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Terrestrial vertebrates have evolved hard skin appendages, such as scales, claws, feathers, and hair that play crucial roles in defense, predation, locomotion, and thermal insulation. The mechanical properties of these skin appendages are largely determined by cornified epithelial components. So-called “hair keratins,” cysteine-rich intermediate filament proteins that undergo covalent cross-linking via disulfide bonds, are the crucial structural proteins of hair and claws in mammals and hair keratin orthologs are also present in lizard claws, indicating an evolutionary origin in a hairless common ancestor of amniotes. Here, we show that reptiles and birds have also other cysteine-rich keratins which lack cysteine-rich orthologs in mammals. In addition to hard acidic (type I) sauropsid-specific (HAS) keratins, we identified hard basic (type II) sauropsid-specific (HBS) keratins which are conserved in lepidosaurs, turtles, crocodilians, and birds. Immunohistochemical analysis with a newly made antibody revealed expression of chicken HBS1 keratin in the cornifying epithelial cells of feathers. Molecular phylogenetics suggested that the high cysteine contents of HAS and HBS keratins evolved independently from the cysteine-rich sequences of hair keratin orthologs, thus representing products of convergent evolution. In conclusion, we propose an evolutionary model in which HAS and HBS keratins evolved as structural proteins in epithelial cornification of reptiles and at least one HBS keratin was co-opted as a component of feathers after the evolutionary divergence of birds from reptiles. Thus, cytoskeletal proteins of hair and feathers are products of convergent evolution and evolutionary co-option to similar biomechanical functions in clade-specific hard skin appendages.

The growth of skin appendages, such as hair, feathers and scales, depends on terminal differentiation of epidermal keratinocytes. Here, we investigated keratinocyte differentiation in avian scutate scales. Cells were isolated from the skin on the legs of 1-day old chicks and subjected to single-cell transcriptomics. We identified two distinct populations of differentiated keratinocytes. The first population was characterized by mRNAs encoding cysteine-rich keratins and corneous beta-proteins (CBPs), also known as beta-keratins, of the scale type, indicating that these cells form hard scales. The second population of differentiated keratinocytes contained mRNAs encoding cysteine-poor keratins and keratinocyte-type CBPs, suggesting that these cells form the soft interscale epidermis. We raised an antibody against keratin 9-like cysteine-rich 2 (KRT9LC2), which is encoded by an mRNA enriched in the first keratinocyte population. Immunostaining confirmed expression of KRT9LC2 in the suprabasal epidermal layers of scutate scales but not in interscale epidermis. Keratinocyte differentiation in chicken leg skin resembled that in human skin with regard to the transcriptional upregulation of epidermal differentiation complex genes and genes involved in lipid metabolism and transport. In conclusion, this study defines gene expression programs that build scutate scales and interscale epidermis of birds and reveals evolutionarily conserved keratinocyte differentiation genes.

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. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

Keratins are the main intermediate filament proteins of epithelial cells. In keratinocytes of the mammalian epidermis they form a cytoskeleton that resists mechanical stress and thereby are essential for the function of the skin as a barrier against the environment. Here, we performed a comparative genomics study of epidermal keratin genes in terrestrial and fully aquatic mammals to determine adaptations of the epidermal keratin cytoskeleton to different environments. We show that keratins K5 and K14 of the innermost (basal), proliferation-competent layer of the epidermis are conserved in all mammals investigated. In contrast, K1 and K10, which form the main part of the cytoskeleton in the outer (suprabasal) layers of the epidermis of terrestrial mammals, have been lost in whales and dolphins (cetaceans) and in the manatee. Whereas in terrestrial mammalian epidermis K6 and K17 are expressed only upon stress-induced epidermal thickening, high levels of K6 and K17 are consistently present in dolphin skin, indicating constitutive expression and substitution of K1 and K10. K2 and K9, which are expressed in a body site-restricted manner in human and mouse suprabasal epidermis, have been lost not only in cetaceans and manatee but also in some terrestrial mammals. The evolution of alternative splicing of K10 and differentiation-dependent upregulation of K23 have increased the complexity of keratin expression in the epidermis of terrestrial mammals. Taken together, these results reveal evolutionary diversification of the epidermal cytoskeleton in mammals and suggest a complete replacement of the quantitatively predominant epidermal proteins of terrestrial mammals by originally stress-inducible keratins in cetaceans.

The skin epithelium, ie, the epidermis, of dolphins and whales (cetaceans) is up to 50 times thicker than that of humans and other mammals living on land. Recently, comparative genomics revealed further striking differences in the cytoskeleton of the outer layers of the epidermis in aquatic and terrestrial mammals. Cetaceans lack the cytoskeletal keratins, which make up more than half of the total protein mass in the cornified epidermal layer of terrestrial mammals under homeostatic conditions. By contrast, orthologs of stress-inducible epithelial keratins are conserved in cetaceans and these keratins are constitutively expressed in their skin. Thus, the epidermal stress response program of a terrestrial common ancestor of modern mammals has become the default program of epidermal differentiation and a central component of the unique cutaneous organization of cetaceans. We propose that phenotypic plasticity during stress responses plays important roles in the evolution of the skin.

Keratins are the main cytoskeletal proteins of epithelial cells and changes in the expression of keratins have contributed to the evolutionary adaptation of epithelia to different environments. Keratin K24 was proposed to be a differentiation marker of epidermal keratinocytes but the significance of K24 expression in the epidermis versus other tissues has remained elusive. Here, we show by RT-PCR, western blot, and immunofluorescence analyses that K24 is highly expressed in the epithelium of the cornea whereas its expression levels are significantly lower in other stratified epithelia including in the epidermis. To investigate the evolutionary history of K24, we screened the genome sequences of vertebrates for orthologs of the human KRT24 gene. The results of this comparative genomics study suggested that KRT24 originated in a common ancestor of amniotes and that it was lost independently in three clades of mammals, i.e. camels, cetaceans, and a subclade of pinnipeds comprising eared seals and the walrus. Together, the results of this study identify K24 as component of the cytoskeleton in the human corneal epithelium and reveal previously unknown differences of keratin gene content among mammalian species.

