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. METHODS: We performed k-means clustering with a dataset of 5,444 students, selected cluster number k = 5, and answers as features. Subsequently, the data was passed to XGBoost with the cluster assignment as target enabling the identification of cluster-relevant questions for each cluster with SHAP. Clusters were examined by total scores, response patterns, and confidence level. Relevant questions were evaluated for difficulty index, discriminatory index, and competence levels. RESULTS: Three of the five clusters can be seen as "performance" clusters: cluster 0 (n = 761) consisted predominantly of students close to graduation. Relevant questions tend to be difficult, but students answered confidently and correctly. Students in cluster 1 (n = 1,357) were advanced, cluster 3 (n = 1,453) consisted mainly of beginners. Relevant questions for these clusters were rather easy. The number of guessed answers increased. There were two "drop-out" clusters: students in cluster 2 (n = 384) dropped out of the test about halfway through after initially performing well; cluster 4 (n = 1,489) included students from the first semesters as well as "non-serious" students both with mostly incorrect guesses or no answers. CONCLUSION: Clusters placed performance in the context of participating universities. Relevant questions served as good cluster separators and further supported our "performance" cluster groupings.

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. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant-pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep-learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA- or enzyme-activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.

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). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.

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. Finally in the course of digitalization, digital plant collections receive more and more interest in schools and universities. RESULTS: We developed a freely available mobile application called Flora Capture allowing users to collect series of plant images from predefined perspectives. These images, together with accompanying metadata, are transferred to a central project server where each observation is reviewed and validated by a team of botanical experts. Currently, more than 4800 plant species, naturally occurring in the Central European region, are covered by the application. More than 200,000 images, depicting more than 1700 plant species, have been collected by thousands of users since the initial app release in 2016. CONCLUSION: Flora Capture allows experts, laymen and citizen scientists to collect a digital herbarium and share structured multi-modal observations of plants. Collected images contribute, e.g., to the training of plant identification algorithms, but also suit educational purposes. Additionally, presence records collected with each observation allow contribute to verifiable records of plant occurrences across the world.

Image-based classification of plant genus and family for trained and untrained plant species.

BACKGROUND: Modern plant taxonomy reflects phylogenetic relationships among taxa based on proposed morphological and genetic similarities. However, taxonomical relation is not necessarily reflected by close overall resemblance, but rather by commonality of very specific morphological characters or similarity on the molecular level. It is an open research question to which extent phylogenetic relations within higher taxonomic levels such as genera and families are reflected by shared visual characters of the constituting species. As a consequence, it is even more questionable whether the taxonomy of plants at these levels can be identified from images using machine learning techniques. RESULTS: Whereas previous studies on automated plant identification from images focused on the species level, we investigated classification at higher taxonomic levels such as genera and families. We used images of 1000 plant species that are representative for the flora of Western Europe. We tested how accurate a visual representation of genera and families can be learned from images of their species in order to identify the taxonomy of species included in and excluded from learning. Using natural images with random content, roughly 500 images per species are required for accurate classification. The classification accuracy for 1000 species amounts to 82.2% and increases to 85.9% and 88.4% on genus and family level. Classifying species excluded from training, the accuracy significantly reduces to 38.3% and 38.7% on genus and family level. Excluded species of well represented genera and families can be classified with 67.8% and 52.8% accuracy. CONCLUSION: Our results show that shared visual characters are indeed present at higher taxonomic levels. Most dominantly they are preserved in flowers and leaves, and enable state-of-the-art classification algorithms to learn accurate visual representations of plant genera and families. Given a sufficient amount and composition of training data, we show that this allows for high classification accuracy increasing with the taxonomic level and even facilitating the taxonomic identification of species excluded from the training process.

Automated plant species identification-Trends and future directions.

Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.

Recommending plant taxa for supporting on-site species identification.

