Automatic Enrichment Research Articles

Continuous chromatography system with 6-zone and 18-column dynamic tandem connection technique for the enrichment of total flavonoids from Epimedium koreanum Nakai.

Natural flavonoids have been shown to have many pharmacological activities. Efficient and continuous enrichment of total flavonoids with high content and low mobile phase usage from complex natural products is greatly needed at the moment. In this study, a new continuous chromatography system (CCS) with 6 zones and the18-column dynamic tandem connection technique was developed and used to enrich total flavonoids from Epimedium koreanum Nakai (EKN). The 18 columns were divided into 6 zones, and the principle of adynamic series of three columns was adopted for each zone to achieve continuous automatic separation and enrichment of total flavonoids under the control of alogic control valve. The CCS separation conditions were established based on single-column chromatography anda theoretical calculation model of the CCS. By means of the self-designed device and method, 485.11±3.16 g of total flavonoids were isolated from 16.2 kg of EKN. It is worth noting that the total content of 18 types of flavonoids in the samples enriched by the CCS was increased from 2.84±0.07% to 88.29±0.22%, the total recovery rate was 92.20±0.38%, and theRSD of each flavonoid was less than 5.0%. Furthermore, compared with single-column chromatography filled with the same volume of chromatography filler, the entire process saved about 2/3 of the mobile phase usage. In summary, the developed device and method could efficiently and continuously enrich total flavonoids from EKN with high-content and low mobile phase usage and would have a wide application prospect in the separation and enrichment of natural products.

Detector upgrade for 222Rn concentration in high purity nitrogen measurement

To ensure the low background of the Jiangmen Underground Neutrino Observatory (JUNO) detector, high purity nitrogen (HPN) is used as a cover gas in direct contact with the liquid scintillator, which serves as the target material for neutrinos. The radon concentration in this nitrogen should be less than 10 μBq/m3. Given the background of the old detector, which is 2.20 ± 0.64 mBq/m3, combined with its enrichment efficiency of 51.0 % at a flow rate of 25 standard liters per minute (SLM), the effective measurement of radon concentrations below 10 μBq/m3 in HPN is a major challenge. A highly sensitive radon detector was upgraded to measure the radon concentration in HPN. A low background radon detection chamber was constructed based on the method of electrostatic collection and electrolytic polishing of low background steel with an internal surface roughness of ≤ 0.2 μm. The background of the radon detection chamber was reduced to 1.47 ± 0.19 mBq/m3. An automatic enrichment system based on 472 g low background activated carbon was developed. The introduction of a large amount of activated carbon has led to a significant increase in the enrichment flow rate to 200 SLM. In addition, the enrichment efficiency of the system simultaneously improved to 71 %. The relationship between the collection efficiency of the detection chamber and the applied voltage was calibrated. The result measured with this detector, shows that the radon concentration in HPN is 2.38 ± 0.30 μBq/m3.

Doc‐KG: Unstructured documents to knowledge graph construction, identification and validation with Wikidata

AbstractThe exponential growth of textual data in the digital era underlines the pivotal role of Knowledge Graphs (KGs) in effectively storing, managing, and utilizing this vast reservoir of information. Despite the copious amounts of text available on the web, a significant portion remains unstructured, presenting a substantial barrier to the automatic construction and enrichment of KGs. To address this issue, we introduce an enhanced Doc‐KG model, a sophisticated approach designed to transform unstructured documents into structured knowledge by generating local KGs and mapping these to a target KG, such as Wikidata. Our model innovatively leverages syntactic information to extract entities and predicates efficiently, integrating them into triples with improved accuracy. Furthermore, the Doc‐KG model's performance surpasses existing methodologies by utilizing advanced algorithms for both the extraction of triples and their subsequent identification within Wikidata, employing Wikidata's Unified Resource Identifiers for precise mapping. This dual capability not only facilitates the construction of KGs directly from unstructured texts but also enhances the process of identifying triple mentions within Wikidata, marking a significant advancement in the domain. Our comprehensive evaluation, conducted using the renowned WebNLG benchmark dataset, reveals the Doc‐KG model's superior performance in triple extraction tasks, achieving an unprecedented accuracy rate of 86.64%. In the domain of triple identification, the model demonstrated exceptional efficacy by mapping 61.35% of the local KG to Wikidata, thereby contributing 38.65% of novel information for KG enrichment. A qualitative analysis based on a manually annotated dataset further confirms the model's excellence, outshining baseline methods in extracting high‐fidelity triples. This research embodies a novel contribution to the field of knowledge extraction and management, offering a robust framework for the semantic structuring of unstructured data and paving the way for the next generation of KGs.

