidacloprid (Toxilat 10WP)--was examined against the WHO/VCRU (World Health Organization/ Vector Control Research Unit) susceptible strain and the AYTW (Ayer Tawar) field strain of house fly, Musca domestica L. These pesticides were administered topically, in the diet, or as a dry residue treatment on plywood. Probit analysis using at least five concentrations and the concentration that was lethal to 50% (LC(50)) of the organisms was applied to compare the toxicology and resistance levels of the AYTW population to different insecticides. In the larvicide laboratory study, ChCy was more effective than cyromazine, with a significantly lower LC(50) value when administered topically or in the diet, although the AYTW population was susceptible to both larvicides with a resistance ratio (RR) <10. For the adulticide laboratory study, cyfluthrin and fipronil exhibited the lowest LC50 values of the adulticides, indicating that they are both effective at controlling adult flies, although lambda-cyhalothrin showed moderate resistance (RR = 11.60 by topical application; 12.41 by plywood treatment). Further investigation of ChCy, cyromazine, cyfluthrin, and fipronil under field conditions confirmed that ChCy and cyromazine strikingly reduced larval density, and surprisingly, ChCy also exhibited adulticidal activity, which significantly reduced adult fly numbers compared with the control group. Cyfluthrin and fipronil were also confirmed to be effective, with a significant reduction in adult fly numbers compared with the control group., (© The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: The trend in chemical insecticide development has focused on improving the efficacy against mosquitoes while reducing the environmental impact. Lethal lures apply an "attract-and-kill" strategy that draws the insect to the killing agent rather than bringing the killing agent to the insect., Methods: In this study, the mosquito oviposition pheromone was extracted from the eggs of Aedes aegypti (L.) and further investigated with a combination of pheromone and granular temephos as a lethal lure., Results: The compound caproic acid attracted significantly more egg-laying mosquitos at 1 ppm (660.83 ± 91.61) than the control (343.83 ± 56.24), which consisted of solvent only (Oviposition Activity Index: 0.316). Further investigation of the combination of caproic acid with granular temephos as a lethal lure attracted significantly more gravid female Ae. aegypti to oviposit their eggs than the temephos treated water and control., Conclusions: This indicated the ability of caproic acid in acting as an attractant and counters the repellency effect of temephos. Additionally, the presence of temephos in the lethal lure also restricted the hatching of the eggs and killed any larvae that hatched.