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Protein–protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interactome mapping remains challenging at the proteome scale. Experimental approaches provide only partial coverage, while existing computational methods often lack generalizability across species or are too resource-intensive for large-scale screening. He
Genetics as a science has roots in studying phenotypes of relatives, but molecular approaches facilitate direct measurements of genomic variation between individuals. Agricultural and human biomedical research are both emphasizing genotype-based instruments, such as polygenic scores, but unlike in agriculture, there is an emerging consensus that family variables act nearly independently of genotyp
The high neutron flux of the European Spallation Source in Lund, Sweden, opens new possibilities for neutron protein crystallography. Making full use of these gains requires dedicated instrumentation and support facilities to maximize its contribution to our understanding of biological processes at the molecular level.
Ancient DNA has revolutionized the study of extinct and extant organisms that lived up to 2 million years ago, enabling the reconstruction of genomes from multiple extinct species, as well as the ecosystems where they once thrived. However, current DNA sequencing techniques alone cannot directly provide insights into tissue identity, gene expression dynamics, or transcriptional regulation, as thes
RATIONALE: Very preterm infants (i.e. born before 32 weeks of gestation) are at risk of cerebral injury and long-term neurodevelopmental impairment. Cerebral near-infrared spectroscopy (NIRS) enables continuous monitoring of cerebral oxygenation to guide clinical management. Interest in NIRS has grown in recent years, highlighting the need for better evidence to support its clinical efficacy in im
Temporal information in episodic memory reflects how experiences are encoded rather than objective elapsed time, giving rise to systematic distortions. These are particularly evident in emotional contexts, consistent with emotion-related biases in attention. The present study investigated whether emotion-driven distortions in remembered temporal distance were explained by encoding-related attentio
Background: Glomerular filtration rate (GFR) is a key indicator of kidney function, typically estimated using creatinine or cystatin C. When cystatin-C-based eGFR is markedly lower than creatinine-based eGFR, this discrepancy may reflect a selective impairment in the filtration of medium-sized molecules, referred to as Shrunken Pore Syndrome (SPS), or more broadly, Selective Glomerular Hypofiltrat
Coronary artery disease is one of the leading causes of mortality worldwide. While early identification and treatment of major cardiovascular risk factors are crucial, recent data suggest the possibility of non-invasively detecting early stages of coronary atherosclerosis and potentially stabilizing or even reversing the burden of atherosclerosis with innovative and existing treatments. Moreover,
The testing and safety cases of assisted and automated driving (AAD) functions require considerations for nonideal environmental conditions, such as adverse and extreme weather. In these extreme conditions, perception sensors (e.g., camera, LiDAR, and RADAR), which build the situational awareness of the vehicle, might produce noisy and degraded data. Therefore, it is key to consider: 1) how to rel
The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet changes in the hardware platform and application. This article introduces a Multi-Objective Hardware-Aware Quantization (MOHAQ) method, which considers hardware performance and inference error as objectives for mixed-precisi
Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC
Recognition of human manipulation actions together with the analysis and execution by a robot is an important issue. Also, perception of spatial relationships between objects is central to understanding the meaning of manipulation actions. Here we would like to merge these two notions and analyze manipulation actions using symbolic spatial relations between objects in the scene. Specifically, we d
This thesis investigates particle sorting in deterministic lateral displacement (DLD) microfluidic devices, with a focus on how particle size and shape influence sorting behavior. A broad range of synthetic and biological particles were studied, including polystyrene particles, fabricated silicon-based structures, and bacterial samples with diverse physical properties.While DLD has traditionally b
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory that facilitates encoding, recalling, and predicting action experiences. Our proposed unsupervised deep episodic memory model as follows: First, encodes observed actions in a latent vector space and, based on this latent encoding, second, infers most similar episodes previously experie
This paper contributes to semantic representation of human demonstrated actions for robot execution of time constrained tasks. We propose a semantic action encoding method based on interactions between the subject and objects in the scene. Our semantic framework is enriched with a descriptive spatial reasoning method which leads to accurate segmentation and recognition of unique action primitives.
Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequences, most approaches suffer from problems relating to vast variations in motions, action combinations, and scene contexts. In this paper, we introduce a novel method for semantic segmentation and recognition of long and co
We present a novel unsupervised framework, which links continuous visual features and symbolic textual descriptions of manipulation activity videos. First, we extract the semantic representation of visually observed manipulations by applying a bottom-up approach to the continuous image streams. We then employ a rule-based reasoning to link visual and linguistic inputs. The proposed framework allow
Dogs exhibit an exceptional range of morphological diversity as a result of their long-term association with humans. Attempts to identify when dog morphological variation began to expand have been constrained by the limited number of Pleistocene specimens, the fragmentary nature of remains, and difficulties in distinguishing early dogs from wolves on the basis of skeletal morphology. In this study
