Miracle-dz/UniFAR-FTUs huggingface dataset:
OCR-D kompakt: Ergebnisse und Stand der Forschung in der Förderinitiative

Zusammenfassung Bereits seit einigen Jahren werden große Anstrengungen unternommen, um die im deutschen Sprachraum erschienenen Drucke des 16. –18. Jahrhunderts zu erfassen und zu digitalisieren. Deren Volltexttransformation konzeptionell und technisch vorzubereiten, ist das übergeordnete Ziel des DFG-Projekts OCR-D, das sich mit der Weiterentwicklung von Verfahren der Optical Character Recognition befasst. Der Beitrag beschreibt den aktuellen Entwicklungsstand der OCR-D-Software und analysiert deren erste Teststellung in ausgewählten Bibliotheken.
Protective effects of Antrodia camphorata extract against hypoxic cell injury and ischemic stroke brain damage

Ischemic stroke is the most prevalent stroke condition in the world resulted in either a transient ischemic attack or long‐lasting neurological problems due to the interrupted or reduced blood flow to the brain. Antrodia camphorata is a well‐known medicinal mushroom native to Taiwan and is familiar due to its medicinal effects. The current study investigated the protective effect of A. camphorata‐alcohol extracts (AC‐AE) against cobalt (II) chloride (CoCl2)‐induced oxidative stress in vitro and ischemia/reperfusion‐induced brain injury in vivo. The rats were pre‐treated with AC‐AE for 4 weeks. Our results showed that AC‐AE reduced cell damage and decreased reactive oxygen species (ROS) production in C6 and PC12 cells under CoCl2‐induced hypoxic condition. AC‐AE doses (385, 770, 1,540 mg/kg/day, 4 weeks) increased nuclear factor erythroid 2–related factor 2 (Nrf2) and heme oxygenase‐1 (HO‐1) mRNA expressions and decreased inducible nitric oxide synthase (iNOS) and cyclooxygenase‐2 (COX‐2) mRNA expressions in Sprague Dawley rat. Besides, it decreased stroke infarct size and increased the level of antioxidants in both brain and serum. Furthermore, it reduced the formation of malondialdehyde (MDA) after ischemia/reperfusion (I/R). Our results suggested that AC‐AE exerted an effective reduction of ischemia stroke by regulating ROS production.
Results of the 3D Virtual Comparison Microscopy Error Rate (VCMER) Study for firearm forensics

The digital examination of scanned or measured 3D surface topography is referred to as Virtual Comparison Microscopy (VCM). Within the discipline of firearm and toolmark examination, VCM enables review and comparison of microscopic toolmarks on fired ammunition components. In the coming years, this technique may supplement and potentially replace the light comparison microscope as the primary instrument used for firearm and toolmark examination. This paper describes a VCM error rate and validation study involving 107 participants. The study included 40 test sets of fired cartridge cases from firearms with a variety of makes, models, and calibers. Participants used commercially available VCM software which allowed digital data distribution, specimen visualization, and submission of conclusions. The software also allowed participants to annotate areas of similarity and dissimilarity to support their conclusions. The primary cohort of 76 qualified United States and Canadian examiners that completed the study had an overall false‐positive error rate of 3 errors from 693 comparisons (0.43%) and a false‐negative error rate of 0 errors from 491 comparisons (0.0%). This accuracy is supplemented by the participant's provided surface annotations which provide insight into the cause of errors and the overall consistency across the independent examinations conducted in the study. The ability to obtain highly accurate conclusions on test fires from a wide range of firearms supports the hypothesis that VCM is a useful tool within the crime laboratory.
SINAI at CLEF eHealth 2020: Testing Different pre-trained Word Embeddings for Clinical Coding in Spanish

This paper describes the system presented by the SINAI team for the Multilingual Information Extraction task of the CLEF eHealth Lab 2020. This task focuses on the automatic assignment of the International Classiﬁcation of Diseases (ICD) codes to health-related texts in Spanish. Our proposal follows a deep learning-based approach where we have used the bidirectional variant of a Long Short Term Memory (LSTM) network along with a stacked Conditional Random Fields (CRF) decoding layer (BiLSTM+CRF). The aim of the experiments carried out was to test the performance of diﬀerent pre-trained word embed-dings for recognizing diagnoses and procedures in clinical text. The main ﬁnding was that combining word embeddings could be a useful strategy to apply for deep learning-based approaches, even though the combined embeddings do not belong to the medical domain. The best MAP scores achieved were 0.314 and 0.293 for the CodiEsp-D and CodiEsp-P sub-tasks, respectively.
Ecomorphological variation in artiodactyl calcanei using 3D geometric morphometrics

