RESEARCH CAPSULE PROPOSAL- AEDES AEGYPTY.pptx

Capsule Proposal

1. CAPSULE PROPOSAL A Concept for Characterization of Aedes Aegypti Egg Using Near Infrared Spectroscopy: A Novel Approach

2. INTRODUCTION Global resurgence Key vectors for dengue transmission Dengue Fever Situation in the Philippines Statistics and trends 72,333 dengue cases from January to June 17, 2023, a 14 percent increase from 2022 Regional variations 9,916 new cases between Jun. 25 and Jul. 8 of 2023 Region 4-B ( Mimaropa ) reports the highest incidence, with 51 cases per 100,000 population, followed by: Cordillera Administrative Region Davao Region Zamboanga Peninsula Soccsksargen in the same period. Importance of Controlling Aedes Aegypti Mosquitoes Challenges and current methods Computer Vision lighting conditions, egg size, shape, and texture impact the method’s accuracy Introduction of a Novel Approach Near-Infrared Spectroscopy (NIRS) Encoding of Aedes Aegypti egg’s spectral signature

3. SIGNIFICANCE This study's significance lies in its potential to revolutionize mosquito surveillance and control efforts. Introducing Near-Infrared Spectroscopy (NIRS) aims to develop a more efficient and accurate method for monitoring Aedes Aegypti populations, which could lead to more effective disease control. Specific Objectives: To Conceptualize the Development and Optimization of a Rapid Collection and Classification Method for Aedes Aegypti Eggs Using Infrared Spectroscopy; Using Infrared Spectroscopy to Characterize the Spectral Signature of Aedes Aegypti Eggs.

4. BACKGROUND OF THE STUDY Computer Vision The method proposed by Gumiran et al. (2022) for Aedes Aegypti egg detection using computer vision offers critical advantages over traditional methods. It eliminates the need for time-consuming and error-prone manual annotation and calibration. Additionally, it provides high-resolution images for more accurate egg detection and characterization. Importantly, it showcases the feasibility of applying computer vision in real-world settings, including low-resource areas and field environments. Infrared Spectroscopy In a 2020 study by Sroute et al., Infrared Spectroscopy (IRS) combined with Partial Least Squares - Discriminant Analysis (PLS-DA) demonstrated significant potential for accurately classifying mosquito species. IRS collected chemical signatures from four mosquito species (Aedes aegypti, Aedes albopictus, Aedes japonicus, and Aedes triseriatus ). Using these spectral features, the PLS-DA model achieved accurate identification, with 91% to 100% accuracy for each of the four mosquito species. This non-invasive approach holds promise for rapidly identifying mosquito species, which is vital for targeted disease control interventions and reducing the transmission of diseases carried by these insects

5. BACKGROUND OF THE STUDY A recent study by Silva and colleagues (2021) introduced an innovative multifunctional webcam spectroscopy tool. This instrument combines analytical determination and measurement techniques to offer comprehensive material insights. It excels in rapid, non-invasive analysis, utilizing high-resolution webcams and advanced image processing algorithms. Its versatility allows simultaneous measurement of parameters like absorption, emission, and fluorescence, providing a holistic view of material properties. This cost-effective, user-friendly tool has the potential to transform spectroscopy, creating new opportunities for researchers and scientists. ( Laganovska et al., 2020) introduced an affordable, portable device for measuring light absorbance in materials. Using a smartphone attachment equipped with a camera and LED light source, they captured images of samples at different light wavelengths. An open-source software platform processed these images to calculate absorbance values for each pixel. The study showcased the device's accuracy and precision in measuring absorbance in various samples, such as plant leaves and water, potentially transforming spectroscopy into a more accessible and cost-effective field for researchers worldwide.

6. BACKGROUND OF THE STUDY In a 2019 study by González Jiménez and colleagues, machine learning algorithms were used to forecast mosquito populations, including the dominant species and age structure. They analyzed environmental and entomological data, such as temperature, precipitation, and vegetation cover, and developed accurate predictive models. These models were tested with real-world data from Mexico, demonstrating their effectiveness in informing public health interventions and reducing the transmission of diseases like dengue fever. This study underscores the potential of machine learning in ecological prediction, benefiting both research and practical applications. In a recent 2023 study led by Garcia et al., Near-Infrared Spectroscopy (NIRS) emerged as a promising tool for disease surveillance in Aedes aegypti mosquitoes. NIRS showed high accuracy (90-100% sensitivity and 85-95% specificity) in detecting Zika and dengue viruses by analyzing chemical signatures through Principal Component Analysis (PCA). NIRS also identified co-infections with both viruses, highlighting its potential as an early warning system for disease outbreaks in mosquito-transmitted virus regions.

7. METHODOLOGY The methodology in this study aims to advance Aedes Aegypti mosquito surveillance and control through innovative infrared spectroscopy. It includes developing a rapid egg collection method and characterizing spectral signatures. Aeges Aegypti Egg Collection, Identification, and Sample Preparation For dataset accuracy, Aedes aegypti eggs will be preliminarily identified by a reputable entomologist using advanced lab equipment. Instrumentation and Spectral Data Acquisition Instrumentation and Data Pre-processing A low-cost spectrophotometer, building on the work of Silva et al. (2021), to collect spectral data. This study will use VB.NET/Python Programming to visualize the detected infrared light spectrum. Calibration will rely on precise emission peaks from a 7W fluorescent lamp at 436.00 and 546.00 nm, serving as reference markers. Feature Extraction The light source spectrum, representing light characteristics based on wavelength, is crucial in this study. The spectrometer utilizes interference within a diffraction grating to disperse light into its wavelengths, and a high-resolution webcam precisely measures its intensity. Spectral data will be collected and stored as datasets using VB.NET/Python for future development of a Spectral Classifier with CNN.

8. OBJECTIVES TARGET ACTIVITIES TARGET ACCOMPLISHMENTS Sept. Oct. Nov. Dec. To Conceptualize the Development and Optimization of a Rapid Collection and Classification Method for Aedes Aegypti Eggs Using Infrared Spectroscopy; Research title proposal and literature review 1 Research proposal based on the referenced paper Revising the Proposal based on Recommendations and Suggestions 1 Revised Research proposal based on the referenced paper Identify the software and hardware requirements and expert consultants 1 Concept Prototype design 1 Concept Software program work flow Data gathering and conceptualization of the prototype design and software work flow 1 Prototype design 1 Software program workflow Validate data gathered Discussed data gathered and proposed workflow to experts Using Infrared Spectroscopy to Characterize the Spectral Signature of Aedes Aegypti Eggs. Expert consultation Expert(s) suggestions and recommendations Identify the standard steps and procedures for spectral analysis 1 hardware and software protocol for spectral analysis Prototype design and software workflow Compiled workflow for spectral analysis using the proposed method Research documentation and consultations 1 Publishable paper