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Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles. Scientific contribution This pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries. Graphical Abstract: (Figure presented.)

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.|Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.

This dataset contains tandem mass spectrometry data cleaned and processed from the publicly available GNPS Spectral Library. We aim to continuously update this dataset with new data points as the spectral libraries expand.

While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature. The Convolution vision Transformer (CvT) is being tested to evaluate vision Transformers that are currently achieving state-of-the-art results in many other vision tasks. CvT combines some of the advantages of convolution with the advantages of dynamic attention and global context fusion of Transformers. It performs worse than widely tested methods such as XGBoost and CNNs, but shows that Transformers have potential to improve yield prediction.

The floating phase, a critical incommensurate phase, has been theoretically predicted as a potential intermediate phase between crystalline ordered and disordered phases. In this study, we investigate the different quantum phases that arise in ladder arrays comprising up to 92 neutral-atom qubits and experimentally observe the emergence of the quantum floating phase. We analyze the site-resolved Rydberg state densities and the distribution of state occurrences. The site-resolved measurement reveals the formation of domain walls within the commensurate ordered phase, which subsequently proliferate and give rise to the floating phase with incommensurate quasi-long-range order. By analyzing the Fourier spectra of the Rydberg density-density correlations, we observe clear signatures of the incommensurate wave order of the floating phase. Furthermore, as the experimental system sizes increase, we show that the wave vectors approach a continuum of values incommensurate with the lattice. Our work motivates future studies to further explore the nature of commensurate-incommensurate phase transitions and their non-equilibrium physics., Comment: 27 pages, 21 figures

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Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.

Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8–23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0–1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut’s prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15–20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study show, Biomechatronics & Human-Machine Control

Explainability in yield prediction helps us fully explore the potential of machine learning models that are already able to achieve high accuracy for a variety of yield prediction scenarios. The data included for the prediction of yields are intricate and the models are often difficult to understand. However, understanding the models can be simplified by using natural groupings of the input features. Grouping can be achieved, for example, by the time the features are captured or by the sensor used to do so. The state-of-the-art for interpreting machine learning models is currently defined by the game-theoretic approach of Shapley values. To handle groups of features, the calculated Shapley values are typically added together, ignoring the theoretical limitations of this approach. We explain the concept of Shapley values directly computed for predefined groups of features and introduce an algorithm to compute them efficiently on tree structures. We provide a blueprint for designing swarm plots that combine many local explanations for global understanding. Extensive evaluation of two different yield prediction problems shows the worth of our approach and demonstrates how we can enable a better understanding of yield prediction models in the future, ultimately leading to mutual enrichment of research and application., Comment: Preprint accepted at IntelliSys 2023

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Contains the results of library cleaning by matchms library cleaning pipelines. ALL_GNPS_NO_PROPOGATED_21_08_2023.mgf library downloaded on 21-08-2023 from https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS_NO_PROPOGATED.mgf The 2 folders contain 2 different cleaning pipelines on the GNPS library. Each folder contains: A yaml file: This contains the settings used for this run. Should be used to rerun the pipeline with the same settings. A .py file: This was the script used to run the pipeline. Not recommended to reuse, instead use the yaml file. A .log file: Containing the log of the run. At the end the processing report is given. A .mgf file: The cleaned library spectra.

