The complex constraints in biological sequence design pose a significant challenge, rendering deep generative modeling a fitting methodology. In various applications, diffusion generative models have achieved noteworthy success. The continuous-time diffusion model framework of score-based generative stochastic differential equations (SDEs) has many advantages, but the initial SDEs do not readily accommodate the representation of discrete data. To model the generation of discrete data, such as biological sequences, using generative SDE models, we present a diffusion process operating within the probability simplex, its stationary distribution being Dirichlet. Discrete data modeling benefits from the natural suitability of diffusion in continuous space, as evidenced by this aspect. This approach, the Dirichlet diffusion score model, is employed by us. This method is demonstrated, in the context of Sudoku creation, by producing samples that adhere to strict constraints. This generative model's ability extends to solving Sudoku puzzles, encompassing intricate designs, without requiring additional training sessions. To conclude, this technique was employed to produce the first computational model for designing human promoter DNA sequences, and the outcome highlighted comparable features between the designed sequences and naturally occurring promoter sequences.

An elegant metric, the graph traversal edit distance (GTED), is determined by the smallest edit distance between strings reconstituted from Eulerian trails in two edge-labeled graphs. Evolutionary kinship between species can be determined via GTED by comparing de Bruijn graphs directly, avoiding the computationally intensive and error-prone task of genome assembly. Ebrahimpour Boroojeny et al. (2018) suggest two integer linear programming methods for GTED, a generalized transportation problem with equality demands, and assert that the problem's solvability is polynomial as the linear programming relaxation of one model consistently produces optimal integer solutions. Contrary to the complexity results of existing string-to-graph matching problems, GTED exhibits polynomial solvability. Through demonstrating GTED's NP-complete complexity and the fact that the ILPs proposed by Ebrahimpour Boroojeny et al. yield only a lower bound for GTED, failing to find a polynomial time solution, we resolve the conflict. We also present the initial two accurate integer linear programming (ILP) models for GTED and analyze their empirical efficiency. These results establish a substantial algorithmic framework for comparing genome graphs, pointing to the use of approximation heuristics. The source code enabling reproduction of the experimental results is situated at https//github.com/Kingsford-Group/gtednewilp/.

A non-invasive neuromodulation procedure, transcranial magnetic stimulation (TMS), effectively treats a wide array of cerebral disorders. A key determinant of successful TMS therapy is the precision of coil placement, presenting a considerable challenge when targeting particular brain regions in individual patients. Calculating the most effective coil placement and the subsequent electric field patterns on the brain's surface can be both financially burdensome and temporally demanding. SlicerTMS, a simulation methodology, allows for the real-time display of the TMS electromagnetic field's dynamics within the 3D Slicer medical imaging platform. Cloud-based inference and augmented reality visualization, using WebXR, are features of our software, which is powered by a 3D deep neural network. We assess SlicerTMS's performance across various hardware setups, contrasting it with the established SimNIBS TMS visualization tool. The code, data, and experiments we conducted are openly available at the following link: github.com/lorifranke/SlicerTMS.

A novel cancer treatment method, FLASH radiotherapy (RT), administers the full therapeutic dose in a timeframe of approximately one-hundredth of a second, employing a dose rate roughly one thousand times higher than conventional RT. To ensure the safety of clinical trials, a beam monitoring system capable of swiftly identifying and interrupting out-of-tolerance beams is critically needed. A FLASH Beam Scintillator Monitor (FBSM) is being created, drawing from the development of two novel, proprietary scintillator materials: an organic polymeric material, known as PM, and an inorganic hybrid, designated as HM. The FBSM, with its vast area coverage, low mass, linear response throughout a wide dynamic range, and radiation tolerance, further enables real-time analysis coupled with an IEC-compliant fast beam-interrupt signal. Prototype devices, subjected to radiation beams containing heavy ions, low-energy protons at nanoampere levels, FLASH dose-rate electron beams, and electron beams in hospital radiotherapy clinics, are detailed in the design concepts and resulting test data of this document. The results quantitatively assess image quality, response linearity, radiation hardness, spatial resolution, and the practicality of real-time data processing. Following a cumulative irradiation of 9 kGy and 20 kGy, the PM and HM scintillators maintained their signal strength without measurable decrement, respectively. A 15-minute exposure to a high FLASH dose rate of 234 Gy/s, culminating in a 212 kGy cumulative dose, resulted in a discernible decrease in the signal of HM, equal to -0.002%/kGy. Across the variables of beam currents, dose per pulse, and material thickness, these tests confirmed the FBSM's linear response. An evaluation of the FBSM's 2D beam image, as measured against commercial Gafchromic film, shows a high resolution and accurate replication of the beam profile, including its primary beam tails. At 20 kiloframes per second (or 50 microseconds per frame), real-time FPGA computation and analysis yield beam position, beam shape, and dose values within a timeframe less than 1 microsecond.

