Osimertinib inhibits brain metastases and improves long-term survival in a patient with advanced squamous cell lung cancer: a case report and literatures review

BackgroundSquamous cell carcinoma (SCC) is one of the most common subtypes of non-small cell lung cancer, but its treatment options remain limited. Epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitors (TKIs) have limited efficacy in the treatment of lung SCC. Here, we report an SCC patient who developed EGFR-T790M mutation and showed gefitinib resistance achieved an extremely long survival by taking Osimertinib alternatively.Case summaryA patient, 66-year-old non-smoking and drinking male with advanced SCC who was deemed inoperable at the time of diagnosis. The first genetic testing showed deletion mutation of exon 19 of EGFR. The patient was then treated with gefitinib with no significant efficacy. EGFR-T790M mutation was found in the second genetic test. The treatment regimen was changed to radiotherapy with Osimertinib, and the patient’s primary lesion and the brain metastases were well controlled.ConclusionThis typical case highlights the important role of Osimertinib in patients with SCC carrying EGFR mutations

Methodology for Determining the Optimal Operating Strategies for a Chilled Water Storage System

This dissertation proposed a new methodology for determining the optimal operating strategies for a chilled water storage system under a Time-of-Use electricity rate structure. It is based on a new classification of operating strategies and an investigation of multiple search paths. Each operating strategy consists of a control strategy and the maximum number of chillers running during the off-peak and on-peak periods. For each month, the strategy with the lowest monthly billing cost and minimal water level higher than the setpoint is selected as the optimal operating strategy for the current month. A system model is built to simulate the tank water level at the end of each time step and the system total power during each time step. This model includes six sub-models. Specifically, the plant model is a forward model using a wire-to-water concept to simulate the plant total power. For the Thermal Energy Storage (TES) model, the tank state is described with total chilled water volume in the tank and its derivation is the tank charging or discharging flow rate. A regression model is adopted to simulate the loop supply and return temperature difference as well as the loop total flow rate demand. In the control strategy sub-model, except for three conventional control strategies and the operation without TES, a new control strategy is advanced to load the chiller optimally. The final results will be a table showing the monthly control strategy and maximal number of chillers staged on during the off-peak and on-peak periods, an approach which is easy for the operators to follow. Two project applications of this methodology are introduced in this dissertation. One is an existing TES system with state-of-the-art control and metering systems. The monthly optimal operating strategies are generated, which will achieve significant savings. The comparisons among different control strategies are also provided. The other application consists of multiple plants with little data. The purpose of the study is to evaluate the economic feasibility of designing a new chilled water storage tank and sharing it among four plants. This problem can be solved with a simplified system model, and an optimal tank size is recommended

ShaDDR: Real-Time Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering

We present ShaDDR, an example-based deep generative neural network which produces a high-resolution textured 3D shape through geometry detailization and conditional texture generation applied to an input coarse voxel shape. Trained on a small set of detailed and textured exemplar shapes, our method learns to detailize the geometry via multi-resolution voxel upsampling and generate textures on voxel surfaces via differentiable rendering against exemplar texture images from a few views. The generation is real-time, taking less than 1 second to produce a 3D model with voxel resolutions up to 512^3. The generated shape preserves the overall structure of the input coarse voxel model, while the style of the generated geometric details and textures can be manipulated through learned latent codes. In the experiments, we show that our method can generate higher-resolution shapes with plausible and improved geometric details and clean textures compared to prior works. Furthermore, we showcase the ability of our method to learn geometric details and textures from shapes reconstructed from real-world photos. In addition, we have developed an interactive modeling application to demonstrate the generalizability of our method to various user inputs and the controllability it offers, allowing users to interactively sculpt a coarse voxel shape to define the overall structure of the detailized 3D shape