The risk of reactivity against healthy tissues: Novel CARs demand testing for overlooked binding properties.

A rapidly growing number of chimeric antigen receptors (CARs) is being translated into cell therapy for malignant and autoimmune diseases. While cancer cell-selective CAR targeting is undergoing continuous refinement, specific testing for overlooked recognition of healthy tissues is commonly not performed, which potentially results in underestimating of the risk of severe tissue damage upon CAR T cell application. Using the FcμR/IgM receptor/FAIM3/TOSO-specific CAR, designed to target chronic lymphocytic leukemia cells, we exemplarily outline a screen to uncover reactivities to healthy tissues and discuss the value of such pre-clinical testing to improve safety in CAR T cell application.

New Strains of the Deep Branching Streptophyte Streptofilum: Phylogenetic Position, Cell Biological and Ecophysiological Traits, and Description of Streptofilum arcticum sp. nov.

Streptofilum capillatum was recently described and immediately caught scientific attention, because it forms a phylogenetically deep branch in the streptophytes and is characterised by a unique cell coverage composed of piliform scales. Its phylogenetic position and taxonomic rank are still controversial discussed. In the present study, we isolated further strains of Streptofilum from biocrusts in sand dunes and Arctic tundra soil.

Is human oversight to AI systems still possible?

The rapid proliferation of artificial intelligence (AI) systems across diverse domains raises critical questions about the feasibility of meaningful human oversight, particularly in high-stakes domains such as new biotechnology. As AI systems grow increasingly complex, opaque, and autonomous, ensuring responsible use becomes a formidable challenge. During our editorial work for the special issue "Artificial Intelligence for Life Sciences", we placed increasing emphasis on the topic of "human oversight".

Integrating binding affinity and tonic signaling enables a rational CAR design for augmented T cell function.

Background: The success of chimeric antigen receptor (CAR) T cell therapy for hematological malignancies has not yet translated into long-term elimination of solid tumors indicating the need for adequately tuning CAR T cell functionality.

Methods: We leveraged a translational pipeline including biophysical characterization and structural prediction of the CAR binding moiety, evaluation of cellular avidity, synapse formation, T cell motility, and functional capacities under repetitive target challenge and in sustained tumor control.

Results: As an example of clinical relevance, we derived a panel of anti-Her2 CARs covering a 4-log affinity range, all expected to target the same Her2 epitope.

Multi-objective optimization of cable-road layouts in smart forestry.

Current cable-road layouts for timber harvesting in steep terrain are often based on either manual planning or automated layouts generated from low-resolution GIS data, limiting potential benefits and informed decision-making. In this paper, we present a novel approach to improve cable-road design using multi-objective optimization based on realistic cable-road representations. We systematically compare the effectiveness of single-objective and multi-objective optimization methods for generating layouts using these representations.

From Industry 5.0 to Forestry 5.0: Bridging the gap with Human-Centered Artificial Intelligence.

Purpose Of The Review: Recent technological innovations in Artificial Intelligence (AI) have successfully revolutionized many industrial processes, enhancing productivity and sustainability, under the paradigm of Industry 5.0. It offers opportunities for the forestry sector such as predictive analytics, automation, and precision management, which could transform traditional forest operations into smart, effective, and sustainable practices.

Zygospore formation in Zygnematophyceae predates several land plant traits.

Recent research on a special type of sexual reproduction and zygospore formation in Zygnematophyceae, the sister group of land plants, is summarized. Within this group, gamete fusion occurs by conjugation. Zygospore development in and is highlighted, which has recently been studied using Raman spectroscopy, allowing chemical imaging and detection of changes in starch and lipid accumulation.

Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop.

The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein Interaction (PPI) network, situated in a federated ensemble-based deep learning context. This methodological approach marks a significant stride in the development of explainable and privacy-aware Artificial Intelligence, significantly contributing to the progression of personalized digital medicine in a responsible and transparent manner.

Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences.

Objective: Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience.

Virtual Reality Cognitive Remediation in Older Adults with Bipolar Disorder: The Effects on Cognitive Performance and Depression in a Feasibility Randomized Controlled Trial.