Spondylolysis is underdiagnosed and often missed in non-musculoskeletal abdominal CT imaging. Our aim was to assess the inter-reader agreement and diagnostic performance of a novel "Darth Vader sign" for the detection of spondylolysis in routine axial images. We performed a retrospective search in the institutional report archives through keyword strings for lumbar spondylolysis and spondylolisthesis. Abdominal CTs from 53 spondylolysis cases (41% female) and from controls (n = 6) without spine abnormalities were identified. A total of 139 single axial slices covering the lumbar spine (86 normal images, 40 with spondylolysis, 13 with degenerative spondylolisthesis without spondylolysis) were exported. Two radiology residents rated all images for the presence or absence of the "Darth Vader sign". The diagnostic accuracy for both readers, as well as the inter-reader agreement, was calculated. The "Darth Vader sign" showed an inter-reader agreement of 0.77. Using the "Darth Vader sign", spondylolysis was detected with a sensitivity and specificity of 65.0-88.2% and 96.2-99.0%, respectively. The "Darth Vader sign" shows excellent diagnostic performance at a substantial inter-reader agreement for the detection of spondylolysis. Using the "Darth Vader sign" in the CT reading routine may be an easy yet effective tool to improve the detection rate of spondylolysis in non-musculoskeletal cases and hence improve patient care.

Objectives: Automated sample delivery and laboratory acceptance systems (PTAS) may influence the hemolysis rate of blood samples due to g-forces, abrupt acceleration, and rapid deceleration. However, quantitative data regarding the rate of hemolysis in PTAS is limited. To fill this void, the effect of a pneumatic tube in combination with an acceptance system (PTAS) on the hemolysis rate was investigated in this study. Methods: Lithium heparin plasma tubes were transported from different clinical departments to the hospital’s laboratory (a) by employees or (b) with an automated PTAS and analyzed for the presence of hemolysis based on a hemolysis index (HI) of >25. Hemolysis indices of 68.513 samples were retrieved from the laboratory information system before and after installation of the PTAS and were subjected to statistical analysis. Results: A total of 32.614 samples were transported by employees, of which 3.815 samples (11.70%) were hemolytic, and 9.441 out of 35.899 samples delivered by PTAS (26.30%) were hemolytic. After the implementation of the PTAS, hemolysis rates increased in all departments. Conclusions: Automated PTAS are associated with increased hemolysis rates. This has implications for routine patient management and should be considered for the transportation of samples used for the determination of hemolysis-sensitive laboratory parameters.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development. Remote sensing satellite images provided by various satellites circumnavigating the world are a cheap and reliable way to obtain data for yield prediction. The field of yield prediction is currently dominated by Deep Learning approaches. While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods. The limitations can be overcome by proposing a pipeline to process remote sensing images into feature-based representations that allow the employment of Extreme Gradient Boosting (XGBoost) for yield prediction. A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning. Feature importances expose the near-infrared spectrum of light as an important feature within our models. The reported results hint at the capabilities of XGBoost for yield prediction and encourage future experiments with XGBoost for yield prediction on other crops in regions all around the world., Comment: 31 pages, 5 figures - Preprint submitted to Computers and Electronics in Agriculture

Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, reducing the features in the data to a smaller set is often of great utility. Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand. These scores can be based only on the distribution of variables and the quantification of their interactions. The previous literature, mainly investigating anomaly detection and clusters, fails to address the redundancy-elimination issue. We propose an evaluation of correlations among features to compute feature importance scores representing the contribution of single features in explaining the dataset's structure. Based on Coalitional Game Theory, our feature importance scores include a notion of redundancy awareness making them a tool to achieve redundancy-free feature selection. We show that the deriving features' selection outperforms competing methods in lowering the redundancy rate while maximizing the information contained in the data. We also introduce an approximated version of the algorithm to reduce the complexity of Shapley values' computations., Comment: To be published in DaWaK 2022 (accepted). Code available at https://github.com/chiarabales/unsupervised_sv @inproceedings{balestraUSV, author = {Chiara Balestra and Florian Huber and Andreas Mayr and Emmanuel M\"uller}, title = {Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data}, booktitle = {DaWaK 2022}, year = {2022}}

Background: Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review: We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review: This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities an