Use of organic material provided by an automatic enrichment device by weaner pigs and its influence on tail lesions.

Providing pigs with organic enrichment material is important for satisfying pigs' natural explorative behavior to prevent injurious tail biting and thus increase animal welfare in general. The aim of this study was to investigate the effects of automatically supplied enrichment material of three different types (alfalfa pellets, oat bran pellets, or a mixture of both) and different enrichment frequencies (2, 4, or 6 supplies/day) on the behavior, the occurrence of tail biting, and daily weight gains of weaner pigs. The results showed significant effects and interactions of enrichment material, frequency and the time of day on the exploratory behavior, the occurrence of tail biting, and daily weight gains. Higher probabilities for pigs using the enrichment material were observed for groups provided with only two supplies/day or receiving oat bran pellets. Additionally, more pigs explored the material when supplied in the afternoon compared to the morning. Tail lesions began to increase in week 4 of the rearing period. Higher probabilities of having a tail lesion were recorded in groups provided with two supplies/day compared to four or six supplies per day. Furthermore, the highest probabilities for pigs having a tail length loss at the end of the rearing period were shown by groups receiving two supplies/day, with 0.170 for alfalfa pellets, 0.342 for mixture, and 0.486 for oat bran pellets. For daily weight gains, only alfalfa groups differed significantly from mixture groups in the case of two supplies/day. No differences were observed for the other factor combinations. These results showed the potential of an automatic enrichment device supporting pigs in performing their natural exploratory behavior in a conventional housing system. Higher numbers of daily enrichment supplies show beneficial effects to reduce the occurrence of tail biting and tail length losses.

Novel fully automated and parallel gas assisted dynamic accelerated solvent extractor and parallel solvent evaporator for analysis of solid and semi-solid samples

The extraction of non-volatile and semi-volatile analytes from solid and semisolid samples has been primarily carried out via heated and/or pressurized liquid extraction mechanisms. Although analyte extraction and concentration processes have significantly evolved and currently several automated solutions are commercially available, these two steps are carried out independently. To the best of our knowledge, human intervention is always required throughout the entire process for sample extract manipulation/transportation among instruments/processes. Expectedly, excessive sample handling throughout the analytical workflow contributes to an increase in the analysis cost, the loss of analyte (s), and numerous potential analytical errors. Herein, we present the first fully automated sample-to-vial solution for analysis of non-volatile and semi-volatile compounds from solid and semisolid samples. This technological development, which is based on gas assisted dynamic accelerated solvent extraction (GA-dASE) and an integrated level-sensing system that controls the endpoint of the evaporation step, allows for fully automated analyte extraction and analyte enrichment. As a proof of concept, we applied this fully automatic extraction and enrichment system towards the quantitative determination of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCPs) in soil samples. Our results showed that GA-dASE not only matched the performance of the legacy accelerated solvent extraction (ASE), in terms of analyte recovery and reproducibility, but also delivered nearly 3-time reduction in labor per sample. Furthermore, our experiments demonstrated the capability of the instrument to perform fully automated extraction and evaporation steps without human intervention and with no impact on data quality (Relative Standard Deviation, RSD, ≤ 20%). In terms of interlaboratory reproducibility (n = 2), our results showed comparable results for the determination of PAHs using either 10- or 100-mL sample cells.

