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Management of non-small-cell lung cancer (NSCLC) is affected by regional specificities. The present study aimed at determining diagnostic and therapeutic procedures including outcome of patients with NSCLC stage III in the real-world setting in Central European countries to define areas for improvements.

This piece is a collective exploration by seven doctoral researchers in Child Studies, who discuss notions of listening to children and young people in a Swedish context. We approach different aspects of listening in research and in practices such as education, psychiatry, and social work. The discussions in this collective writing are an invitation for continuous reflections about the contexts where listening to children is done, its challenges and possibilities.

Abstract GeoMx Digital Spatial Profiling (Nanostring) is a commercial spatial transcriptomics method to selectively analyze regions of interest within intact tissue sections. We show that decalcification with ethylene-diamine-tetra-acetic (EDTA) variably attenuates probe counts, while probes that are more resistant to this effect consequently appear overexpressed after quantile normalization. By determining the undisclosed full-length target sequences of probes used in the human whole transcriptome panel, hereby updating target transcripts and genes, we find that the gene-promiscuity of probes is an important factor that determines sensitivity to EDTA incubation.

Monthly streamflow forecasting is critical for improving water resource management. In this study, several base-classifier data-mining algorithms – conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – as well as several hybrid data-mining techniques – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR – that combine dagging with these algorithms were developed and applied to forecasting streamflow 3 and 12 months into the future (i.e., Qt+3 and Qt+12) based on meteorological data from a nearby station. Thirty years of data (from 1988 to 2018) that included precipitation, minimum relative humidity, maximum relative humidity, evaporation, hours of sunshine, maximum temperature, minimum temperature, wind speed, and streamflow were collected at the Kermanshah synoptic station were input to develop and evaluate several models. Varying combinations of the input data were tested to find the optimal set to employ. The models were validated and compared using several quantitative statistical indices. Though all satisfactorily predicted monthly streamflow, the hybrid models (DA-CR, DA-ISOR, and DA-SMOR) outperformed the base-classifier models (CR, ISOR, and SMOR), proving that dagging improved data-mining models significantly. Of the hybrid models, D-SMOR was the best. The models developed in this study are cost-effective tools for quick and accurate monthly streamflow forecasting. HIGHLIGHTS Streamflow is forecasting based on meteorological data from a nearby station.; Forecasting streamflow is for 3 and 12 months ahead (i.e., Qt+3 and Qt+12).; Conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR models are developed.; The DA-SMOR model has the highest prediction power.; Minimum and maximum temperature are the two most effective variables in Q forecasting.;

In this study, the vote algorithm used to improve the performances of three machine-learning models including M5Prime (M5P), random forest (RF), and random tree (RT) is developed (i.e. V-M5P, V-RF, and V-RT). Developed models were tested for forecasting soil temperature (TS) at 1, 2, and 3 days ahead at depths of 5 and 50 cm. All models were developed using different climatic variables, including mean, minimum, and maximum air temperatures; sunshine hours; evaporation; and solar radiation, which were evaluated. Correlation coefficients of 0.95 for the V-M5P model, 0.95 for the V-RF model, and 0.91 for the V-RT model were recorded for both 1- and 2-day ahead forecasting at a depth of 5 cm. For 3-day ahead forecasting, V-RF was the superior model with Nash–Sutcliff efficiency (NSE) values of 0.85, compared to V-M5P's value of 0.81 and V-RT's value of 0.81. The results at a depth of 5 cm indicate that V-RT was the least effective model. At a depth of 50 cm, forecasted TsS was in good agreement with measurements, and the V-RF was slightly superior. Among the limitations of the current work is that the models were unable to improve their performances by increasing the forecasting horizon. HIGHLIGHTS Modelling soil temperature at different depths based on meteorological variables using vote algorithm.; Forecasting soil temperature at 1, 2, and 3 days ahead at depths of 5 and 50 cm using machine learning models.; M5P, RF, and RT are applied.; An ensemble approach with the vote algorithm (i.e. V-M5P, V-RF, and V-RT) is also proposed.;