OBJECTIVES: To investigate whether dynamic contrast-enhanced (DCE)-MR bone perfusion could serve as surrogate for morphologic ultra-short echo time (UTE) bone images and to correlate perfusion with morphologic hallmarks in histologically proven foci of medication-related osteonecrosis of the jaw (MRONJ). METHODS: Retrospective study including 20 patients with established diagnosis of MRONJ. Qualitative consensus assessment of predefined jaw regions by two radiologists was used as reference standard using Likert scale (0-3) for standard imaging hallmarks in MRONJ (osteolysis, sclerosis, periosteal thickening). DCE-MRI measurements performed in corresponding regions of the mandible were then correlated with qualitative scores. Regions were grouped into "non-affected" and "pathologic" based on binarized Likert scores of different imaging hallmarks (0-1 vs 2-3). DCE-MRI measurements among hallmarks were compared using Mann-Whitney-U-testing. ROC (receiver-operating-characteristic) analysis was performed for each of the perfusion parameters to assess diagnostic performance for identification of MRONJ using morphologic ratings as reference standard. RESULTS: Median perfusion measurements of "pathologic" regions in wash-in, peak enhancement intensity and integrated area under the curve are significantly higher than those of "non-affected" regions, irrespective of reference imaging hallmark (p < 0.05). No significant perfusion differences were found between "pathologic" regions with and without osteolysis (p = 0.180). ROC analysis showed fair diagnostic performance of DCE-MRI parameters for identification of MRONJ (AUC 0.626-0.727). CONCLUSIONS: DCE bone perfusion parameters are significantly increased in MRONJ compared to non-affected regions, irrespective of osteolysis. Due to certain overlap DCE-MRI bone perfusion cannot serve as full surrogate for UTE bone imaging but may enhance reader confidence.

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

BACKGROUND Interstitial lung disease (ILD) is a frequent complication of systemic sclerosis (SSc), and its early detection and treatment may prevent deterioration of lung function. Different vendors have recently made larger image matrices available as a post-processing option for computed tomography (CT), which could facilitate the diagnosis of SSc-ILD. Therefore, the objective of this study was to assess the effect of matrix size on lung image quality in patients with SSc by comparing a 1024-pixel matrix to a standard 512-pixel matrix and applying different reconstruction kernels. METHODS Lung scans of 50 patients (mean age 54 years, range 23-85 years) with SSc were reconstructed with these two different matrix sizes, after determining the most appropriate kernel in a first step. Four observers scored the images on a five-point Likert scale regarding image quality and detectability of clinically relevant findings. RESULTS Among the eight tested kernels, the Br59-kernel (sharp) reached the highest score (19.48 ± 3.99), although differences did not reach statistical significance. The 1024-pixel matrix scored higher than the 512-pixel matrix HRCT overall (p = 0.01) and in the subcategories sharpness (p < 0.01), depiction of bronchiole (p < 0.01) and overall image impression (p < 0.01), and lower for the detection of ground-glass opacities (GGO) (p = 0.04). No significant differences were found for detection of extent of reticulations/bronchiectasis/fibrosis (p = 0.50) and image noise (p = 0.09). CONCLUSIONS Our results show that with the use of a sharp kernel, the 1024-pixel matrix HRCT, provides a slightly better subjective image quality in terms of assessing interstitial lung changes, whereby GGO are more visible on the 512-pixel matrix. However, it remains to be answered to what extent this is related to the improved representation of the smallest structures.

BACKGROUND Loeys-Dietz Syndrome is a rare connective tissue disorder that is associated with arterial pathologies such as aortic dissections, tortuosity and aneurysms.We present a child with Loeys-Dietz Syndrome type 2 that received total aortic and bilateral subclavian artery replacement. CASE REPORT A 9-year old boy with Loeys-Dietz Syndrome type 2 and acute type B aortic dissection received an urgent complete thoracic and thoraco-abdominal aortic repair within three days. First, the ascending aorta and aortic root were replaced in a Tirone David and Frozen Elephant Trunk procedure. Then, the descending and supramesenteric aorta was replaced by a Dacron interposition graft with direct implantation of the celiac trunk. During the 15 months follow-up, the patient required three more surgical interventions for rapid expanding aneurysms of both subclavian arteries and the infrarenal aorta. No major adverse event nor secondary interventions occurred. Ultrasonographic and magnetic resonance imaging follow-up is continued at 6-months intervals. CONCLUSION Children with Loeys-Dietz Syndrome may require extensive aortic repair for aortic dissection and show rapidly expanding aneurysms. Referral to a center with pediatric vascular expertise and long-term follow-up examinations are crucial.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