Automatic Extraction of Specimens from Multi-specimen Herbaria

Since herbarium specimens are increasingly becoming digitized and accessible in online repositories, an important need has emerged to develop automated tools to process and enrich these collections to facilitate better access to the preserved archives. Particularly, automatic enrichment of multi-specimen herbaria sheets poses unique challenges and problems that have not been adequately addressed. The complexity of localization of species in a page increases exponentially when multiple specimens are present in the same page. This already challenges the performance of models that work accurately with single specimens. Therefore, in this work, we have performed experiments to identify the models that perform well for the plant specimen localization problem. The major bottleneck for performing such experiments was the lack of labeled data. We also address this problem by proposing tools and algorithms to semi-automatically generate annotations for herbarium images. Based on our experiments, segmentation models perform much better than detection models for the task of plant localization. Our binary segmentation model can accurately extract specimens from the background and achieves an F1 score of 0.977. The ablation experiments for multi-specimen instance segmentation show that our proposed augmentation method provides a 38% increase in performance (0.51 mAP@0.9 versus 0.37) on a dataset of 1,500 plant instances.

Influence of an Automatic Enrichment Device on Laying Hen Behavior and Plumage Condition.

Feather pecking and cannibalism are prominent problems in modern laying hen husbandry. Among the various approaches to address this issue, environmental enrichment plays a crucial role. In this on-farm study, four winter gardens of an organic farm henhouse were equipped with an automatic enrichment device. Different quantities of downpipes dosing grain on rough-coated pecking plates (PPs) were tested. One group served as a control (CG) without an automatic enrichment device, while the others were offered different numbers of PPs, with one equipped with a doubled amount of PPs (DEG) compared to the other two groups (SEG). Video analyses of the hens' duration of stay and behaviors in the recorded winter garden area and around the PPs were performed, and regular assessments of the plumage condition were conducted. By the end of the production cycle, no hens with intact plumage were found, with hens in the CG and DEG showing worse scores and earlier deterioration in the plumage condition than in the SEG. The offer of PPs showed a significant influence on the duration of stay in the filmed area. Hens in the DEG stayed significantly longer (mean: 129 s, SD: 126 s) than those in the CG (mean: 79 s, SD: 91 s; p < 0.05) and SEG (mean: 75 s, SD: 83 s; p < 0.005). On the performed behavioral bouts per hen and minute (CG mean bouts/minute (SD): 5.47 (2.92); SEG mean (SD): 5.33 (2.76); SEG mean (SD): 5.81 (3.24)), no significant influences were detected. Environmental pecking was the behavior most frequently observed in all winter gardens, where, particularly around the PPs, pecking at the device was observed. Therefore, the enrichment device can be assessed as well accepted by the hens in winter gardens. The effect of the device on the plumage condition remains unclear, with external factors probably showing a greater influence than the enrichment.

Sequence-constrained multitask horizon tracking

Deep learning-based automatic horizon tracking has achieved promising results but still faces serious cross-horizon phenomena. Therefore, based on multitask learning, seismic data characteristics, and the seismic horizon sequence (or position) relationship, we have developed a sequence-constrained multitask horizon tracking (SMHT) method for high-precision automatic horizon tracking. SMHT contains the horizon label automatic enrichment, the multitask horizon tracking network (MHTN), and the horizon sequence-constrained loss function. Horizon label automatic enrichment aims to automatically generate the upper and lower auxiliary horizon labels of the target horizon label, and then the horizon region labels corresponding to the auxiliary and target horizon labels for MHTN. MHTN contains the shared layer, the auxiliary task, the main task, and the horizon sequence-constrained horizon correction. In MHTN, the shared layer generates multiscale feature maps of the input seismic data. The auxiliary task uses the concept of object detection to extract the horizon region (or probability), and the main task extracts the high-precision horizon within the extracted horizon region. The horizon sequence-constrained horizon correction with the horizon sequence-constrained loss function aims to avoid the cross-horizon phenomenon and finally obtain precise horizon tracking results. Application of MHTN to two field 3D seismic data sets finds that SMHT performs well in automatic horizon tracking.