Artiodactyl postcrania are commonly used as paleoecological indicators but these studies are usually limited to artiodactyls within a single family. Here, we use 3D geometric morphometrics to analyze the morphology of calcanei from five artiodactyl families (Antilocapridae, Bovidae, Cervidae, Giraffidae, and Tragulidae) and identify common ecological trends among these families using principal component analysis. Our results indicate that antilocaprid and some bovid calcanei show convergent evolution of cursorial morphology and that other bovids have independently evolved less cursorial morphology that is more similar to cervids. This study shows that parallel ecomorphological trends can be identified in multiple families of artiodactyls, as well as within artiodactyl groups. This further suggests that the calcaneus may be a good indicator of ecology and function in fossil groups that are taxonomically ambiguous or not closely related to living taxa.
Personality, Retirement, and Cognitive Impairment: Moderating and Mediating Associations

Objectives: Five-factor model (FFM) personality traits, including higher conscientiousness and lower neuroticism, are associated with lower risk of dementia and cognitive impairment. In this research, we test whether retirement status moderates and/or mediates the relation between personality and cognitive impairment. Method: We used data from the Health and Retirement Study (N = 9899), a longitudinal study of Americans over the age of 50 years, to examine moderating and mediating associations between personality traits and retirement status on risk of dementia and cognitive impairment not dementia (CIND) over an 8–10 year follow-up. Results: Personality and retirement each had strong, independent associations with risk of dementia and CIND. There were not, however, strong or consistent, moderating or mediating associations between personality and retirement predicting impairment risk. Discussion: Overall, these results indicate that personality and retirement are independent risk factors for incident cognitive impairment. Mechanisms other than retirement are likely to explain this association.
Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network

Abstract. Purpose: Accurate segmentation of treatment planning computed tomography (CT) images is important for radiation therapy (RT) planning. However, low soft tissue contrast in CT makes the segmentation task challenging. We propose a two-step hierarchical convolutional neural network (CNN) segmentation strategy to automatically segment multiple organs from CT. Approach: The first step generates a coarse segmentation from which organ-specific regions of interest (ROIs) are produced. The second step produces detailed segmentation of each organ. The ROIs are generated using UNet, which automatically identifies the area of each organ and improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we combined UNet with a generative adversarial network. The generator is designed as a UNet that is trained to segment organ structures and the discriminator is a fully convolutional network, which distinguishes whether the segmentation is real or generator-predicted, thus improving the segmentation accuracy. We validated the proposed method on male pelvic and head and neck (H&N) CTs used for RT planning of prostate and H&N cancer, respectively. For the pelvic structure segmentation, the network was trained to segment the prostate, bladder, and rectum. For H&N, the network was trained to segment the parotid glands (PG) and submandibular glands (SMG). Results: The trained segmentation networks were tested on 15 pelvic and 20 H&N independent datasets. The H&N segmentation network was also tested on a public domain dataset (N = 38) and showed similar performance. The average dice similarity coefficients (mean ± SD) of pelvic structures are 0.91 ± 0.05 (prostate), 0.95 ± 0.06 (bladder), 0.90 ± 0.09 (rectum), and H&N structures are 0.87 ± 0.04 (PG) and 0.86 ± 0.05 (SMG). The segmentation for each CT takes <10 s on average. Conclusions: Experimental results demonstrate that the proposed method can produce fast, accurate, and reproducible segmentation of multiple organs of different sizes and shapes and show its potential to be applicable to different disease sites.
Time series expression patterns reveal the molecular processes of pancreatic cancer progression.

