The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a critical bottleneck for RNA-based therapeutics development. Recent advances have highlighted the potential of machine learning (ML) to predict transfection efficiency from molecular structure, enabling high-throughput virtual screening and accelerating lead identification. However, existing approaches are hindered by inadequate data quality, ineffective feature representations, low predictive accuracy, and poor generalizability. Here, we present LANTERN (Lipid nANoparticle Transfection Efficiency pRedictioN), a robust ML framework for predicting transfection efficiency based on ionizable lipid representation. We benchmarked a diverse set of ML models against AGILE, a previously published model developed for transfection prediction. Our results show that combining simpler models with chemically informative features, particularly count-based Morgan fingerprints, outperforms more complex models that rely on internally learned embeddings, such as AGILE. We also show that a multi-layer perceptron trained on a combination of Morgan fingerprints and Expert descriptors achieved the highest performance ($\text{R}^2$ = 0.8161, r = 0.9053), significantly exceeding AGILE ($\text{R}^2$ = 0.2655, r = 0.5488). We show that the models in LANTERN consistently have strong performance across multiple evaluation metrics. Thus, LANTERN offers a robust benchmarking framework for LNP transfection prediction and serves as a valuable tool for accelerating lipid-based RNA delivery systems design.

We propose two neural network based and data-driven supply and demand models to analyze the efficiency, identify service gaps, and determine the significant predictors of demand, in the bus system for the Department of Public Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to James Madison University (JMU). The supply and demand models, one temporal and one spatial, take many variables into account, including the demographic data surrounding the bus stops, the metrics that the HDPT reports to the federal government, and the drastic change in population between when JMU is on or off session. These direct and data-driven models to quantify supply and demand and identify service gaps can generalize to other cities' bus systems. Keywords-- transportation systems, bus systems, public transportation, direct ridership models, data driven models, mathematical modeling, neural networks, machine learning, supply models, demand models, machine learning, service gaps, social vulnerability, public transportation access, GIS data, data science, data quality.

The topic of retrofitting, i.e., the modernization of machines and systems into the digital age, is also an important trend in terms of sustainability, energy saving and resource optimization that we are also serving. Our solutions for automation and quality assurance are used every day in numerous industries such as the automotive industry, the food industry and for mechanical engineering. From concept design to the integration of the finished system, and of course the subsequent support, we do everything in-house. Our vision is to continue creating innovative turnkey solutions, produce new products that are missing on the market and ensuring the future of quality assurance in the machine vision industry. Roman: "Due to great work experiences with American clients, we decided to enter the US market. Just like in Germany, we want to offer the US market our turn-key inspection solutions and services with the goal to guarantee the highest quality and offer our clients a high ROI with our full-spectrum machine vision systems."

The primary focus of this paper is to produce a proof of concept for extracting drowsiness information from videos to help elderly living on their own. To quantify yawning, eyelid and head movement over time, we extracted 3000 images from captured videos for training and testing of deep learning models integrated with OpenCV library. The achieved classification accuracy for eyelid and mouth open/close status were between 94.3%-97.2%. Visual inspection of head movement from videos with generated 3D coordinate overlays, indicated clear spatiotemporal patterns in collected data (yaw, roll and pitch). Extraction methodology of the drowsiness information as timeseries is applicable to other contexts including support for prior work in privacy-preserving augmented coaching, sport rehabilitation, and integration with big data platform in healthcare.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. Stocks took a pounding last week as the political turbulence in Washington and terror attacks in Spain caught up with the market. But closer to home employers statewide increased their payrolls by 82,600 jobs in July. Sectors that saw the most employment gains include government, which added 18,800 jobs; educational and health services, which saw an increase of 18,600; and leisure and hospitality, which was up 15,200 jobs. Dark day: The long-awaited solar eclipse sweeps across America on Monday.