Feathers are the most complex skin appendages of vertebrates. Mature feathers consist of interconnected dead keratinocytes that are filled with heavily cross-linked proteins. Although the molecular architecture determines essential functions of feathers, only few feather proteins have been characterized with regard to their amino acid sequences and evolution. Here, we identify Epidermal Differentiation protein containing DPCC Motifs (EDDM) as a cysteine-rich protein that has co-evolved with other feather proteins. The EDDM gene is located within the avian epidermal differentiation complex (EDC), a cluster of genes that has originated and diversified in amniotes. EDDM shares the exon-intron organization with EDC genes of other amniotes, including humans, and a gene encoding an EDDM-like protein is present in crocodilians, suggesting that avian EDDM arose by sequence modification of an epidermal differentiation gene present in a common ancestor of archosaurs. The EDDM protein contains multiple sequence repeats and a higher number of cysteine residues than any other protein encoded in the EDC. Immunohistochemical analysis of chicken skin and skin appendages showed expression of EDDM in barb and barbules of feathers as well as in the subperiderm on embryonic scutate scales. These results suggest that the diversification and differential expression of EDDM, besides other EDC genes, was instrumental in facilitating the evolution of the most complex molecular architecture of feathers.

Large language models (LLMs) such as Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used across domains, yet their generic training data and propensity for hallucination limit reliability in safety-critical fields like forestry. This paper outlines the conception and prototype of ForestGPT, a domain-specialised assistant designed to support forest professionals while preserving expert oversight. It addresses two looming risks: unverified adoption of generic outputs and professional mistrust of opaque algorithms. We propose a four-level development path: (1) pre-training a transformer on curated forestry literature to create a baseline conversational tool; (2) augmenting it with Retrieval-Augmented Generation to ground answers in local and time-sensitive documents; (3) coupling growth simulators for scenario modeling; and (4) integrating continuous streams from sensors, drones and machinery for real-time decision support. A Level-1 prototype, deployed at Futa Expo 2025 via a mobile app, successfully guided multilingual visitors and demonstrated the feasibility of lightweight fine-tuning on open-weight checkpoints. We analyse technical challenges, multimodal grounding, continual learning, safety certification, and social barriers including data sovereignty, bias and change management. Results indicate that trustworthy, explainable, and accessible LLMs can accelerate the transition to Forestry 5.0, provided that human-in-the-loop guardrails remain central. Future work will extend ForestGPT with full RAG pipelines, simulator coupling and autonomous data ingestion. Whilst exemplified in forestry, a complex, safety-critical, and ecologically vital domain, the proposed architecture and development path are broadly transferable to other sectors that demand trustworthy, domain-specific language models under expert oversight.

This study explores the usability of human-robot collaboration in the previously under-researched field of forestry and agroforestry. The robotic platforms used were Boston Dynamics Spot and the Agile X Bunker, the latter equipped with a movable arm. The research was conducted in an experimental robotic test park, simulating real-world scenarios relevant to forestry and agriculture. The focus of this study is on the use of these robots as collaborative robots (cobots). Usability, as a central characteristic in human-computer interaction, was evaluated using the well-established System Usability Scale (SUS). The results demonstrate the potential of these robotic systems to enhance productivity and safety, while also underscoring the importance of user-centered design in the development of collaborative tools. A key finding of this work is that successful integration of AI-driven technologies in sectors such as forestry and agriculture requires a focus on human-centered AI which includes good usability, and accessibility, emphasizing the importance of the concept of universal access.

Colorectal cancer (CRC) is one of the most common solid malignant burdens worldwide. Cancer immunology and immunotherapy have become fundamental areas in CRC research and treatment. Currently, the method of generating Immune-Related Gene Prognostic Indices (IRGPIs) has been found to predict patient prognosis as an immune-related prognostic biomarker in a variety of tumors. However, their role in patients with CRC remains mostly unknown. Therefore, we aimed to establish an IRGPI for prognosis evaluation in CRC. RNA-sequencing data and clinical information of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases as training and validation sets, respectively. Immune-related gene data was obtained from the and databases. The weighted gene co-expression network analysis (WGCNA) was used to identify hub immune-related genes. An IRGPI was then constructed using Cox regression methods. Based on the median risk score of IRGPI, patients could be divided into high-risk and low-risk groups. To further investigate the immunologic differences, Gene set variation analysis (GSVA) studies were conducted. In addition, immune cell infiltration and related functional analysis were used to identify the differential immune cell subsets and related functional pathways. We identified 49 immune-related genes associated with the prognosis of CRC, 17 of which were selected for an IRGPI. The IRGPI model significantly differentiates the survival rates of CRC patients in the different groups. The IRGPI as an independent prognostic factor significantly correlates with clinico-pathological factors such as age and tumor stage. Furthermore, we developed a nomogram to improve the clinical utility of the IRGPI score. Immuno-correlation analysis in different IRGPI groups revealed distinct immune cell infiltration (CD4 T cells resting memory) and associated pathways (macrophages, Type I IFNs responses, iDCs.), providing new insights into the tumor microenvironment. At last, drug sensitivity analysis revealed that the high-risk IRGPI group was sensitive to 11 and resistant to 15 drugs. Our study established a promising immune-related risk model for predicting survival in CRC patients. This could help to better understand the correlation between immunity and the prognosis of CRC providing a new perspective for personalized treatment of CRC.