PURPOSE Pancreatic cancer is a fatal malignant tumor with no obvious characteristics in the early stage of onset and high metastatic ability which results in a low survival. Understanding the detailed process of pancreatic cancer contributes to new treatments to prolong patients' survival. METHODS We carried out an in-depth analysis by modularization while seeking for critical genes in the pathogenesis of pancreatic cancer so as to identify the molecular mechanisms of the condition using differential analysis, co-expression module analysis, enrichment analysis, and network connectivity analysis. In light of the hypergeometric test, ncRNA (non-coding RNA) and transcription factors that regulate the module would be predicted. RESULTS Conclusively, seven co-expression modules were obtained, in which CPA2 and A1BG were significantly differentially expressed in patients who had pancreatic cancer with active regulation in dysfunction modules. The modular genes significantly participated in second-messenger-mediated signaling as well as cellular calcium homeostasis and also controlled the interactions of neuroactive ligand-receptor. Besides, we identified ncRNA pivot including FENDRR and miR-92a-3p as well as transcription factors pivot including SPI1, STAT5A which significantly regulated the dysfunction module. CONCLUSION This study can help reveal core dysfunction modules, potential regulatory factors, and driver genes for pancreatic cancer, enhancing the understanding of its pathogenesis and providing a reference for prediction with respect to the survival time of patients with this condition.
Cardiovascular risk in patients with severe mental illness in Italy

Abstract Background Patients with severe mental illness (SMI), such as schizophrenia or bipolar disorders, are more frequently affected by metabolic syndrome and cardiovascular (CV) diseases than the general population, with a significant reduction in life expectancy. Beyond metabolic syndrome, quantifying the risk of CV morbidity in the long-term may help clinicians to put in place preventive strategies. In this study, we assessed 10-year CV risk in patients with SMI and healthy individuals using an algorithm validated on the Italian general population. Methods Patients aged 35–69 years diagnosed with SMI were consecutively recruited from psychiatric acute care units. Single CV risk factors were assessed, and 10-year CV risk calculated by means of the CUORE Project 10-year CV risk algorithm, based on the combination of the following risk factors: age, systolic blood pressure, total and high-density lipoprotein cholesterol, diabetes, smoking habit, and hypertensive treatment. Patients’ data were compared with those from the general population. The 10-year CV risk was log-transformed, and multivariable linear regression was used to estimate mean ratios, adjusting for age, and education. Results Three hundred patients and 3,052 controls were included in the analysis. Among men, the 10-year CV risk score was very similar between patients with SMI and the general population (mean ratio [MR]: 1.02; 95%CI 0.77–1.37), whereas a 39% increase in 10-year CV risk was observed in women with SMI compared to the general population (MR: 1.39; 95%CI 1.16–1.66). Conclusions In our study, women with SMI were consistently more at risk than the general population counterpart, even at younger age.
Mixed Style Feature Representation and B-maximal Clustering for Style Change Detection

The goal of Style Change Detection task in a document is to determine if it was written by more than one author and in such case, to delimit which paragraph (or more generally a portion of text) corresponds to each one of them. The objective of our proposal is to build a paragraph representation based on general Style Feature computed considering characters, lexical and syntactic features, without the use of semantic words. The paragraphs were grouped employing a non overlapped variant of the B 0 -maximal clustering algorithm, where the overlapping was eliminated considering the order of paragraphs in the document.
Proof-of-Useful-Work as Dual-Purpose Mechanism for Blockchain and AI: Blockchain Consensus that Enables Privacy Preserving Data Mining

Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, we propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models, a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.
Sequential Adaptive Design for Jump Regression Estimation in Materials Discovery

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was for the purpose of accelerating imaging speed in a high resolution material imaging; the second was use of sequential design for the purpose of mapping a chemical phase diagram. In both examples, the underlying regression functions have discontinuities, so many of the existing design optimization approaches cannot be applied because they mostly assume a continuous regression function. Although some existing adaptive design strategies developed from treed regression models can handle the discontinuities, the Bayesian approaches come with computationally expensive Markov Chain Monte Carlo techniques for posterior inferences and subsequent design point selections, which is not appropriate for the first motivating example that requires computation at least faster than the original imaging speed. In addition, the treed models are based on the domain partitioning that are inefficient when the discontinuities occurs over complex sub-domain boundaries. We propose a simple and effective adaptive design strategy for a regression analysis with discontinuities: some statistical properties with a fixed design will be presented first, and then these properties will be used to propose a new criterion of selecting the design points for the regression analysis. Sequential design with the new criterion will be presented with comprehensive simulated examples, and its application to the two motivating examples will be presented.
Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy than existing schemes.
Machine Learning–Based Screening of Healthy Meals From Image Analysis: System Development and Pilot Study