By extracting lignin, pulp production can be increased without heavy investments in a new recovery boiler, the typical bottleneck of a pulp mill. The extraction is performed by using 0.20 and 0.15 weight equivalents of CO2 and H2SO4 respectively. Herein, we describe lignin esterification with fatty acids using benign reagents to generate a lignin ester mixable with gas oils. The esterification is accomplished by activating the fatty acid and lignin with acetic anhydride which can be regenerated from the acetic acid recycled in this reaction. The resulting mass balance ratio is fatty acid/lignin/acetic acid (2 : 1 : 0.1). This lignin ester can be hydroprocessed to generate hydrocarbons in gasoline, aviation, and diesel range. A 300-hour continuous production of fuel was accomplished. By recirculating reagents from both the esterification step and applying a water gas shift reaction on off-gases from the hydroprocessing, a favorable overall mass balance is realized.

By extracting lignin, pulp production can be increased without heavy investments in a new recovery boiler, the typical bottleneck of a pulp mill. The extraction is performed by using 0.20 and 0.15 weight equivalents of CO2 and H2SO4 respectively. Herein, we describe lignin esterification with fatty acids using benign reagents to generate a lignin ester mixable with gas oils. The esterification is accomplished by activating the fatty acid and lignin with acetic anhydride which can be regenerated from the acetic acid recycled in this reaction. The resulting mass balance ratio is fatty acid/lignin/acetic acid (2 : 1 : 0.1). This lignin ester can be hydroprocessed to generate hydrocarbons in gasoline, aviation, and diesel range. A 300-hour continuous production of fuel was accomplished. By recirculating reagents from both the esterification step and applying a water gas shift reaction on off-gases from the hydroprocessing, a favorable overall mass balance is realized.

By extracting lignin, pulp production can be increased without heavy investments in a new recovery boiler, the typical bottleneck of a pulp mill. The extraction is performed by using 0.20 and 0.15 weight equivalents of CO2 and H2SO4 respectively. Herein, we describe lignin esterification with fatty acids using benign reagents to generate a lignin ester mixable with gas oils. The esterification is accomplished by activating the fatty acid and lignin with acetic anhydride which can be regenerated from the acetic acid recycled in this reaction. The resulting mass balance ratio is fatty acid/lignin/acetic acid (2 : 1 : 0.1). This lignin ester can be hydroprocessed to generate hydrocarbons in gasoline, aviation, and diesel range. A 300-hour continuous production of fuel was accomplished. By recirculating reagents from both the esterification step and applying a water gas shift reaction on off-gases from the hydroprocessing, a favorable overall mass balance is realized.

The effect of fracture geometry on bentonite erosion for a generic repository site in crystalline host rock environment was investigated by means of 2-d numerical simulations. Fracture geometry was varied systematically using random aperture normal distributions with a mean aperture of 1 mm and standard deviations between 0 and 0.7 mm, respectively. Moreover, two aperture correlation lengths (0.2 m and 2 m) were applied. Based on the synthetic fracture aperture fields generated the cubic law in conjunction with the Darcy equation is used to simulate fracture flow fields for mean flow velocities in the fracture between 1 x 10(-5) m/s and 1 x 10(-7) m/s. These flow fields are used in a two-way coupling approach to bentonite erosion simulations. The results of the study clearly show the influence of variable fracture aperture on bentonite erosion behaviour and erosion rates (kg/a). Increasing fracture aperture standard deviation leads to increasing heterogeneous flow velocity distributions governing the erosion behaviour and erosion rates. Calculated steady state erosion rates are in the range of similar to 0.25 kg/a down to similar to 0.014 kg/a. The highest erosion rate is calculated for the highest mean flow velocity in conjunction with the highest standard deviation. The effect of aperture heterogeneity diminishes for the lowest flow velocities. In summary, the results show the effect of fracture heterogeneity on bentonite erosion, especially for high to medium mean flow velocities combined with high to medium fracture heterogeneity under the model boundary conditions and model capabilities and limitations considered. An increase of up to g heterogeneous flow velocity distributions governing the erosion behaviour and erosion rates. Calculated steady state erosion rates are in the range of similar to 83% in erosion rate compared to the constant aperture case highlights the need to consider fracture aperture heterogeneity and its effect on the bentonite erosion in th, QC 20220301