Introduction: Dementia, depression, and cardiovascular disease are major public health concerns for older adults, requiring early intervention. This study investigates whether a virtual reality cognitive remediation program (VR-CR) can improve cognitive function and depressive symptoms in older adults, and determines the necessary sample size for future studies. Integrated VR and CR interventions have shown promising outcomes in older adults with neurodegenerative and mental health disorders.

Simulating cable corridors based on terrestrial LiDAR data.

This article introduces a new basis for optimising cable corridor layouts in timber extraction on steep terrain by using a digital twin of a forest. Traditional approaches for generating cable corridor layouts rely on less accurate contour maps, which can lead to layouts which rely on infeasible supports, undermining confidence in the generated layouts. We present a detailed simulational approach which uses high-resolution tree maps and digital terrain models to compute realistic representations of all possible cable corridors in a given terrain.

Raman Spectral Analysis in the CH-Stretching Region as a Guiding Beacon for Non-Targeted, Disruption-Free Monitoring of Germination and Biofilm Formation in the Green Seaweed Ulva.

Raman spectroscopy was used to study the complex interactions and morphogenesis of the green seaweed Ulva (Chlorophyta) and its associated bacteria under controlled conditions in a reductionist model system. Integrating multiple imaging techniques contributes to a more comprehensive understanding of these biological processes. Therefore, Raman spectroscopy was introduced as a non-invasive, label-free tool for examining chemical information of the tripartite community Ulva mutabilis-Roseovarius sp.

The Light-activated Effect of Natural Anthraquinone Parietin against Candida auris and Other Fungal Priority Pathogens.

Antimicrobial photodynamic therapy (aPDT) is an evolving treatment strategy against human pathogenic microbes such as the species, including the emerging pathogen . Using a modified EUCAST protocol, the light-enhanced antifungal activity of the natural compound parietin was explored. The photoactivity was evaluated against three separate strains of five yeasts, and its molecular mode of action was analysed via several techniques, i.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods.

Zygospore development of (Charophyta) investigated by serial block-face scanning electron microscopy and 3D reconstructions.

Sexual reproduction of Zygnematophyceae by conjugation is a less investigated topic due to the difficulties of the induction of this process and zygospore ripening under laboratory conditions. For this study, we collected field sampled zygospores of and three additional strains in Austria and Greece. Serial block-face scanning electron microscopy was performed on high pressure frozen and freeze substituted zygospores and 3D reconstructions were generated, allowing a comprehensive insight into the process of zygospore maturation, involving storage compound and organelle rearrangements.

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches.

Sensors for Digital Transformation in Smart Forestry.

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts.

CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks.

Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key blockage for the use of AI in clinical decision support systems (CDSS). Although AI models are already performing excellently in systems medicine, their black-box nature entails that patient-specific decisions are incomprehensible for the physician. Explainable AI (XAI) algorithms aim to "explain" to a human domain expert, which input features influenced a specific recommendation.

The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.

Purpose: This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance.

Cobalt Oxide 2D Nanosheets Formed at a Polarized Liquid|Liquid Interface toward High-Performance Li-Ion and Na-Ion Battery Anodes.

Cobalt oxide (CoO)-based nanostructures have the potential as low-cost materials for lithium-ion (Li-ion) and sodium-ion (Na-ion) battery anodes with a theoretical capacity of 890 mAh/g. Here, we demonstrate a novel method for the production of CoO nanoplatelets. This involves the growth of flower-like cobalt oxyhydroxide (CoOOH) nanostructures at a polarized liquid|liquid interface, followed by conversion to flower-like CoO via calcination.

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

Summary: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features.

Lipid degradation and photosynthetic traits after prolonged darkness in four Antarctic benthic diatoms, including the newly described species sp. nov.

In polar regions, the microphytobenthos has important ecological functions in shallow-water habitats, such as on top of coastal sediments. This community is dominated by benthic diatoms, which contribute significantly to primary production and biogeochemical cycling while also being an important component of polar food webs. Polar diatoms are able to cope with markedly changing light conditions and prolonged periods of darkness during the polar night in Antarctica.