Background Recent research has led to the development of many information technology–supported systems for health care control, including systems estimating nutrition from images of meals. Systems that capture data about eating and exercise are useful for people with diabetes as well as for people who are simply on a diet. Continuous monitoring is key to effective dietary control, requiring systems that are simple to use and motivate users to pay attention to their meals. Unfortunately, most current systems are complex or fail to motivate. Such systems require some manual inputs such as selection of an icon or image, or by inputting the category of the user’s food. The nutrition information fed back to users is not especially helpful, as only the estimated detailed nutritional values contained in the meal are typically provided. Objective In this paper, we introduce healthiness of meals as a more useful and meaningful general standard, and present a novel algorithm that can estimate healthiness from meal images without requiring manual inputs. Methods We propose a system that estimates meal healthiness using a deep neural network that extracts features and a ranking network that learns the relationship between the degrees of healthiness of a meal using a dataset prepared by a human dietary expert. First, we examined whether a registered dietitian can judge the healthiness of meals solely by viewing meal images using a small dataset (100 meals). We then generated ranking data based on comparisons of sets of meal images (850 meals) by a registered dietitian’s viewing meal images and trained a ranking network. Finally, we estimated each meal’s healthiness score to detect unhealthy meals. Results The ranking estimated by the proposed network and the ranking of healthiness based on the dietitian’s judgment were correlated (correlation coefficient 0.72). In addition, extracting network features through pretraining with a publicly available large meal dataset enabled overcoming the limited availability of specific healthiness data. Conclusions We have presented an image-based system that can rank meals in terms of the overall healthiness of the dishes constituting the meal. The ranking obtained by the proposed method showed a good correlation to nutritional value–based ranking by a dietitian. We then proposed a network that allows conditions that are important for judging the meal image, extracting features that eliminate background information and are independent of location. Under these conditions, the experimental results showed that our network achieves higher accuracy of healthiness ranking estimation than the conventional image ranking method. The results of this experiment in detecting unhealthy meals suggest that our system can be used to assist health care workers in establishing meal plans for patients with diabetes who need advice in choosing healthy meals.
Mental health status of health-care professionals working in quarantine and non-quarantine Egyptian hospitals during the COVID-19 pandemic.

Background Variable models of care have been adopted in different countries in response to the COVID-19 pandemic. Egypt has assigned certain hospitals specifically for the quarantine of COVID-19 patients, where operational medical teams work continuously for 14 days, after which they are released for self-isolation at home for a similar period. Aims The study aimed to evaluate and compare perceived adverse psychological symptoms (stress, anxiety, depression), and insomnia by health-care professionals working in quarantine and non-quarantine hospitals during the COVID-19 pandemic in Egypt, and to explore associated factors with adverse psychological symptoms and insomnia. Methods An online cross-sectional survey was performed in April 2020, using a snowball sampling method. Sociodemographic information perceived general health, healthy lifestyle, insomnia (using Insomnia Severity Index), adverse psychological symptoms (using the Depression, Anxiety and Stress Scale-21), worries and concerns about COVID-19, future perspective about COVID-19, and coping strategies were collected. Results Five hundred and forty health-care professionals participated; 10.2% (n=55) worked in quarantine hospitals. Younger age (ORa=0.96, 95% CI:0.93-0.99, ORa=0.95; 95% CI:0.92-0.97; ORa=0.96 CI:0.93-0.99), being not ready/sure of readiness to work in quarantine hospital (ORa=1.91, 95% CI:1.22-3.00; ORa=2.01, 95% CI:1.28-3.15; ORa=1.91, 95% CI:1.22-2.98), and insomnia (ORa=5.22, 95% CI:3.38-8.05; ORa=7.58, 95% CI:4.91-11.68; ORa=6.38 95% CI:4.19-9.73) significantly predicted stress, depression and anxiety, respectively. Being female (ORa=1.59, 95% CI:1.04-2.42; ORa=2.09, 95% CI:1.38-3.16) could also significantly predict stress and anxiety. Conclusion Female and younger age health care professionals were more prone to report adverse psychological symptoms. More and earlier screening for health-care professionals dealing with COVID-19, in addition to providing psychological support, is highly encouraged.