By extracting lignin, pulp production can be increased without heavy investments in a new recovery boiler, the typical bottleneck of a pulp mill. The extraction is performed by using 0.20 and 0.15 weight equivalents of CO2 and H2SO4 respectively. Herein, we describe lignin esterification with fatty acids using benign reagents to generate a lignin ester mixable with gas oils. The esterification is accomplished by activating the fatty acid and lignin with acetic anhydride which can be regenerated from the acetic acid recycled in this reaction. The resulting mass balance ratio is fatty acid/lignin/acetic acid (2 : 1 : 0.1). This lignin ester can be hydroprocessed to generate hydrocarbons in gasoline, aviation, and diesel range. A 300-hour continuous production of fuel was accomplished. By recirculating reagents from both the esterification step and applying a water gas shift reaction on off-gases from the hydroprocessing, a favorable overall mass balance is realized.

Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain bioc

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain bioc

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

By extracting lignin, pulp production can be increased without heavy investments in a new recovery boiler, the typical bottleneck of a pulp mill. The extraction is performed by using 0.20 and 0.15 weight equivalents of CO2 and H2SO4 respectively. Herein, we describe lignin esterification with fatty acids using benign reagents to generate a lignin ester mixable with gas oils. The esterification is accomplished by activating the fatty acid and lignin with acetic anhydride which can be regenerated from the acetic acid recycled in this reaction. The resulting mass balance ratio is fatty acid/lignin/acetic acid (2 : 1 : 0.1). This lignin ester can be hydroprocessed to generate hydrocarbons in gasoline, aviation, and diesel range. A 300-hour continuous production of fuel was accomplished. By recirculating reagents from both the esterification step and applying a water gas shift reaction on off-gases from the hydroprocessing, a favorable overall mass balance is realized.

By extracting lignin, pulp production can be increased without heavy investments in a new recovery boiler, the typical bottleneck of a pulp mill. The extraction is performed by using 0.20 and 0.15 weight equivalents of CO2 and H2SO4 respectively. Herein, we describe lignin esterification with fatty acids using benign reagents to generate a lignin ester mixable with gas oils. The esterification is accomplished by activating the fatty acid and lignin with acetic anhydride which can be regenerated from the acetic acid recycled in this reaction. The resulting mass balance ratio is fatty acid/lignin/acetic acid (2 : 1 : 0.1). This lignin ester can be hydroprocessed to generate hydrocarbons in gasoline, aviation, and diesel range. A 300-hour continuous production of fuel was accomplished. By recirculating reagents from both the esterification step and applying a water gas shift reaction on off-gases from the hydroprocessing, a favorable overall mass balance is realized.

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model’s prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a

In the version of this Comment initially published, an error appeared in the “Specificity” equation displayed in the middle-right panel of Fig. 2. Originally reading “ fp/fp+tn”, the equation has been corrected to read: “ tn/tn+fp”. The error has been corrected in the online version of the Article. *A list of authors and their affiliations appears online., SCOPUS: no.j, SCOPUS: er.j, info:eu-repo/semantics/published

Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns frag-mental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/ MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.