A new entropy-based heuristic for automatic enrichment of ontologies

A new entropy-based heuristic for automatic enrichment of ontologies

A new entropy-based heuristic for automatic enrichment of ontologies

A new entropy-based heuristic for automatic enrichment of ontologies

Isolation of TTF-1 Positive Circulating Tumor Cells for Single-Cell Sequencing by Using an Automatic Platform Based on Microfluidic Devices

Single-cell sequencing provides promising information in tumor evolution and heterogeneity. Even with the recent advances in circulating tumor cell (CTC) technologies, it remains a big challenge to precisely and effectively isolate CTCs for downstream analysis. The Cell RevealTM system integrates an automatic CTC enrichment and staining machine, an AI-assisted automatic CTC scanning and identification system, and an automatic cell picking machine for CTC isolation. H1975 cell line was used for the spiking test. The identification of CTCs and the isolation of target CTCs for genetic sequencing were performed from the peripheral blood of three cancer patients, including two with lung cancer and one with both lung cancer and thyroid cancer. The spiking test revealed a mean recovery rate of 81.81% even with extremely low spiking cell counts with a linear relationship between the spiked cell counts and the recovered cell counts (Y = 0.7241 × X + 19.76, R2 = 0.9984). The three cancer patients had significantly higher TTF-1+ CTCs than healthy volunteers. All target CTCs were successfully isolated by the Cell Picker machine for a subsequent genetic analysis. Six tumor-associated mutations in four genes were detected. The present study reveals the Cell RevealTM platform can precisely identify and isolate target CTCs and then successfully perform single-cell sequencing by using commercially available genetic devices.

Architecture for the Automatic Creation and Enrichment of Ontologies from Linked Data

Architecture for the Automatic Creation and Enrichment of Ontologies from Linked Data

A Place to Be? Use of Winter Gardens with and without Automatic Enrichment Devices by Laying Hens

Since the abolition of beak trimming in laying hens in Germany, the importance of adequate enrichment material to help reduce feather pecking and cannibalism has grown. Here we tested an automatic enrichment device dosing grain via rough-coated pecking plates (PPs) on an organic farm, comparing its offer in four winter gardens (WGs). Winter garden (WG) 1 served as the control area without an automatic enrichment device, while WGs 2–4 offered different quantities of PPs, with WG 2 offering double the amount of PPs as WGs 3 and 4. The number of laying hens (Lohmann Brown Lite) per m² and close to the enrichment device (one hen‘s body length around) was determined using photo records. The usage behavior of the hens differed in the four WGs and with the animals’ age. Over the whole husbandry period (60 weeks), on average, 1.48 hens/m² were detected in the control area (WG 1), and a mean of 2.27 hens/m² in the enriched WGs. Compared to WG 1, the number of hens per m² was higher in WG 2 (2.43 hens/m²) and WG 3 (2.59 hens/m²) (p &lt; 0.05), but similar in WG 4 (1.79 hens/m²). At the end of the husbandry period, fewer animals (mean of all WGs: 1.43 hens/m²) used the WGs than from beginning to the middle of the laying period (mean of all WGs: 2.05–2.15 hens/m²; p &lt; 0.05). Our data indicate that the automatic enrichment device positively influences the animals’ use of the WG.

ReString: an open-source Python software to perform automatic functional enrichment retrieval, results aggregation and data visualization

Functional enrichment analysis is an analytical method to extract biological insights from gene expression data, popularized by the ever-growing application of high-throughput techniques. Typically, expression profiles are generated for hundreds to thousands of genes/proteins from samples belonging to two experimental groups, and after ad-hoc statistical tests, researchers are left with lists of statistically significant entities, possibly lacking any unifying biological theme. Functional enrichment tackles the problem of putting overall gene expression changes into a broader biological context, based on pre-existing knowledge bases of reference: database collections of known expression regulation, relationships and molecular interactions. STRING is among the most popular tools, providing both protein–protein interaction networks and functional enrichment analysis for any given set of identifiers. For complex experimental designs, manually retrieving, interpreting, analyzing and abridging functional enrichment results is a daunting task, usually performed by hand by the average wet-biology researcher. We have developed reString, a cross-platform software that seamlessly retrieves from STRING functional enrichments from multiple user-supplied gene sets, with just a few clicks, without any need for specific bioinformatics skills. Further, it aggregates all findings into human-readable table summaries, with built-in features to easily produce user-customizable publication-grade clustermaps and bubble plots. Herein, we outline a complete reString protocol, showcasing its features on a real use-case.

The Austrian Semantic EO Data Cube Infrastructure

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes.

