From Micro-Biology to Macro-Maps: How AI Transforms Tick Proteomics into Real-World Outbreak Predictors From Micro-Biology to Macro-Maps: How AI Transforms Tick Proteomics into Real-World Outbreak Predictors Published in Computational Virology & Epidemiology • 2026 Imagine a virus capable of bypassing a tick’s internal defense system, traveling from its digestive tract to its saliva, and jumping into livestock and humans with devastating clinical consequences. This is the reality of Severe Fever with Thrombocytopenia Syndrome (SFTS) , a high-consequence bunyavirus rapidly emerging as a critical public health priority across East and Southeast Asia. For decades, studying vector-borne diseases meant looking at field ecology and laboratory molecular biology as two completely separate worlds. Today, the integration of AI-driven structural biology and machine learning bridg...
Project Title: "Depth-Guard" – 3D Spatial Occupancy Monitor 1. The Problem In a smart warehouse, a robot needs to know if a loading zone is clear or occupied. A 2D camera alone can’t tell the difference between a "flat picture of a box" on the floor and an "actual 3D box." The Goal: Build a Python-based system that uses Computer Vision and Depth Perception (AI 3D) to identify objects and determine their 3D volume (Size) and Distance from the camera. 2. Intern Tasks Object Detection: Use a pre-trained model (like YOLOv8) to draw 2D boxes around objects. Depth Mapping: Use a depth estimation model (like MiDaS or a simulated Stereo-depth feed) to calculate how far each object is. Occupancy Logic: If an object is closer than 1 meter and larger than a specific volume, mark the zone as "BLOCKED." Alert System: Print a warning if the 3D space is too crowded. 3. Sample Datasets (Simulation) Since interns may not have 3D cameras (LiDAR/RGB-D), pr...