bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving deep learning method for biomedical named entity recognition by using entity definition information
Authors: Xiong Ying , Chen Shuai , Tang Buzhou , Chen Qingcai , Wang Xiaolong , Yan Jun , Zhou Yi 
Published: December 2021

Abstract
Background
Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information.

Material and method
We investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score.

Results
Entity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER.

Conclusion
Our entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.
Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization
Authors: Rivera-Zavala Renzo M. , Martínez Paloma 
Published: December 2021

Abstract
Background
The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain.

Methods
In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture.

Results
Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset.

Conclusion
These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.
Deep learning with language models improves named entity recognition for PharmaCoNER
Authors: Sun Cong , Yang Zhihao , Wang Lei , Zhang Yin , Lin Hongfei , Wang Jian 
Published: December 2021

Abstract
Background
The recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Although considerable efforts have been made to recognize biomedical entities in English texts, to date, only few limited attempts were made to recognize them from biomedical texts in other languages. PharmaCoNER is a named entity recognition challenge to recognize pharmacological entities from Spanish texts. Because there are currently abundant resources in the field of natural language processing, how to leverage these resources to the PharmaCoNER challenge is a meaningful study.

Methods
Inspired by the success of deep learning with language models, we compare and explore various representative BERT models to promote the development of the PharmaCoNER task.

Results
The experimental results show that deep learning with language models can effectively improve model performance on the PharmaCoNER dataset. Our method achieves state-of-the-art performance on the PharmaCoNER dataset, with a max F1-score of 92.01%.

Conclusion
For the BERT models on the PharmaCoNER dataset, biomedical domain knowledge has a greater impact on model performance than the native language (i.e., Spanish). The BERT models can obtain competitive performance by using WordPiece to alleviate the out of vocabulary limitation. The performance on the BERT model can be further improved by constructing a specific vocabulary based on domain knowledge. Moreover, the character case also has a certain impact on model performance.
Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid tumors
Authors: Xiang Shiyu , Ren Jiliang , Xia Zhipeng , Yuan Ying , Tao Xiaofeng 
Published: December 2021

Abstract
Objective
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups.

Materials and methods
A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups.

Results
For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P < 0.05) and higher values on skewness MRE (AUC, 0.777; sensitivity 71.87%; specificity 76.36%; P < 0.05).

Conclusion
DCE-MRI histogram parameters, especially TTP and MRE parameters, show promise as effective indicators for identifying and classifying parotid tumors. Entropy TTP and kurtosis MRE were found to be independent differentiating variables for malignant parotid gland tumors. The 10th WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors.
Is the healthy start scheme associated with increased food expenditure in low-income families with young children in the United Kingdom?
Authors: Parnham Jennie , Millett Christopher , Chang Kiara , Laverty Anthony A. , von Hinke Stephanie , Pearson-Stuttard Jonathan , Vamos Eszter P. 
Published: December 2021

Abstract
Background
Healthy Start is a food assistance programme in the United Kingdom (UK) which aims to provide a nutritional safety-net and enable low-income families on welfare benefits to access a healthier diet through the provision of food vouchers. Healthy Start was launched in 2006 but remains under-evaluated. This study aims to determine whether participation in the Healthy Start scheme is associated with differences in food expenditure in a nationally representative sample of households in the UK.

Methods
Cross-sectional analyses of the Living Costs and Food Survey dataset (2010–2017). All households with a child (0–3 years) or pregnant woman were included in the analysis (n = 4869). Multivariable quantile regression compared the expenditure and quantity of fruit and vegetables (FV), infant formula and total food purchases. Four exposure groups were defined based on eligibility, participation and income (Healthy Start Participating, Eligible Non-participating, Nearly Eligible low-income and Ineligible high-income households).

Results
Of 876 eligible households, 54% participated in Healthy Start. No statistically significant differences were found in FV or total food purchases between participating and eligible non-participating households, but infant formula purchases were lower in Healthy Start participating households. Ineligible higher-income households had higher purchases of FV.

Conclusion
This study did not find evidence of an association between Healthy Start participation and FV expenditure. Moreover, inequalities in FV purchasing persist in the UK. Higher participation and increased voucher value may help to improve programme performance and counteract the harmful effects of poverty on diet.
Intimate partner violence during COVID-19 lockdown in Norway: the increase of police reports
Authors: Nesset Merete Berg , Gudde Camilla Buch , Mentzoni Gro Elisabet , Palmstierna Tom 
Published: December 2021

Abstract
Background
In March 2020, the Norwegian government announced a COVID-19 lockdown in order to reduce the spread of the coronavirus. In Norway, lockdown measures included restricting people’s ability to leave their home and the closing of social institutions, thus reducing the capacity for victims of intimate partner violence to alert someone outside of their home about violent incidents that occurred during lockdown. At the same time, the restrictive measures forced the victim and the perpetrator to stay together for prolonged periods within the home, and reduced the possibility for them to escape or leave the perpetrator. The aim of this study was to investigate how the frequency and character of intimate partner violence reported to the police changed during the period of lockdown in Norway.

