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1.
Artif Intell Med ; 133: 102423, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36328669

RESUMO

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.


Assuntos
Inteligência Artificial , Medicina , Humanos
2.
Plants (Basel) ; 11(19)2022 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-36235492

RESUMO

Functional and structural adjustments of plants in response to environmental factors, including those occurring in alpine habitats, can result in transient acclimation, plastic phenotypic adjustments and/or heritable adaptation. To unravel repeatedly selected traits with potential adaptive advantage, we studied parallel (ecotypic) and non-parallel (regional) differentiation in leaf traits in alpine and foothill ecotypes of Arabidopsis arenosa. Leaves of plants from eight alpine and eight foothill populations, representing three independent alpine colonization events in different mountain ranges, were investigated by microscopy techniques after reciprocal transplantation. Most traits clearly differed between the foothill and the alpine ecotype, with plastic adjustments to the local environment. In alpine populations, leaves were thicker, with altered proportions of palisade and spongy parenchyma, and had fewer trichomes, and chloroplasts contained large starch grains with less stacked grana thylakoids compared to foothill populations. Geographical origin had no impact on most traits except for trichome and stomatal density on abaxial leaf surfaces. The strong parallel, heritable ecotypic differentiation in various leaf traits and the absence of regional effects suggests that most of the observed leaf traits are adaptive. These trait shifts may reflect general trends in the adaptation of leaf anatomy associated with the colonization of alpine habitats.

3.
Sci Rep ; 12(1): 16857, 2022 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-36207536

RESUMO

Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest (GDF) with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our proposed explainable approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer.


Assuntos
Algoritmos , Aprendizado de Máquina , Biologia Computacional/métodos , Humanos , Medicina de Precisão , Biologia de Sistemas
4.
Bioinformatics ; 38(Supplement_2): ii120-ii126, 2022 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-36124793

RESUMO

MOTIVATION: The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability and explainability. RESULTS: In this work, we present a novel graph-based deep learning framework for disease subnetwork detection via explainable GNNs. Each patient is represented by the topology of a protein-protein interaction (PPI) network, and the nodes are enriched with multi-omics features from gene expression and DNA methylation. In addition, we propose a modification of the GNNexplainer that provides model-wide explanations for improved disease subnetwork detection. AVAILABILITY AND IMPLEMENTATION: The proposed methods and tools are implemented in the GNN-SubNet Python package, which we have made available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Redes Neurais de Computação , Mapas de Interação de Proteínas , Humanos
5.
Abdom Radiol (NY) ; 47(12): 4151-4159, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36104481

RESUMO

PURPOSE: To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC. METHODS: This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann-Whitney U test, Bonferroni-Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant. RESULTS: Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, "low gray-level zone emphasis" yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC. CONCLUSION: Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment.


Assuntos
Adenocarcinoma , Carcinoma Ductal Pancreático , Neoplasias Hepáticas , Neoplasias Pancreáticas , Humanos , Estudos Retrospectivos , Tomografia Computadorizada por Raios X , Microambiente Tumoral
6.
IEEE Comput Graph Appl ; PP2022 Aug 18.
Artigo em Inglês | MEDLINE | ID: mdl-35981070

RESUMO

The process of finding a diagnosis in the medical domain relies on implicit knowledge and the experience of a human expert. In this paper, we report on the observation of human decision making, shown by the example of pathology. By tracking the diagnostic steps, individual building blocks are identified, which not only contribute to a diagnostic finding, but can also be used in the future to train and develop artificial intelligence (AI) algorithms. This work also provides insights into the interaction of human experts regarding the observation time of so-called 'hot spots', the magnification used for specific findings, and the overall observation and decision path followed. The documentation scheme yields a standardized examination procedure which shows the concept the pathologist is actually looking for as well as the possible features of findings that can be identified. This contribution indicates how important visualization is for human-centered AI, and specifically for enabling human oversight with respect to AI implementation in high-stake areas such as medicine.

7.
Stud Health Technol Inform ; 294: 137-138, 2022 May 25.
Artigo em Inglês | MEDLINE | ID: mdl-35612038

RESUMO

Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability of the feature selection methods BORUTA, VITA, and RRF (regularized random forest). In particular, we investigate the feature ranking instability of the associated stochastic algorithms. For stabilization of the feature ranks, we propose to compute consensus values from multiple feature selection runs, applying rank aggregation techniques. Our results show that these consolidated features are more accurate and robust, which helps to make practical machine learning applications more trustworthy.


