Current methods for detecting and assessing HIV-1 antibody resistance.

Antiretroviral therapy is the standard treatment for HIV, but it requires daily use and can cause side effects. Despite being available for decades, there are still 1.5 million new infections and 700,000 deaths each year, highlighting the need for better therapies.

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.

Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties.

When physical properties of molecules are being modeled with machine learning, it is desirable to incorporate (3)-covariance. While such models based on low body order features are not complete, we formulate and prove general completeness properties for higher order methods and show that 6 - 5 of these features are enough for up to atoms. We also find that the Clebsch-Gordan operations commonly used in these methods can be replaced by matrix multiplications without sacrificing completeness, lowering the scaling from () to () in the degree of the features.

Tailored Interaction between Cellulose Nanowhiskers and Core-Shell Particles Determines the Optical and Mechanical Properties in Hybrid Films.

Hybrid materials of core-shell particles and cellulose nanowhiskers (CNWs) were synthesized to produce opal films with increasing tensile strength. After the incorporation of CNWs into the processed particle films, differences in the mechanical and optical properties were noticeable, which stemmed from the adhesion forces between the cellulose and the particles' shell material. Two different particle compositions were compared, using polystyrene as cores, and either poly(ethyl acrylate) (PEA) or a copolymer of ethyl acrylate and 3 wt % of 2-hydroxyethyl methacrylate (HEMA) as the shell material.

Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables.

Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques.

Selective loss of Y chromosomes in lung adenocarcinoma modulates the tumor immune environment through cancer/testis antigens.

There is increasing recognition that the sex chromosomes, X and Y, play an important role in health and disease that goes beyond the determination of biological sex. Loss of the Y chromosome (LOY) in blood, which occurs naturally in aging men, has been found to be a driver of cardiac fibrosis and heart failure mortality. LOY also occurs in most solid tumors in males and is often associated with worse survival, suggesting that LOY may give tumor cells a growth or survival advantage.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study.

Introduction: Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability.

Methods: This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples.

Internal world models in humans, animals, and AI.

How do brains-biological or artificial-respond and adapt to an ever-changing environment? In a recent meeting, experts from various fields of neuroscience and artificial intelligence met to discuss internal world models in brains and machines, arguing for an interdisciplinary approach to gain deeper insights into the underlying mechanisms.

Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1.

Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables.

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

A Deeper Analysis of Volumetric Relightable Faces.

Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3 while handling both viewpoint and illumination editing from a single image.

Legal hypergraphs.

Complexity science provides a powerful framework for understanding physical, biological and social systems, and network analysis is one of its principal tools. Since many complex systems exhibit multilateral interactions that change over time, in recent years, network scientists have become increasingly interested in modelling and measuring networks featuring . At the same time, while network analysis has been more widely adopted to investigate the structure and evolution of law as a complex system, the utility of dynamic higher-order networks in the legal domain has remained largely unexplored.

Enhancing catalysis studies with chat generative pre-trained transformer (ChatGPT): Conversation with ChatGPT.

The progress made in natural language processing (NLP) and large language models (LLMs), such as generative pre-trained transformers, (GPT) has provided exciting opportunities for enhancing research across various fields. Within the realm of catalysis studies, GPT-driven models present valuable support in expediting the exploration and comprehension of catalytic processes. This research underscores the significance of ChatGPT in catalysis research, emphasizing its prowess as a valuable tool for furthering scientific inquiries.

Computing Generalized Convolutions Faster Than Brute Force.

In this paper, we consider a general notion of convolution. Let be a finite domain and let be the set of -length vectors (tuples) of . Let be a function and let be a coordinate-wise application of .

Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models.

The mechanisms triggering the human immunodeficiency virus type I (HIV-1) to switch the coreceptor usage from CCR5 to CXCR4 during the course of infection are not entirely understood. While low CD4+ T cell counts are associated with CXCR4 usage, a predominance of CXCR4 usage with still high CD4+ T cell counts remains puzzling. Here, we explore the hypothesis that viral adaptation to the human leukocyte antigen (HLA) complex, especially to the HLA class II alleles, contributes to the coreceptor switch.

Dynamics and durability of HIV-1 neutralization are determined by viral replication.

Human immunodeficiency virus type 1 (HIV-1)-neutralizing antibodies (nAbs) that prevent infection are the main goal of HIV vaccine discovery. But as no nAb-eliciting vaccines are yet available, only data from HIV-1 neutralizers-persons with HIV-1 who naturally develop broad and potent nAbs-can inform about the dynamics and durability of nAb responses in humans, knowledge which is crucial for the design of future HIV-1 vaccine regimens. To address this, we assessed HIV-1-neutralizing immunoglobulin G (IgG) from 2,354 persons with HIV-1 on or off antiretroviral therapy (ART).

Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions.

Generative adversarial networks (GANs) have gained significant attention in the field of image synthesis, particularly in computer vision. GANs consist of a generative model and a discriminative model trained in an adversarial setting to generate realistic and novel data. In the context of image synthesis, the generator produces synthetic images, whereas the discriminator determines their authenticity by comparing them with real examples.

Toward Explainable Artificial Intelligence for Precision Pathology.

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond.

Learning domain invariant representations by joint Wasserstein distance minimization.

Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain.

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that show a binding behaviour associated with DNA methylation.

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks.

Essential for understanding far-from-equilibrium processes, nonadiabatic (NA) molecular dynamics (MD) requires expensive calculations of the excitation energies and NA couplings. Machine learning (ML) can simplify computation; however, the NA Hamiltonian requires complex ML models due to its intricate relationship to atomic geometry. Working directly in the time domain, we employ bidirectional long short-term memory networks (Bi-LSTM) to interpolate the Hamiltonian.

Concept for the Real-Time Monitoring of Molecular Configurations during Manipulation with a Scanning Probe Microscope.

A bold vision in nanofabrication is the assembly of functional molecular structures using a scanning probe microscope (SPM). This approach requires continuous monitoring of the molecular configuration during manipulation. Until now, this has been impossible because the SPM tip cannot simultaneously act as an actuator and an imaging probe.

Alterations in the hepatocyte epigenetic landscape in steatosis.

Fatty liver disease or the accumulation of fat in the liver, has been reported to affect the global population. This comes with an increased risk for the development of fibrosis, cirrhosis, and hepatocellular carcinoma. Yet, little is known about the effects of a diet containing high fat and alcohol towards epigenetic aging, with respect to changes in transcriptional and epigenomic profiles.