An Approach for Automatic Ontology Enrichment from Texts

The automatic ontology enrichment consists of automatic knowledge extraction from texts related to a domain of discourse in the aim to enrich automatically an initial ontology of the same domain. However, the passage, from a plain text to an enriched ontology requires a number of steps. In this paper, we present a three steps ontology enrichment approach. In the first step, we apply natural language processing techniques to obtain tagged sentences. The second step allows us to reduce each extracted sentence to an SVO (Subject, Verb, and Object) sentence, supposed to preserve main information carried by the original sentence(s) from which it is extracted. Finally, in the third step, we proceed to enrich an initial ontology built manually by adding extracted terms in the generated SVO as new concepts or instances of concepts and new relations. To validate our approach, we have used “Phytotherapy" domain because of the availability of related texts on the WWW and also because its usefulness for pharmaceutical industry. The first results obtained, after experiments on a set of different texts, testify the performance of the proposed approach.

French FastContext: A publicly accessible system for detecting negation, temporality and experiencer in French clinical notes

The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes.

Automatic Structuring of Ontology Terms Based on Lexical Granularity and Machine Learning: Algorithm Development and Validation.

BackgroundAs the manual creation and maintenance of biomedical ontologies are labor-intensive, automatic aids are desirable in the lifecycle of ontology development.ObjectiveProvided with a set of concept names in the Foundational Model of Anatomy (FMA), we propose an innovative method for automatically generating the taxonomy and the partonomy structures among them, respectively.MethodsOur approach comprises 2 main tasks: The first task is predicting the direct relation between 2 given concept names by utilizing word embedding methods and training 2 machine learning models, Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory Networks (Bi-LSTM). The second task is the introduction of an original granularity-based method to identify the semantic structures among a group of given concept names by leveraging these trained models.ResultsResults show that both CNN and Bi-LSTM perform well on the first task, with F1 measures above 0.91. For the second task, our approach achieves an average F1 measure of 0.79 on 100 case studies in the FMA using Bi-LSTM, which outperforms the primitive pairwise-based method.ConclusionsWe have investigated an automatic way of predicting a hierarchical relationship between 2 concept names; based on this, we have further invented a methodology to structure a group of concept names automatically. This study is an initial investigation that will shed light on further work on the automatic creation and enrichment of biomedical ontologies.

From electronic health records to terminology base: A novel knowledge base enrichment approach

Enriching terminology base (TB) is an important and continuous process, since formal term can be renamed and new term alias emerges all the time. As a potential supplementary for TB enrichment, electronic health record (EHR) is a fundamental source for clinical research and practise. The task to align the set of external terms in EHRs to TB can be regarded as entity alignment without structure information. Conventional approaches mainly use internal structural information of multiple knowledge bases (KBs) to map entities and their counterparts among KBs. However, the external terms in EHRs are independent clinical terms, which lack of interrelations. To achieve entity alignment in this case, we proposed a novel automatic TB enrichment approach, named semantic & structure embeddings-based relevancy prediction (S2ERP). To obtain the semantic embedding of external terms, we fed them with formal entity into a pre-trained language model. Meanwhile, a graph convolutional network was used to obtain the structure embeddings of the synonyms and hyponyms in TB. Afterwards, S2ERP combines both embeddings to measure the relevancy. Experimental results on clinical indicator TB, collected from 38 top-class hospitals of Shanghai Hospital Development Center, showed that the proposed approach outperforms baseline methods by 14.16% in Hits@1.

Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning.

We present a method for real-time visualization and automatic processing for detection and classification of untreated cancer cells in blood during stain-free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating tumor cells in the blood, following an initial label-free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing the quantitative optical thickness profiles of the cells during flow. We automatically classified primary and metastatic colon cancer cells, where the two types of cancer cells were isolated from the same individual, as well as four types of blood cells. We used low-coherence off-axis interferometric phase microscopy and a microfluidic channel to image cells during flow quantitatively. The acquired images were processed and classified based on their morphology and quantitative phase features during the cell flow. We achieved high accuracy of 92.56% for distinguishing between the cells, enabling further automatic enrichment and cancer-cell grading from blood. © 2020 International Society for Advancement of Cytometry.