Assuntos
Algoritmos , Aprendizado de Máquina , Biomarcadores , Biologia Computacional/métodos
8.
Microorganisms ; 10(5)2022 Apr 30.
Artigo em Inglês | MEDLINE | ID: mdl-35630392

RESUMO

Trebouxiophyceae are microalgae occupying even extreme environments such as polar regions or deserts, terrestrial or aquatic, and can occur free-living or as lichen photobionts. Yet, it is poorly understood how environmental factors shape their metabolism. Here, we report on responses to light and temperature, and metabolic adjustments to desiccation in Diplosphaera epiphytica, isolated from a lichen, and Edaphochlorella mirabilis, isolated from Tundra soil, assessed via growth and photosynthetic performance parameters. Metabolite profiling was conducted by GC-MS. A meta-analysis together with data from a terrestrial and an aquatic Chlorella vulgaris strain reflected elements of phylogenetic relationship, lifestyle, and relative desiccation tolerance of the four algal strains. For example, compatible solutes associated with desiccation tolerance were up-accumulated in D. epiphytica, but also sugars and sugar alcohols typically produced by lichen photobionts. The aquatic C. vulgaris, the most desiccation-sensitive strain, showed the greatest variation in metabolite accumulation after desiccation and rehydration, whereas the most desiccation-tolerant strain, D. epiphytica, showed the least, suggesting that it has a more efficient constitutive protection from desiccation and/or that desiccation disturbed the metabolic steady-state less than in the other three strains. The authors hope that this study will stimulate more research into desiccation tolerance mechanisms in these under-investigated microorganisms.

9.
N Biotechnol ; 70: 67-72, 2022 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-35526802

RESUMO

Artificial Intelligence (AI) for the biomedical domain is gaining significant interest and holds considerable potential for the future of healthcare, particularly also in the context of in vitro diagnostics. The European In Vitro Diagnostic Medical Device Regulation (IVDR) explicitly includes software in its requirements. This poses major challenges for In Vitro Diagnostic devices (IVDs) that involve Machine Learning (ML) algorithms for data analysis and decision support. This can increase the difficulty of applying some of the most successful ML and Deep Learning (DL) methods to the biomedical domain, just by missing the required explanatory components from the manufacturers. In this context, trustworthy AI has to empower biomedical professionals to take responsibility for their decision-making, which clearly raises the need for explainable AI methods. Explainable AI, such as layer-wise relevance propagation, can help in highlighting the relevant parts of inputs to, and representations in, a neural network that caused a result and visualize these relevant parts. In the same way that usability encompasses measurements for the quality of use, the concept of causability encompasses measurements for the quality of explanations produced by explainable AI methods. This paper describes both concepts and gives examples of how explainability and causability are essential in order to demonstrate scientific validity as well as analytical and clinical performance for future AI-based IVDs.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Algoritmos , Aprendizado de Máquina , Software
10.
Sensors (Basel) ; 22(8)2022 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-35459028

RESUMO

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline-no AI can do this. Consequently, human-centered AI (HCAI) is a combination of "artificial intelligence" and "natural intelligence" to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.


Assuntos
Inteligência Artificial , Robótica , Ecossistema , Fazendas , Florestas , Humanos
11.
Phytotaxa ; 532(3): 192-208, 2022 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-35330967

RESUMO

Timaviella Sciuto & Moro is a recently established cryptic genus of cyanobacteria separated from the morphologically close Leptolyngbya due to clear differences in the 16S rRNA gene sequence and the 16S-23S ITS region secondary structure. Conducting research on biological soil crusts in coastal ecotopes of Ukraine and Germany, we repeatedly observed thin filamentous cyanobacteria morphologically corresponding to the common terrestrial species Leptolyngbya edaphica (Elenkin) Anagnostidis & Komárek. Molecular data based on 16S rRNA gene sequence comparison of the original strains of the morphospecies indicated unambiguous assignment to the genus Timaviella. Based on this finding, we proposed the new nomenclatural combination Timaviella edaphica (Elenkin) O.M. Vynogr. & Mikhailyuk in our previous publication. Deeper molecular study of the four original strains which were morphologically identified as T. edaphica based on the 16S rRNA gene concatenated with the 16S-23S ITS region and 16S-23S ITS secondary structure analysis showed that they are not identical. Three of them (isolated from biocrusts of Black Sea coast and forest path near Kyiv, Ukraine) had high similarity both in 16S rRNA (99.7-100%) and 16S-23S ITS (99.8-100%) hence actually representing T. edaphica. The strain Us-6-3 isolated from biocrusts on sand dunes of Usedom Island in the Baltic Sea, Germany, differs both from original strains of T. edaphica and all published Timaviella species in 16S rRNA gene sequence identity, as well as in sequence and structure of the 16S-23S ITS region. Here we describe Timaviella dunensis sp. nov. and give an expanded description of T. edaphica based on morphological and molecular features. A tabular review of Timaviella species with data on their phenotypic and genotypic features, ecology and distribution is included.