Methods
All cases of intimate partner violence registered in police files before the pandemic (from January 2016-February 2020) and during lockdown in Norway (March-December 2020) were included in the study, representing a total of 974 cases. Differences in the number and severity of cases were calculated using χ2-tests and Wilcoxon’s rank sum test. Differences in the characteristics of the reported violence was assessed with the Brief Spousal Assault form for the Evaluation of Risk (B-SAFER) and tested with Fischer’s exact test with Bonferroni correction. Standardised Morbidity Rate (SMR) statistics were used to analyse the proportion of immigrants as compared to the general population.

Results
Reported intimate partner violence increased by 54% during the lockdown period in Norway. Between March-December 2020, the police assessed the cases as being at higher risk of imminent and severe violence. Our findings indicated an overrepresentation of immigrant perpetrators before and during lockdown (SMR = 1.814, 95% CI = 1.792–1.836 before, and SMR = 1.807, 95% CI = 1.742–1.872 during lockdown). Notably, while victims with an immigrant background were overrepresented before lockdown, we found significantly lower proportion of immigrant IPV victims during the lockdown period (SMR = 1.070, 95% CI = 1.052–1.087 before, and SMR = 0.835, CI 95% CI = 0.787-0.883 during lockdown). Also, there were significantly more female perpetrators and male victims reported to the police during the lockdown period. A higher proportion of the victims were assessed as having unsafe living conditions and personal problems during lockdown. Finally, during the lockdown period in Norway, a higher proportion of perpetrators had a history of intimate relationship problems.

Conclusions
Intimate partner violence increased dramatically during the COVID-19 lockdown. A range of options for victims to escape from their perpetrators, particularly during times of crisis, should be developed in line with good practice, and with a special focus on the most vulnerable victims.
Chronic hypersensitivity pneumonitis is associated with an increased risk of venous thromboembolism: a retrospective cohort study
Authors: Sobiecka Małgorzata , Szturmowicz Monika , Lewandowska Katarzyna , Kowalik Agata , Łyżwa Ewa , Zimna Katarzyna , Barańska Inga , Jakubowska Lilia , Kuś Jan , Langfort Renata , Tomkowski Witold 
Published: December 2021

Abstract
Background
Idiopathic pulmonary fibrosis (IPF) and chronic hypersensitivity pneumonitis share commonalities in pathogenesis shifting haemostasis balance towards the procoagulant and antifibrinolytic activity. Several studies have suggested an increased risk of venous thromboembolism in IPF. The association between venous thromboembolism and chronic hypersensitivity pneumonitis has not been studied yet.

Methods
A retrospective cohort study of IPF and chronic hypersensitivity pneumonitis patients diagnosed in single tertiary referral center between 2005 and 2018 was conducted. The incidence of symptomatic venous thromboembolism was evaluated. Risk factors for venous thromboembolism and survival among those with and without venous thromboembolism were assessed.

Results
A total of 411 (259 IPF and 152 chronic hypersensitivity) patients were included (mean age 66.7 ± 8.4 vs 51.0 ± 13.3 years, respectively). There were 12 (4.6%) incident cases of venous thromboembolism in IPF and 5 (3.3%) in chronic hypersensitivity pneumonitis cohort. The relative risk (RR) of venous thromboembolism in chronic hypersensitivity pneumonitis was not significantly different to that found in patients with IPF (7.1 vs 11.8/1000 person-years, RR 1.661 95% CI 0.545–6.019, respectively).
The treatment with systemic steroids (OR 5.38; 95% CI 1.65–18.8, p = 0.006) and GAP stage 3 (OR 7.85; 95% CI 1.49–34.9; p = 0.037) were significant risk factors for venous thromboembolism in IPF. Arterial hypertension and pulmonary hypertension significantly increased risk of venous thromboembolism in chronic hypersensitivity pneumonitis. There were no significant differences in survival between patients with and without venous thromboembolism.

Conclusions
The patients with chronic hypersensitivity pneumonitis have a marked increase in the risk of venous thromboembolism, similar to the patients with IPF. Venous thromboembolism does not affect the survival of patients with IPF and chronic hypersensitivity pneumonitis.
LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images
Authors: Zhang Guang , Ren Yanwei , Xi Xiaoming , Li Delin , Guo Jie , Li Xiaofeng , Tian Cuihuan , Xu Zunyi 
Published: December 2021

Abstract
Purpose
This study proposed a novel Local Reference Semantic Code (LRSC) network for automatic breast ultrasound image classification with few labeled data.

