Lambda Science (United States)

Rapid advances in generative artificial intelligence have revolutionized biological modeling across domains such as protein, genetics, and single-cell. However, existing works often organize applications by molecule types or specific research tasks, overlooking the methodological convergence and cross-modal innovations. This paper aims to present a unified methodological perspective that highlights the fundamental technical commonalities across biological modalities. We systematically organize recent advances in generative modeling for biology through the lens of core machine learning paradigms, from language models (LMs) and diffusion models to their emerging hybrid architectures. Our work reveals how techniques initially developed for one molecular type (e.g., protein design) can be effectively transferred to others (e.g., RNA engineering), and identifies the convergence trend where discrete diffusion models and iterative language models represent different facets of a unified generative framework. We cover the evolution from domain-specific models to multi-modal biological foundation models and agent-based systems. By emphasizing methodological connections rather than applications, this paper aims to accelerate cross-domain innovation and make the field more accessible to the broader machine learning community. We conclude by identifying promising research directions where successful techniques in one biological domain remain unexplored in others, offering a roadmap for future advances in generative biology.

Our research investigates a new approach to presbyopia correction that incorporates phase masking designs into visual optics. Phase masking designs can create degraded PSFs that remain mostly unchanged in the presence of some defocus. These masks can also be tailored to the workings of the human visual system to minimize the impact of the degraded PSFs. This paper covers work done to design a phase masking IOL and to experimentally verify this concept. The IOL design process is carried out in Zemax OpticStudio where custom figures of merit are used to optimize presbyopia correction. For the experimental side of this study, a proof-of-concept PMMA phase plate was created using diamond turning and this optic’s impact on vision was investigated using an artificial eye. This showed that the phase masking design produces acceptable image quality across a range of object distances and pupil sizes.

Abstract The analysis and prediction of antibody–antigen (Ab–Ag) interactions often overlook critical structural features such as glycosylation, physical chemical conditions like pH and salt concentration, as well as the lack of standardized criteria for selecting complexes based on structural properties and sequence identity. Common practices in dataset construction rely on removing redundancy using sequence identity thresholds, which can inadvertently exclude complexes with alternative binding modes that share identical sequences. To enable more precise Ab–Ag modeling and antibody engineering, it is essential to incorporate richer structural and physical information into both physics-based and machine learning models. To address these limitations, we present ANABAG, a new curated dataset of Ab–Ag complexes annotated at the residue level with UniProt sequence information and enriched with a wide range of structural and physicochemical features. The dataset allows flexible filtering of complexes using a variety of descriptors available at both the complex and residue levels. Selected features are ready to use in machine learning workflows, while the structural files are compatible with antibody design and docking pipelines like Rosetta or Haddock. The complete dataset is available on Zenodo, and all accompanying scripts and usage documentation can be accessed via GitHub at https://github.com/DSIMB/anabag-handler.git.

Comparative evaluations of key policy decisions detracting from and confronting the conceptual necessity for initiating the combinatorial approach in real time operations (CA RTO) for preempting genetic disease lesions including the Mutome are summarized here (Figures The harnessing of Evolutionary Genetics of Speciation (EGS) and specifically 'Hybrid Incompatibility (HI)' and its wider generational manifestation

In recent years, mosaic images have found great success thanks to the increasing development in the field of imaging as well as the technological evolution of computer systems (camera, mobile, etc.). Mosaic images are obtained by merging several images of the same scene. The process incorporates several steps, each of which requires resources and execution time depending on the size, quality and resolution of the images used. In this paper, we propose a new image mosaic method that significantly reduces the execution time. The idea is to apply the stages of registration and search for the best inliers, necessary for the calculation of the geometric transformation, to the miniature of images. This allows the minimization of the overall processing time without altering the quality of the results. The experiment, on a database of images, shows that the proposed algorithm provides rapid results compared to similar methods. Also, we have extended our method to generate 360° panoramic images.

This work presents an efficient numerical method based on spectral expansions for simulation of heat and moisture diffusive transfers through multilayered porous materials. Traditionally, by using the finite-difference approach, the problem is discretized in time and space domains (Method of lines) to obtain a large system of coupled Ordinary Differential Equations (ODEs), which is computationally expensive. To avoid such a cost, this paper proposes a reduced-order method that is faster and accurate, using a much smaller system of ODEs. To demonstrate the benefits of this approach, tree case studies are presented. The first one considers nonlinear heat and moisture transfer through one material layer. The second case - highly nonlinear - imposes a high moisture content gradient - simulating a rain like condition - over a two-layered domain, while the last one compares the numerical prediction against experimental data for validation purposes. Results show how the nonlinearities and the interface between materials are easily and naturally treated with the spectral reduced-order method. Concerning the reliability part, predictions show a good agreement with experimental results, which confirm robustness, calculation efficiency and high accuracy of the proposed approach for predicting the coupled heat and moisture transfer through porous materials.

User support personnel, systems engineers, and administrators of HPC installations need to be aware of log and telemetry information from different systems in order to perform routine tasks ranging from systems management to user inquiries. We present an integrated, distributed HPC tailored monitoring system, based on a current generation software stack from the DevOps community, with integration into the work load management system. The goal of this system is to provide a quicker turnaround time for user inquiries in response to errors. Dashboards provide an overlay of system and node level events on top of correlated metrics data. This information is directly available for querying, manipulation, and filtering, allowing statistical analysis and aggregation of collected data. Furthermore, additional dashboards offer in-sight into how users are interacting with available resources and pin-point fluctuations in utilization. The system can integrate sources of information from other monitoring solutions and event-based sources.

I argue that the clinical theory of psychoanalytic technique is flawed because of the absence of the conceptualization of the patient and analyst as whole people who are vital alive agencies. This flaw interferes with the effectiveness of psychoanalytic technique and limits the description of what actually takes place in psychoanalysis. It is then difficult to focus on the transformational changes in the present moment with its issues of closeness, intimacy and responsibility in an evolving self.

Mechanical stress in flip-chip assemblies continues to be a significant problem both for the reliability of the component and for the assembly of a flat component to the next board-level assembly. This paper describes the combination of unique low-temperature multi-step curing profiles with the use of variable frequency microwaves (VFM) to produce lower warpage components both on the die side of the package as well as on the board carrier side. This lower warpage compared to standard convection cure is maintained even after three sequential lead-free solder reflow conditions. Statistical data support this improved co-planarity by Shadow Moire measurements at various stages of processing from as-received parts through prebake, cure, and three reflow cycles. Typical co-planarity improvement in the 12-65 percent range is observed and verified by confirmation sample sizes used for microwave cured parts and conventional box oven cured parts. Thinned and larger die, and reduced thickness substrate boards showed the most warpage improvement with VFM. Two under-fill chemistries show the same effect despite lower cure temperatures and faster cure cycle times.