Leveraging large language models to construct feedback from medical multiple-choice Questions.

Exams like the formative Progress Test Medizin can enhance their effectiveness by offering feedback beyond numerical scores. Content-based feedback, which encompasses relevant information from exam questions, can be valuable for students by offering them insight into their performance on the current exam, as well as serving as study aids and tools for revision. Our goal was to utilize Large Language Models (LLMs) in preparing content-based feedback for the Progress Test Medizin and evaluate their effectiveness in this task.

Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.

Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effectiveness of DL in capturing leaf shape, we used geometric morphometrics (GM), an emerging component of eXplainable Artificial Intelligence (XAI) toolkits.

Opportunistic plant observations reveal spatial and temporal gradients in phenology.

Opportunistic plant records provide a rapidly growing source of spatiotemporal plant observation data. Here, we used such data to explore the question whether they can be used to detect changes in species phenologies. Examining 19 herbaceous and one woody plant species in two consecutive years across Europe, we observed significant shifts in their flowering phenology, being more pronounced for spring-flowering species (6-17 days) compared to summer-flowering species (1-6 days).

Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: A Case Study with a Bivalve Data Set.

Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of 2 deep learning methods-supervised classification approaches and unsupervised similarity learning-to infer organism relationships from specimen images.

Species delimitation 4.0: integrative taxonomy meets artificial intelligence.

Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.

Parametrization of biological assumptions to simulate growth of tree branching architectures.

Modeling and simulating the growth of the branching of tree species remains a challenge. With existing approaches, we can reconstruct or rebuild the branching architectures of real tree species, but the simulation of the growth process remains unresolved. First, we present a tree growth model to generate branching architectures that resemble real tree species.

7 Dimensions of software change patterns.

Evolving software is a highly complex and creative problem in which a number of different strategies are used to solve the tasks at hand. These strategies and reoccurring coding patterns can offer insights into the process. However, they can be highly project or even task-specific.

In Vitro Activities of Dithiocarbamate Derivatives against Metacestode Vesicles.

The metacestode stage of the fox tapeworm causes the severe zoonotic disease alveolar echinococcosis. New treatment options are urgently needed. Disulfiram and dithiocarbamates were previously shown to exhibit activity against the trematode As both parasites belong to the platyhelminths, here we investigated whether these compounds were also active against metacestode vesicles in vitro.

Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring.

Plant phenology plays a vital role in assessing climate change. To monitor this, individual plants are traditionally visited and observed by trained volunteers organized in national or international networks - in Germany, for example, by the German Weather Service, DWD. However, their number of observers is continuously decreasing.

Discovering unknown response patterns in progress test data to improve the estimation of student performance.

Background: The Progress Test Medizin (PTM) is a 200-question formative test that is administered to approximately 11,000 students at medical universities (Germany, Austria, Switzerland) each term. Students receive feedback on their knowledge (development) mostly in comparison to their own cohort. In this study, we use the data of the PTM to find groups with similar response patterns.

The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem.

We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science.

A systematic comparison of deep learning methods for EEG time series analysis.

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data.

Nutritional Assessment of Ready-to-Eat Salads in German Supermarkets: Comparison of the nutriRECIPE-Index and the Nutri-Score.

Globally, an unbalanced diet causes more deaths than any other factor. Due to a lack of knowledge, it is difficult for consumers to select healthy foods at the point of sale. Although different front-of-pack labeling schemes exist, their informative value is limited due to small sets of considered parameters and lacking information on ingredient composition.

Disulfiram and dithiocarbamate analogues demonstrate promising antischistosomal effects.

Schistosomiasis is a neglected tropical disease with more than 200 million new infections per year. It is caused by parasites of the genus Schistosoma and can lead to death if left untreated. Currently, only two drugs are available to combat schistosomiasis: praziquantel and, to a limited extent, oxamniquine.

The potential of multispectral imaging flow cytometry for environmental monitoring.

Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time-intensive and not well-suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification.

SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks.

Stakeholders of software development projects have various information needs for making rational decisions during their daily work. Satisfying these needs requires substantial knowledge of where and how the relevant information is stored and consumes valuable time that is often not available. Easing the need for this knowledge is an ideal text-to-SQL benchmark problem, a field where public datasets are scarce and needed.

Deep Learning in Plant Phenological Research: A Systematic Literature Review.

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales.

Synaptic Scaling-An Artificial Neural Network Regularization Inspired by Nature.

Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the objective of finding new strategies to enhance the learning capabilities of neural networks, we focus on a phenomenon that is closely related to learning tasks and neural stability in biological neural networks, called homeostatic plasticity.

Multi-view classification with convolutional neural networks.

Humans' decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object's class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision.

Flora Capture: a citizen science application for collecting structured plant observations.

Background: Digital plant images are becoming increasingly important. First, given a large number of images deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about plant morphological characteristics.

Sulfoximines as Rising Stars in Modern Drug Discovery? Current Status and Perspective on an Emerging Functional Group in Medicinal Chemistry.

Sulfoximines have been largely disregarded in medicinal chemistry for a long time. However, recently, they have risen to the apparent level of stardom on the drug discovery scene. Considering the outstanding properties of sulfoximines, this versatile functional group has advanced to implementation in several drug discovery programs.

Pollen analysis using multispectral imaging flow cytometry and deep learning.

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics).

Synthesis and Biological Evaluation of Endocannabinoid Uptake Inhibitors Derived from WOBE437.

WOBE437 ((2E,4E)-N-(3,4-dimethoxyphenethyl)dodeca-2,4-dienamide, 1) is a natural product-derived, highly potent inhibitor of endocannabinoid reuptake. In this study, we synthesized almost 80 analogues of 1 with different types of modifications in the dodecadienoyl domain as well as the dimethoxyphenylethyl head group, and we investigated their effects on anandamide uptake into U937 cells. Intriguingly, none of these analogues was a more potent inhibitor of anandamide uptake than WOBE437 (1).