Latent variable models have become essential tools in computational neuroscience for comprehending neural computation. Autoimmune pancreatitis Consequently, a suite of robust offline algorithms for the extraction of latent neural pathways from neural recordings has been created. Still, despite the potential for real-time alternatives to furnish prompt feedback to experimenters and enhance experimental protocols, they have drawn considerably less attention. find more In this research, we detail the exponential family variational Kalman filter (eVKF), a recursive online Bayesian method for learning the dynamical system and inferring the latent trajectories simultaneously. Arbitrary likelihoods are accommodated by eVKF, which employs the constant base measure exponential family to model the stochasticity of latent states. The predict step of the Kalman filter is presented with a closed-form variational analogue, producing a provably tighter bound on the Evidence Lower Bound (ELBO) than another online variational method. The synthetic and real-world data validate our method's effectiveness, which notably shows competitive performance.

As machine learning algorithms gain widespread adoption in high-stakes contexts, there is growing apprehension about their potential to discriminate against certain segments of society. Despite the multitude of methods proposed for producing fair machine learning models, a common limitation is the implicit expectation of identical data distributions across training and deployment phases. The unfortunate reality is that, while fairness might be incorporated during model training, its practical application may not reflect this, causing unexpected outcomes at deployment. Despite the extensive research into building resilient machine learning models when confronted with dataset transformations, the prevailing methodologies predominantly prioritize the transfer of precision. This paper delves into the transfer of both accuracy and fairness in domain generalization, examining the challenges posed by test data originating from unseen domains. We formulate theoretical upper bounds on the unfairness and expected loss during deployment, followed by the deduction of necessary conditions that permit the perfect transfer of fairness and accuracy through invariant representation learning. Guided by this concept, we devise a learning algorithm that ensures machine learning models remain both fair and accurate when deployed in dynamic environments. Empirical studies utilizing real-world data confirm the validity of the proposed algorithm. The model implementation is present at the given GitHub address: https://github.com/pth1993/FATDM.

SPECT provides a mechanism to perform absorbed-dose quantification tasks for $alpha$-particle radiopharmaceutical therapies ($alpha$-RPTs). However, quantitative SPECT for $alpha$-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. In response to these difficulties, we introduce a SPECT reconstruction technique, quantitative and low-count, for isotopes with multiple emission peaks. The scarcity of detected photons requires the reconstruction method to extract the highest possible amount of information from each photon detected. medroxyprogesterone acetate List-mode (LM) processing of data across diverse energy windows is instrumental in fulfilling the objective. To reach this goal, a list-mode multi-energy window (LM-MEW) OSEM-based SPECT reconstruction strategy is introduced. This method employs data from multiple energy windows, recorded in list mode, and accounts for the energy characteristics of each photon detected. To achieve computational efficiency, we built a multi-GPU implementation of this algorithm. A single-scatter environment was used in 2-D SPECT simulation studies to assess the method while imaging [$^223$Ra]RaCl$_2$. When estimating activity uptake within defined regions of interest, the proposed method yielded better results compared to strategies relying on a single energy window or binned data. Improvements in both precision and accuracy of performance were witnessed, across a range of region-of-interest scales. Employing the LM-MEW method, our research demonstrated that using multiple energy windows and processing data in LM format improved quantification performance in low-count SPECT imaging, specifically for isotopes with multiple emission peaks.