12.
Bioinformatics ; 2022 Feb 09.
Artigo em Inglês | MEDLINE | ID: mdl-35139148

RESUMO

MOTIVATION: Limited data access has hindered the field of precision medicine from exploring its full potential, e.g., concerning machine learning and privacy and data protection rules. METHODS: Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets, and (iii) imbalanced phenotypes, evaluated on five biomedical datasets. RESULTS: The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances. CONCLUSION: Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics, and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

13.
Artigo em Inglês | MEDLINE | ID: mdl-35077357

RESUMO

Optimal performance is desired for decision-making in any field with binary classifiers and diagnostic tests, however common performance measures lack depth in information. The area under the receiver operating characteristic curve (AUC) and the area under the precision recall curve are too general because they evaluate all decision thresholds including unrealistic ones. Conversely, accuracy, sensitivity, specificity, positive predictive value and the F1 score are too specificthey are measured at a single threshold that is optimal for some instances, but not others, which is not equitable. In between both approaches, we propose deep ROC analysis to measure performance in multiple groups of predicted risk (like calibration), or groups of true positive rate or false positive rate. In each group, we measure the group AUC (properly), normalized group AUC, and averages of: sensitivity, specificity, positive and negative predictive value, and likelihood ratio positive and negative. The measurements can be compared between groups, to whole measures, to point measures and between models. We also provide a new interpretation of AUC in whole or part, as balanced average accuracy, relevant to individuals instead of pairs. We evaluate models in three case studies using our method and Python toolkit and confirm its utility.

14.
World J Urol ; 40(5): 1125-1134, 2022 May.
Artigo em Inglês | MEDLINE | ID: mdl-35084542

RESUMO

LITERATURE REVIEW: Cystoscopy is the gold standard for initial macroscopic assessments of the human urinary bladder to rule out (or diagnose) bladder cancer (BCa). Despite having guidelines, cystoscopic findings are diverse and often challenging to classify. The extent of the false negatives and false positives in cystoscopic diagnosis is currently unknown. We suspect that there is a certain degree of under-diagnosis (like the failure to detect malignant tumours) and over-diagnosis (e.g. sending the patient for unnecessary transurethral resection of bladder tumors with anesthesia) that put the patient at risk. CONCLUSIONS: XAI robot-assisted cystoscopes would help to overcome the risks/flaws of conventional cystoscopy. Cystoscopy is considered a less life-threatening starting point for automation than open surgical procedures. Semi-autonomous cystoscopy requires standards and cystoscopy is a good procedure to establish a model that can then be exported/copied to other procedures of endoscopy and surgery. Standards also define the automation levels-an issue for medical product law. These cystoscopy skills do not give full autonomy to the machine, and represent a surgical parallel to 'Autonomous Driving' (where a standard requires a human supervisor to remain in the 'vehicle'). Here in robotic cystoscopy, a human supervisor remains bedside in the 'operating room' as a 'human-in-the-loop' in order to safeguard patients. The urologists will be able to delegate personal- and time-consuming cystoscopy to a specialised nurse. The result of automated diagnostic cystoscopy is a short video (with pre-processed photos from the video), which are then reviewed by the urologists at a more convenient time.


Assuntos
Inteligência Artificial , Neoplasias da Bexiga Urinária , Cistoscopia/métodos , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Bexiga Urinária/patologia , Neoplasias da Bexiga Urinária/diagnóstico por imagem , Neoplasias da Bexiga Urinária/cirurgia
15.
Plant J ; 109(3): 568-584, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-34767672

RESUMO

Charophyte green algae (CGA) are assigned to be the closest relatives of land plants and therefore enlighten processes in the colonization of terrestrial habitats. For the transition from water to land, plants needed significant physiological and structural changes, as well as with regard to cell wall composition. Sequential extraction of cell walls of Nitellopsis obtusa (Charophyceae) and Spirogyra pratensis (Zygnematophyceae) offered a comparative overview on cell wall composition of late branching CGA. Because arabinogalactan-proteins (AGPs) are considered common for all land plant cell walls, we were interested in whether these special glycoproteins are present in CGA. Therefore, we investigated both species with regard to characteristic features of AGPs. In the cell wall of Nitellopsis, no hydroxyproline was present and no AGP was precipitable with the ß-glucosyl Yariv's reagent (ßGlcY). By contrast, ßGlcY precipitation of the water-soluble cell wall fraction of Spirogyra yielded a glycoprotein fraction rich in hydroxyproline, indicating the presence of AGPs. Putative AGPs in the cell walls of non-conjugating Spirogyra filaments, especially in the area of transverse walls, were detected by staining with ßGlcY. Labelling increased strongly in generative growth stages, especially during zygospore development. Investigations of the fine structure of the glycan part of ßGlcY-precipitated molecules revealed that the galactan backbone resembled that of AGPs with 1,3- 1,6- and 1,3,6-linked Galp moieties. Araf was present only in small amounts and the terminating sugars consisted predominantly of pyranosidic terminal and 1,3-linked rhamnose residues. We introduce the term 'rhamnogalactan-protein' for this special AGP-modification present in S. pratensis.