In the context of breast cancer treatment optimization, this study prospectively examines the feasibility and outcomes of utilizing intraoperative radiotherapy (IORT) as a boost in combination with standard external beam radiotherapy (EBRT) for high-risk patients. Different guidelines recommend such a tumor bed boost in addition to whole breast irradiation with EBRT for patients with risk factors for local breast cancer recurrence. The TARGIT BQR (NCT01440010) is a prospective, multicenter registry study aimed at ensuring the quality of clinical outcomes. It provides, for the first time, data from a large cohort with a detailed assessment of acute and long-term toxicity following an IORT boost using low-energy X-rays. Inclusion criteria encompassed tumors up to 3.5 cm in size and preoperative indications for a boost. The IORT boost, administered immediately after tumor resection, delivered a single dose of 20 Gy. EBRT and systemic therapy adhered to local tumor board recommendations. Follow-up for toxicity assessment (LENT SOMA criteria: fibrosis, teleangiectasia, retraction, pain, breast edema, lymphedema, hyperpigmentation, ulceration) took place before surgery, 6 weeks to 90 days after EBRT, 6 months after IORT, and then annually using standardized case report forms (CRFs). Between 2011 and 2020, 1133 patients from 10 centers were preoperatively enrolled. The planned IORT boost was conducted in 90%, and EBRT in 97% of cases. Median follow-up was 32 months (range 1–120, 20.4% dropped out), with a median age of 61 years (range 30–90). No acute grade 3 or 4 toxicities were observed. Acute side effects included erythema grade 1 or 2 in 4.4%, palpable seroma in 9.1%, punctured seroma in 0.3%, and wound healing disorders in 2.1%. Overall, chronic teleangiectasia of any grade occurred in 16.2%, fibrosis grade ≥ 2 in 14.3%, pain grade ≥ 2 in 3.4%, and hyperpigmentation in 1.1%. In conclusion, a tumor bed boost through IORT using low-energy X-rays is a swift and feasible method that demonstrates low rates in terms of acute or long-term toxicity profiles in combination with whole breast irradiation.

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques.

This paper presents a framework for utilizing available open data resources, such as Google Earth Engine, to assess environmental conditions and climate change vulnerability in Kosovo. This paper lays the groundwork for academic, government, and private sector institutions to develop strategies for addressing climate change and environmental issues in Kosovo and for other developing nations to understand the extent of change occurring. The study demonstrates the value of open data for environmental sustainability and resilience building. The framework is based on the analysis of air pollution, groundwater monitoring, urban environments, and deforestation in Kosovo using publicly available models. The model is re-coded for Kosovo in JavaScript using open datasets to create an environmental assessment of the scopes and scales of the environmental issues that plague Kosovo. The results show that there is a lack of institutional frameworks for assessing climate change impacts in Kosovo, with limited capacity for conducting environmental assessments, and with limited data capacity for resource and data scarce environments with community managed frameworks. By leveraging extensive amounts of data and applying analytical frameworks, this paper contributes to the knowledge base of environmental conditions in Kosovo with emphasis on open data and technological advancements.

Abstract The research aims to classify alluvial fans’ morphometric properties using the SOM algorithm. It also determines the relationship between morphometric characteristics and erosion rate and lithology using the GMDH algorithm. For this purpose, alluvial fans of 4 watersheds in Iran are extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amount of erosion, and formation material are investigated using the self-organizing map (SOM) method. Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as feature selection algorithms are used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm is employed to predict erosion and formation material based on morphometries. The results indicated that the semi-automatic method in GIS could detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting erosion were fan area (A f ) and minimum fan height (H min-f ). The feature selection algorithm identified (H min-f ), maximum fan height (H max-f ), minimum fan slope, and fan length (L f ) to be the morphometries most important for determining formation material, and basin area, fan area, (H max-f ) and compactness coefficient (C irb ) were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R2 = 0.94, R2 = 0.87).

Abstract We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes. To the best of our knowledge, CARDIO:DE is the first freely available and distributable German clinical corpus in the cardiovascular domain. In summary, our corpus offers unique opportunities for collaborative and reproducible research on natural language processing models for German clinical texts.

Background & Aims: Patient-derived organoid cancer models are generated from epithelial tumor cells and reflect tumor characteristics. However, they lack the complexity of the tumor microenvironment, which is a key driver of tumorigenesis and therapy response. Here, we developed a colorectal cancer organoid model that incorporates matched epithelial cells and stromal fibroblasts. Methods: Primary fibroblasts and tumor cells were isolated from colorectal cancer specimens. Fibroblasts were characterized for their proteome, secretome, and gene expression signatures. Fibroblast/organoid co-cultures were analyzed by immunohistochemistry and compared with their tissue of origin, as well as on gene expression levels compared with standard organoid models. Bioinformatics deconvolution was used to calculate cellular proportions of cell subsets in organoids based on single-cell RNA sequencing data. Results: Normal primary fibroblasts, isolated from tumor adjacent tissue, and cancer associated fibroblasts retained their molecular characteristics in vitro, including higher motility of cancer associated compared with normal fibroblasts. Importantly, both cancer-associated fibroblasts and normal fibroblasts supported cancer cell proliferation in 3D co-cultures, without the addition of classical niche factors. Organoids grown together with fibroblasts displayed a larger cellular heterogeneity of tumor cells compared with mono-cultures and closely resembled the in vivo tumor morphology. Additionally, we observed a mutual crosstalk between tumor cells and fibroblasts in the co-cultures. This was manifested by considerably deregulated pathways such as cell-cell communication and extracellular matrix remodeling in the organoids. Thrombospondin-1 was identified as a critical factor for fibroblast invasiveness. Conclusion: We developed a physiological tumor/stroma model, which will be vital as a personalized tumor model to study disease mechanisms and therapy response in colorectal cancer.