BACKGROUND: Predicting a list of plant taxa most likely to be observed at a given geographical location and time is useful for many scenarios in biodiversity informatics. Since efficient plant species identification is impeded mainly by the large number of possible candidate species, providing a shortlist of likely candidates can help significantly expedite the task. Whereas species distribution models heavily rely on geo-referenced occurrence data, such information still remains largely unused for plant taxa identification tools. RESULTS: In this paper, we conduct a study on the feasibility of computing a ranked shortlist of plant taxa likely to be encountered by an observer in the field. We use the territory of Germany as case study with a total of 7.62M records of freely available plant presence-absence data and occurrence records for 2.7k plant taxa. We systematically study achievable recommendation quality based on two types of source data: binary presence-absence data and individual occurrence records. Furthermore, we study strategies for aggregating records into a taxa recommendation based on location and date of an observation. CONCLUSION: We evaluate recommendations using 28k geo-referenced and taxa-labeled plant images hosted on the Flickr website as an independent test dataset. Relying on location information from presence-absence data alone results in an average recall of 82%. However, we find that occurrence records are complementary to presence-absence data and using both in combination yields considerably higher recall of 96% along with improved ranking metrics. Ultimately, by reducing the list of candidate taxa by an average of 62%, a spatio-temporal prior can substantially expedite the overall identification problem.

Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton.

BACKGROUND: Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple environmental threats like eutrophication, climate warming and introduction of invasive species more intensive monitoring would be helpful to develop adequate measures. However, traditional methods such as microscopic counting by experts or high throughput flow cytometry based on scattering and fluorescence signals are either too time-consuming or inaccurate for species identification tasks. The combination of high qualitative microscopy with high throughput and latest development in machine learning techniques can overcome this hurdle. RESULTS: In this study, image based cytometry was used to collect ~ 47,000 images for brightfield and Chl a fluorescence at 60× magnification for nine common freshwater species of nano- and micro-phytoplankton. A deep neuronal network trained on these images was applied to identify the species and the corresponding life cycle stage during the batch cultivation. The results show the high potential of this approach, where species identity and their respective life cycle stage could be predicted with a high accuracy of 97%. CONCLUSIONS: These findings could pave the way for reliable and fast phytoplankton species determination of indicator species as a crucial step in water quality assessment.

Advance care planning, culture and religion: an environmental scan of Australian-based online resources.

Objectives Culture and religion are important in advance care planning (ACP), yet it is not well understood how this is represented in ACP online resources. The aim of the present study was to identify the availability of Australian-based ACP websites and online informational booklets containing cultural and religious information. Methods An environmental scanning framework was used with a Google search conducted from 30 June 2015 to 5 July 2015. Eligible Australian-based ACP websites and online informational booklets were reviewed by two analysts (APS & PM) for information pertaining to at least one culture or religion. Common characteristics were agreed upon and tabulated with narrative description. Results Seven Australian-based ACP websites were identified with varying degrees of cultural and religious information. Seven Australian-based ACP informational booklets were identified addressing culture or religion, namely of Aboriginal and Torres Strait Islander (n=5), Sikh (n=1) and Italian (n=1) communities. Twenty-one other online resources with cultural and religious information were identified, developed within the context of health and palliative care. Conclusions There is no comprehensive Australian-based ACP website or informational booklet supporting ACP across several cultural and religious contexts. Considering Australia's multicultural and multifaith population, such a resource may be beneficial in increasing awareness and uptake of ACP. What is known about the topic? Health professionals and consumers frequently use the Internet to find information. Non-regulation has resulted in the proliferation of ACP online resources (i.e. ACP websites and online informational booklets). Although this has contributed to raising awareness of ACP, the availability of Australian-based ACP online resources with cultural and religious information is not well known. What does this paper add? This paper is the first to use an environmental scanning methodology to identify Australian-based ACP websites and online informational booklets with cultural and religious information. What are the implications for practitioners? The results of this environmental scan present the availability of Australian-based ACP websites and online informational booklets containing cultural and religious information. A thorough understanding may assist in identifying gaps for future ACP project planning and policy objectives, consistent with meeting cultural and religious needs. This may be beneficial for health professionals, consumers, health associations, organisations and government policy makers concerned with ACP.

Religious leaders' perceptions of advance care planning: a secondary analysis of interviews with Buddhist, Christian, Hindu, Islamic, Jewish, Sikh and Bahá'í leaders.