Aim of this work is to gain a comprehensive understanding of the effects of an increasing I3-phase stability of Tialloys on the L-PBF processing behavior. For this purpose, seven different Ti-alloys with an increasing concentration of the I3-phase stabilizing elements Fe and V were prepared by L-PBF and in-situ alloy formation. The Molybdenum equivalent (Moeq) of the examined alloys, as a measure of I3-phase stability, was varied systematically between -3.3 and 25. It is shown that a homogeneous distribution of elements is achievable by in-situ alloying. The experiments prove that the investigated alloys can be processed by a single L-PBF parameter set with high relative density above 99.8%. This finding is substantiated by calculated thermo-physical material properties and an analytical model. To understand the underlying metallurgical effects governing the L-PBF results, the samples were investigated extensively by EDS, EBSD, XRD, light microscopy and compression tests. The I3-phase fraction varies in dependence of the Moeq between 0% and 99%. Because of rapid solidification inherent in L-PBF a Moeq of 10 is sufficient to receive more than 90% I3-phase. The same amount of I3-phase after furnace cooling was only observed in alloys with Moeq of 20 or more. While all alloy compositions can be processed with high relative density of over 99.8%, alloys with a Moeq between 15 and 20 show a brittle material behavior in as-built state, resulting in cracking during L-PBF. This behavior is attributed to the formation of co-phase during L-PBF. In contrast, the highest I3-stabilized alloy with a nominal Moeq of 25 exhibits a very high ductility with a fracture strain exceeding 50%.

BACKGROUND CT artifacts induced by orthopedic implants can limit image quality and diagnostic yield. As a number of different strategies to reduce artifact extent exist, the aim of this study was to systematically compare ex vivo the impact of different CT metal artifact reduction (MAR) strategies on spine implants made of either standard titanium or carbon-fiber-reinforced-polyetheretherketone (CFR-PEEK). METHODS Spine surgeons fluoroscopically-guided prepared six sheep spine cadavers with pedicle screws and rods of either titanium or CFR-PEEK. Samples were subjected to single- and dual-energy (DE) CT-imaging. Different tube voltages (80, DE mixed, 120 and tin-filtered 150 kVp) at comparable radiation dose and iterative reconstruction versus monoenergetic extrapolation (ME) techniques were compared. Also, the influence of image reconstruction kernels (soft vs. bone tissue) was investigated. Qualitative (Likert scores) and quantitative parameters (attenuation changes induced by implant artifact, implant diameter and image noise) were evaluated by two independent radiologists. Artifact degree of different MAR-strategies and implant materials were compared by multiple ANOVA analysis. RESULTS CFR-PEEK implants induced markedly less artifacts than standard titanium implants (p < .001). This effect was substantially larger than any other tested MAR technique. Reconstruction algorithms had small impact in CFR-PEEK implants and differed significantly in MAR efficiency (p < .001) with best MAR performance for DECT ME 130 keV (bone kernel). Significant differences in image noise between reconstruction kernels were seen (p < .001) with minor impact on artifact degree. CONCLUSIONS CFR-PEEK spine implants induce significantly less artifacts than standard titanium compositions with higher MAR efficiency than any alternate scanning or image reconstruction strategy. DECT ME 130 keV image reconstructions showed least metal artifacts. Reconstruction kernels primarily modulate image

