Research Date: 2026-04-05 Category: AI-Genomics-Gene-Regulation Focus: PARM deep learning model for predicting and designing promoter activity The Bottom Line (TL;DR) Scientists just built an AI that can read and write the “grammar” of gene promoters—the DNA switches that control when and where genes turn on. The model, called PARM (Promoter Activity Regulatory Model), can: ✅ Predict how active a promoter will be in different cell types—just from its DNA sequence ✅ Design custom promoters that work as well as natural ones ✅ Reveal the hidden “rules” of gene regulation that were mysterious for decades Why it matters: This is a major step toward programmable gene expression—think precision gene therapies that activate only in the right cells, or regenerative medicine where we can control exactly which genes turn on during tissue repair. ...

AWS Middle East Data Center Attacks: Strategic Analysis and Lessons Learned Date: April 5, 2026 Author: Cloud Infrastructure Security Team Classification: Public Technical Insight Executive Summary In March-April 2026, Amazon Web Services (AWS) experienced unprecedented kinetic attacks on its Middle East data center infrastructure, marking the first documented wartime strikes against major hyperscaler facilities. Iranian Shahed-136 drones and ballistic missiles targeted AWS regions ME-CENTRAL-1 (United Arab Emirates) and ME-SOUTH-1 (Bahrain), causing structural damage, service disruptions, and forcing a fundamental reevaluation of cloud infrastructure resilience assumptions. ...

AI in Radiobiology & Radiopharmaceuticals: April 2026 Update Research Date: 2026-04-04 Category: AI-Radiobiology-Radiopharmaceutical Focus: AI-driven theranostics dosimetry, precision radiotherapy frameworks, and radiopharmaceutical discovery advances 1. AI-Enhanced Theranostics Dosimetry: Comprehensive 2025 Review A systematic review in Nuclear Medicine and Molecular Imaging (August 2025) examined deep learning applications in theranostic radiopharmaceutical dosimetry across three critical domains: image quality enhancement, dose estimation, and organ segmentation [1]. Deep Learning Architectures U-Net-based models: Primary architecture for organ segmentation, achieving Dice similarity coefficients >0.90 in benchmark challenges [1] Generative Adversarial Networks (GANs): Used for PET image synthesis and quality enhancement; Jyoti et al. achieved PSNR 32.83 and SSIM 77.48 for synthetic brain PET representing Alzheimer’s disease stages [1] Hybrid transformer networks: Emerging for multi-task dosimetry workflows combining segmentation and dose prediction [1] PET Image Synthesis Innovation Wang et al. demonstrated 3D U-Net synthesis of synaptic density (¹¹C-UCB-J) and amyloid deposition (¹¹C-PiB) PET from widely available ¹⁸F-FDG scans [1] Mean region-of-interest biases within ±2% across Alzheimer’s disease and cognitively normal groups [1] Applications: overcoming short-lived radionuclide imaging limitations, reducing radiation exposure, enabling delayed-time-point dosimetry without additional scans [1] Dosimetry Software Integrating AI QDOSE: Supports AI-based semi- and fully-automated organ segmentation, single time-point dosimetry, one-click hybrid dosimetry [1] MIM Software: Voxel-level dosimetry with AI-enhanced segmentation capabilities [1] VoxelDose, BigDose, RMDP: Additional voxel-based dosimetry packages incorporating ML components [1] Critical Challenges Identified Accurate dose estimation from theranostic pairs (diagnostic/therapeutic imaging correlation) [1] Lack of standardized imaging datasets for DL training [1] Radionuclide decay chain modeling complexity for multi-emitter isotopes [1] Need for optimization and standardization of AI models for clinical reliability [1] 2. Precision Radiotherapy Implementation Framework: Semantic AI Analysis A PMC-published study (2025) applied AI-driven semantic and temporal analysis to 3,343 unique articles (1964–2025) from Scopus, PubMed, and Web of Science, mapping radiotherapy-radiobiology-oncology evolution [2]. ...

Introduction: Standing at the Inflection Point As we conclude the Agentic Omics series in March 2026, we find ourselves at a genuine inflection point. The past two years have witnessed extraordinary progress: AlphaFold 3’s extension to protein complexes and ligands, the emergence of 7B-parameter genome models like Evo, foundation models for single-cell biology achieving clinical utility, and the first wave of agentic systems orchestrating multi-step scientific workflows. Yet we also face sobering realities: Phase III clinical trial results remain the ultimate arbiter of success, regulatory frameworks are still crystallising, and the gap between computational prediction and biological causality remains stubbornly wide. ...

Executive Summary Running Qwen3.5-35B GPTQ Int4 on 4× Nvidia T4 16GB GPUs is feasible with vLLM through tensor parallelism, distributing model computation across all GPUs. The Qwen3.5-35B model (35B total parameters with 3B activated via MoE) has an estimated GPTQ Int4 footprint of approximately 8-10 GB, which requires tensor parallelism across all 4 GPUs (totaling 64GB) to achieve optimal performance. vLLM’s architecture, built on PagedAttention for efficient memory management and GPTQ quantization support, enables this configuration to deliver reasonable throughput for inference workloads while staying within T4 GPU memory constraints. However, performance will be substantially lower than on higher-end GPUs due to T4’s limited PCIe bandwidth (16× Gen3) and lower FP32 compute capability. ...

Introduction: The Closed Loop of Discovery For centuries, the scientific method has followed a familiar rhythm: a human scientist observes a phenomenon, formulates a hypothesis, designs an experiment, executes it manually or with basic automation, analyses the results, and iterates. This cycle — hypothesis, experiment, analysis, refinement — is the engine of scientific progress. But it’s also a bottleneck. Each iteration takes days, weeks, or months. Human bandwidth limits the search space we can explore. And crucially, the loop is open: the scientist must close it manually, bringing their intuition and experience to bear at every step. ...

The Dual-Use Dilemma In July 2024, the Arc Institute published a paper in Science describing Evo, a 7.6 billion parameter foundation model trained on 300 billion nucleotides spanning all domains of life. The model could generate functional DNA sequences, predict fitness effects of mutations, and even design novel regulatory elements. It was a scientific breakthrough—and immediately raised a question that every researcher in biological AI now confronts: Could this same technology be used to create biological weapons? ...

Introduction: The Open Science Paradox In May 2024, Google DeepMind published AlphaFold 3 in Nature, describing a system that could predict the structure of protein complexes with DNA, RNA, ligands, and small molecules—a dramatic leap beyond AlphaFold 2’s protein-only predictions. But there was a catch: the code wasn’t released. For six months, researchers could read about the breakthrough but couldn’t reproduce it, build on it, or verify the claims independently. ...

The Agentic AI space is maturing fast. This week brought clear winners in the framework wars, a convergence among coding agents, and a decisive shift toward enterprise security. Here’s what you need to know. The Multi-Agent Framework Landscape: Winners Emerge LangGraph: The Production Choice If you’re building agents that need to run reliably in production, LangGraph has become the default choice. Companies like Uber, LinkedIn, and Klarna have had LangGraph agents running in production for over a year. ...