BACKGROUND: International guidance for advance care planning (ACP) supports the integration of spiritual and religious aspects of care within the planning process. Religious leaders' perspectives could improve how ACP programs respect patients' faith backgrounds. This study aimed to examine: (i) how religious leaders understand and consider ACP and its implications, including (ii) how religion affects followers' approaches to end-of-life care and ACP, and (iii) their implications for healthcare. METHODS: Interview transcripts from a primary qualitative study conducted with religious leaders to inform an ACP website, ACPTalk, were used as data in this study. ACPTalk aims to assist health professionals conduct sensitive conversations with people from different religious backgrounds. A qualitative secondary analysis conducted on the interview transcripts focussed on religious leaders' statements related to this study's aims. Interview transcripts were thematically analysed using an inductive, comparative, and cyclical procedure informed by grounded theory. RESULTS: Thirty-five religious leaders (26 male; mean 58.6-years-old), from eight Christian and six non-Christian (Jewish, Buddhist, Islamic, Hindu, Sikh, Bahá'í) backgrounds were included. Three themes emerged which focussed on: religious leaders' ACP understanding and experiences; explanations for religious followers' approaches towards end-of-life care; and health professionals' need to enquire about how religion matters. Most leaders had some understanding of ACP and, once fully comprehended, most held ACP in positive regard. Religious followers' preferences for end-of-life care reflected family and geographical origins, cultural traditions, personal attitudes, and religiosity and faith interpretations. Implications for healthcare included the importance of avoiding generalisations and openness to individualised and/ or standardised religious expressions of one's religion. CONCLUSIONS: Knowledge of religious beliefs and values around death and dying could be useful in preparing health professionals for ACP with patients from different religions but equally important is avoidance of assumptions. Community-based initiatives, programs and faith settings are an avenue that could be used to increase awareness of ACP among religious followers' communities.

Derivatives of biarylalkyl carboxylic acid induce pleiotropic phenotypes in adult Schistosoma mansoni in vitro.

Schistosomes and other parasitic platyhelminths cause infectious diseases of worldwide significance for humans and animals. Despite their medical and economic importance, vaccines are not available and the number of drugs is alarmingly limited. For most platyhelminths including schistosomes, Praziquantel (PZQ) is the commonly used drug. With respect to its regular application in mass treatment programs, however, there is increasing concern about resistance development.Previous studies demonstrated that inhibitors used to treat non-parasitic human diseases may be useful to be tested for their effects on parasites. To this end, we focused on biarylalkyl carboxylic acids (BACAs) as basis, which had been shown before to be interesting candidates in the context of finding alternative approaches to treat diabetes mellitus. We tested 32 chemically modified derivatives of these substances (biarylalkyl carboxylic acid derivatives (BACADs)) for their effects on adult Schistosoma mansoni in vitro. Treatment with 18 BACADs resulted in egg production-associated phenotypes and reduced pairing stability. In addition, 12 of these derivatives affected vitality and/or caused severe tegument damage, gut dilatation, or other forms of tissue disintegration which led to the death of worms. In most cases (10/12), one derivative caused more than one phenotype at a time. In vitro experiments in the presence of serum albumin (SA) and alpha-acidic glycoprotein (AGP) indicated a varying influence of these blood components on the effects of two selected derivatives. The variety of observed phenotypes suggested that different targets were hit. The results demonstrated that BACADs are interesting substances with respect to their anti-schistosomal effects.

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. We aim to identify code change patterns in order to draw conclusions about the software development process. For this, we propose a novel way to calculate high-level file overarching diffs, and a novel way to parallelize pattern mining. In a study of 1000 Java projects, we mined and analyzed a total of 45,000 patterns. We present 13 patterns, showing extreme points of the 7 pattern categories we identified. We found that a large number of high-level change patterns exist and occur frequently. The majority of mined patterns were associated with a specific project and contributor, where and by whom it was more likely to be used. While a large number of different code change patterns are used, only a few, mostly unsurprising ones, are common under all circumstances. The majority of code change patterns are highly specific to different context factors that we further explore.