Assuntos
Evolução Biológica , Parede Celular/química , Embriófitas/química , Galactanos/química , Mucoproteínas/química , Proteínas de Plantas/química , Spirogyra/química , Spirogyra/genética , Carofíceas/química , Carofíceas/genética , Galactanos/genética , Mucoproteínas/genética , Proteínas de Plantas/genética
16.
Methods Mol Biol ; 2364: 177-198, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34542854

RESUMO

This chapter presents an overview of the most common F-actin influencing substances, used to study actin dynamics in living plant cells for studies on morphogenesis, motility, organelle movement, apoptosis, or abiotic stress. These substances can be divided into two major subclasses-F-actin-stabilizing and F-actin-polymerizing substances like jasplakinolide and chondramides and F-actin-severing compounds like cytochalasins and latrunculins. Jasplakinolide, which may have anti-cancer activities, was originally isolated from a marine sponge and can now be synthesized and has become commercially available, which is responsible for its wide distribution as membrane-permeable F-actin-stabilizing and F-actin-polymerizing agent. Recently an acyclic derivate of jasplakinolide was isolated. Cytochalasins, derived from fungi, show an F-actin-severing function, and many derivatives are commercially available (A, B, C, D, E, H, J), also making it a widely used compound for F-actin disruption. The same can be stated for latrunculins (A, B), derived from Red Sea sponges; however the mode of action is different by binding to G-actin and inhibiting incorporation into the filament. In the case of swinholide, isolated from red algae or the cyanobacterium Nostoc, a stable complex with actin dimers is formed, resulting in severing F-actin.For influencing F-actin dynamics in plant cells, only membrane permeable drugs are useful in a broad range. We, however, introduce also the phallotoxins and synthetic derivatives thereof, as they are widely used to visualize F-actin in fixed cells. A particular uptake mechanism has been shown for hepatocytes but has also been described in siphonal giant algae. The focus is set on F-actin dynamics in plant cells where alterations in cytoplasmic streaming can be particularly well studied; moreover fluorescence methods for phalloidin- and antibody staining as well as techniques for immunoelectron microscopy are explained.


Assuntos
Poríferos , Citoesqueleto de Actina , Actinas , Animais , Citocalasinas/farmacologia , Depsipeptídeos , Faloidina , Células Vegetais
17.
Environ Epidemiol ; 5(6): e182, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34909561

RESUMO

The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world's fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research. In addition, the platform enables adding new data and analysis pipelines. HEAP's final product can be deployed in multiple instances to create a network of shareable and reusable knowledge on the impact of exposures on public health.

18.
Mycol Prog ; 20(6): 797-808, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34720793

RESUMO

Lichens and their isolated symbionts are potentially valuable resources for biotechnological approaches. Especially mycobiont cultures that produce secondary lichen products are receiving increasing attention, but lichen mycobionts are notoriously slow-growing organisms. Sufficient biomass production often represents a limiting factor for scientific and biotechnological investigations, requiring improvement of existing culturing techniques as well as methods for non-invasive assessment of growth. Here, the effects of pH and the supplement of growth media with either D-glucose or three different sugar alcohols that commonly occur in lichens, D-arabitol, D-mannitol and ribitol, on the growth of the axenically cultured mycobiont isolated from the lichen Xanthoria parietina were tested. Either D-glucose or different sugar alcohols were offered to the fungus at different concentrations, and cumulative growth and growth rates were assessed using two-dimensional image analysis over a period of 8 weeks. The mycobiont grew at a pH range from 4.0 to 7.0, whereas no growth was observed at higher pH values. Varying the carbon source in Lilly-Barnett medium (LBM) by replacing 1% D-glucose used in the originally described LBM by either 1%, 2% or 3% of D-mannitol, or 3% of D-glucose increased fungal biomass production by up to 26%, with an exponential growth phase between 2 and 6 weeks after inoculation. In summary, we present protocols for enhanced culture conditions and non-invasive assessment of growth of axenically cultured lichen mycobionts using image analysis, which may be useful for scientific and biotechnological approaches requiring cultured lichen mycobionts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11557-021-01707-7.

19.
Pediatr Radiol ; 2021 Oct 19.
Artigo em Inglês | MEDLINE | ID: mdl-34664088

RESUMO

In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can "see" more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics - thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled "explainable AI," which should enable a balance/cooperation between AI and human intelligence - thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks.

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