OBJECTIVES: The aims of this study were to test whether spectral photon-counting radiography (SPCR) is able to identify and distinguish different crystals associated with arthropathies in vitro and to validate findings in a gouty human third toe ex vivo. MATERIALS AND METHODS: Industry-standard calibration rods of calcium pyrophosphate, calcium hydroxyapatite (HA), and monosodium urate (MSU) were scanned with SPCR in an experimental setup. Each material was available at 3 different concentrations, and a dedicated photon-counting detector was used for SPCR, whereas validation scans were obtained on a clinical dual-energy computed tomography (DECT) scanner. Regions of interest were placed on SPCR images and consecutive DECT images to measure x-ray attenuation characteristics, including effective atomic numbers (Zeff). Statistical tests were performed for differentiation of Zeff between concentrations, materials, and imaging modalities. In addition, a third toe from a patient with chronic gouty arthritis was scanned with SPCR and DECT for differentiation of MSU from HA. RESULTS: In both SPCR and DECT, significant differences in attenuation and Zeff values were found for different concentrations among (P < 0.001) and between different materials (P < 0.001). Overall, quantitative measurements of Zeff did not differ significantly between SPCR- and DECT-derived measurements (P = 0.054-0.412). In the human cadaver toe, gouty bone erosions were visible on standard grayscale radiographic images; however, spectral image decomposition revealed the nature and extent of MSU deposits and was able to separate it from bone HA by Zeff. CONCLUSIONS: Identification and differentiation of different crystals related to arthropathies are possible with SPCR at comparable diagnostic accuracy to DECT. Further research is needed to assess diagnostic accuracy and clinical usability in vivo.

Large MS/MS dataset build from data that was obtained from GNPS (accessed on 2020-05-11): https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS.json The data was cleaned and pre-processed using notebooks provided here: https://github.com/iomega/spec2vec_gnps_data_analysis/tree/master/notebooks ● 112,956 positive ionmode spectra ● metadata was cleaned and corrected using matchms (https://github.com/matchms/matchms) and lookup routines using PubChem ● 92,954 of the spectra have Smiles and InchiKey (13717 unique InchiKey in first 14 characters)

Lesen und Erzählen gehören zu den elementaren Kulturtechniken des Menschen. Dieses Buch bringt beide Perspektiven zusammen und zeigt, wie Leser ihre individuellen Lebensgeschichten über die Rezeption von Literatur verhandeln und sich dabei im Spiegel literarischer Texte selbst entwerfen. Damit wird zugleich eine Einführung in die aktuelle Identitätsforschung gegeben. Zudem wird deutlich, dass Lesen - und vielmehr noch die Reflexion bewegender Leseerfahrungen - gerade in einer Zeit, in der biographische Selbstentwürfe zunehmend vor neue Herausforderungen gestellt werden, eine bedeutsame Ressource für die Verhandlung der eigenen Identität darstellt.

Collection of mass spectral data, downloaded from GNPS on 2020-05-11 via https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS.json Contains 154,820 spectra.

In this paper, we propose a novel approach to solve the 3D non-rigid registration problem from RGB images using Convolutional Neural Networks (CNNs). Our objective is to find a deformation field (typically used for transferring knowledge between instances, e.g., grasping skills) that warps a given 3D canonical model into a novel instance observed by a single-view RGB image. This is done by training a CNN that infers a deformation field for the visible parts of the canonical model and by employing a learned shape (latent) space for inferring the deformations of the occluded parts. As result of the registration, the observed model is reconstructed. Because our method does not need depth information, it can register objects that are typically hard to perceive with RGB-D sensors, e.g. with transparent or shiny surfaces. Even without depth data, our approach outperforms the Coherent Point Drift (CPD) registration method for the evaluated object categories., Comment: Accepted final version. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Mass spectrometry data is at the heart of numerous applications in the biomedical and lifesciences. With growing use of high-throughput techniques, researchers need to analyze largerand more complex datasets. In particular through joint effort in the research community,fragmentation mass spectrometry datasets are growing in size and number. Platforms such asMassBank (Horai et al., 2010), GNPS (Wang et al., 2016) or MetaboLights (Haug et al., 2020)serve as an open-access hub for sharing of raw, processed, or annotated fragmentation massspectrometry data. Without suitable tools, however, exploitation of such datasets remainsoverly challenging. In particular, large collected datasets contain data acquired using differentinstruments and measurement conditions, and can further contain a significant fraction ofinconsistent, wrongly labeled, or incorrect metadata (annotations).