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. Secondly, we use a quantitative morphometric approach to infer the shape similarity of the generated simulations and real tree species. Within a functional-structural plant model, we implement a set of biological parameters that affect the branching architecture of trees. By modifying the parameter values, we aim to generate basic shapes of spruce, pine, oak and poplar. Tree shapes are compared using geometric morphometrics of landmarks that capture crown and stem outline shapes. Five biological parameters, namely xylem flow, shedding rate, proprioception, gravitysense and lightsense, most influenced the generated tree branching patterns. Adjusting these five parameters resulted in the different tree shapes of spruce, pine, oak, and poplar. The largest effect was attributed to gravity, as phenotypic responses to this effect resulted in different growth directions of gymnosperm and angiosperm branching architectures. Since we were able to obtain branching architectures that resemble real tree species by adjusting only a few biological parameters, our model is extendable to other tree species. Furthermore, the model will also allow the simulation of structural tree-environment interactions. Our simplifying approach to shape comparison between tree species, landmark geometric morphometrics, showed that even the crown-trunk outlines capture species differences based on their contrasting branching architectures.

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.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity.

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. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.

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. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics.

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. In this study, we explore the feasibility of using opportunistically captured plant observations, collected via the plant identification app Flora Incognita to determine the onset of flowering and, based on that, create interpolation maps comparable to those of the DWD. Therefore, the opportunistic observations of 17 species collected in 2020 and 2021 were assigned to "Flora Incognita stations" based on location and altitude in order to mimic the network of stations forming the data basis for the interpolation conducted by the DWD. From the distribution of observations, the percentile representing onset of flowering date was calculated using a parametric bootstrapping approach and then interpolated following the same process as applied by the DWD. Our results show that for frequently observed, herbaceous and conspicuous species, the patterns of onset of flowering were similar and comparable between both data sources. We argue that a prominent flowering stage is crucial for accurately determining the onset of flowering from opportunistic plant observations, and we discuss additional factors, such as species distribution, location bias and societal events contributing to the differences among species and phenology data. In conclusion, our study demonstrates that the phenological monitoring of certain species can benefit from incorporating opportunistic plant observations. Furthermore, we highlight the potential to expand the taxonomic range of monitored species for phenological stage assessment through opportunistic plant observation data.

In Vitro Activities of Dithiocarbamate Derivatives against Echinococcus multilocularis Metacestode Vesicles.

The metacestode stage of the fox tapeworm Echinococcus multilocularis 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 Schistosoma mansoni. As both parasites belong to the platyhelminths, here we investigated whether these compounds were also active against E. multilocularis metacestode vesicles in vitro. We used an in vitro drug-screening cascade for the identification of novel compounds against E. multilocularis metacestode vesicles with disulfiram and 51 dithiocarbamates. Five compounds showed activity against E. multilocularis metacestode vesicles after five days of drug incubation in a damage marker release assay. Structure-activity relationship analyses revealed that a S-2-hydroxy-5-nitro benzyl moiety was necessary for anti-echinococcal activity, as derivatives without this group had no effect on E. multilocularis metacestode vesicles. The five active compounds were further tested for potential cytotoxicity in mammalian cells. For two compounds with low toxicity (Schl-32.315 and Schl-33.652), IC50 values in metacestode vesicles and IC50 values in germinal layer cells were calculated. The compounds were not highly active on isolated GL cells with IC50 values of 27.0 ± 4.2 µM for Schl-32.315 and 24.7 ± 11.5 µM for Schl-33.652, respectively. Against metacestode vesicles, Schl-32.315 was not very active either with an IC50 value of 41.6 ± 3.2 µM, while Schl-33.652 showed a low IC50 of 4.3 ± 1 µM and should be further investigated in the future for its activity against alveolar echinococcosis.

Towards automated traceability maintenance.

Traceability relations support stakeholders in understanding the dependencies between artifacts created during the development of a software system and thus enable many development-related tasks. To ensure that the anticipated benefits of these tasks can be realized, it is necessary to have an up-to-date set of traceability relations between the established artifacts. This goal requires the creation of traceability relations during the initial development process. Furthermore, the goal also requires the maintenance of traceability relations over time as the software system evolves in order to prevent their decay. In this paper, an approach is discussed that supports the (semi-) automated update of traceability relations between requirements, analysis and design models of software systems expressed in the UML. This is made possible by analyzing change events that have been captured while working within a third-party UML modeling tool. Within the captured flow of events, development activities comprised of several events are recognized. These are matched with predefined rules that direct the update of impacted traceability relations. The overall approach is supported by a prototype tool and empirical results on the effectiveness of tool-supported traceability maintenance are provided.