Methods
In the proposed network, the local structure extractor is firstly developed to learn the local reference which describes common local characteristics of tumors. After that, a two-stage hierarchical encoder is developed to encode the local structures of lesion into the high-level semantic code. Based on the learned semantic code, the self-matching layer is proposed for the final classification.

Results
In the experiment, the proposed method outperformed traditional classification methods and AUC (Area Under Curve), ACC (Accuracy), Sen (Sensitivity), Spec (Specificity), PPV (Positive Predictive Values), and NPV(Negative Predictive Values) are 0.9540, 0.9776, 0.9629, 0.93, 0.9774 and 0.9090, respectively. In addition, the proposed method also improved matching speed.

Conclusions
LRSC-network is proposed for breast ultrasound images classification with few labeled data. In the proposed network, a two-stage hierarchical encoder is introduced to learn high-level semantic code. The learned code contains more effective high-level classification information and is simpler, leading to better generalization ability.
Epidemiological profile of multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis among Congolese patients
Authors: Elion Assiana Darrel Ornelle , Abdul Jabar Babatunde Pacôme Achimi , Linguissi Laure Stella Ghoma , Epola Micheska , Vouvoungui Jeannhey Christevy , Mabiala Albert , Biyogho Christopher Mebiame , Ronald Edoa Jean , Adegbite Bayodé Roméo , Adegnika Ayola Akim , Elton Linzy , Canseco Julio Ortiz , McHugh Timothy D. , Ahombo Gabriel , Ntoumi Francine 
Published: December 2021

Abstract
Background
There is paucity of data on the prevalence and distribution of multidrug- Resistant-Tuberculosis (MDR-TB) in the Republic of Congo. Among the challenges resides the implementation of a robust TB resistance diagnostic program using molecular tools. In resource limited settings there is a need to gather data to enable prioritization of actions. The objective of this study was is to implement molecular tools as a best of diagnosing MDR and XDR-TB among presumptive tuberculosis patients referred to reference hospital of Makelekele in Brazzaville, Republic of the Congo.

Methods
We have conducted a cross-sectional study, including a total of 92 presumptive pulmonary tuberculosis patients and who had never received treatment recruited at the reference hospital of Makelekele from October 2018 to October 2019. The socio-demographic and clinical data were collected as well as sputum samples. Rifampicin resistance was investigated using Xpert (Cepheid) and second-line TB drugs Susceptibility testing were performed by the Brucker HAIN Line Probe Assay (GenoType MTBDRsl VER 2.0 assay) method.

Results
From the 92 recruited patients, 57 (62%) were found positive for the Mycobacterium tuberculosis complex. The prevalence of rifampicin-resistant tuberculosis (RR-TB) was 9.8% (9/92) and importantly 2.2% were pre-XDR/XDR.

Conclusion
This study showed a high rate of rifampicin resistance and the presence of extensively drug-resistant tuberculosis in the study area in new patients. This study highlights the need for further studies of TB drug resistance in the country.
A study of sharp coefficient bounds for a new subfamily of starlike functions
Authors: Ullah Khalil , Srivastava H. M. , Rafiq Ayesha , Arif Muhammad , Arjika Sama 
Published: December 2021

AbstractIn this article, by employing the hyperbolic tangent function tanhz, a subfamily$\mathcal{S}_{\tanh }^{\ast }$Stanh∗of starlike functions in the open unit disk$\mathbb{D}\subset \mathbb{C}$D⊂C:$$\begin{aligned} \mathbb{D}= \bigl\{ z:z\in \mathbb{C} \text{ and } \vert z \vert < 1 \bigr\} \end{aligned}$$D={z:z∈C and |z|<1}is introduced and investigated. The main contribution of this article includes derivations of sharp inequalities involving the Taylor–Maclaurin coefficients for functions belonging to the class$\mathcal{S}_{\tanh }^{\ast } $Stanh∗of starlike functions in$\mathbb{D}$D. In particular, the bounds of the first three Taylor–Maclaurin coefficients, the estimates of the Fekete–Szegö type functionals, and the estimates of the second- and third-order Hankel determinants are the main problems that are proposed to be studied here.
On a nonlocal implicit problem under Atangana–Baleanu–Caputo fractional derivative
Authors: Alnahdi Abeer S. , Jeelani Mdi Begum , Abdo Mohammed S. , Ali Saeed M. , Saleh S. 
Published: December 2021

AbstractIn this paper, we study a class of initial value problems for a nonlinear implicit fractional differential equation with nonlocal conditions involving the Atangana–Baleanu–Caputo fractional derivative. The applied fractional operator is based on a nonsingular and nonlocal kernel. Then we derive a formula for the solution through the equivalent fractional functional integral equations to the proposed problem. The existence and uniqueness are obtained by means of Schauder’s and Banach’s fixed point theorems. Moreover, two types of the continuous dependence of solutions to such equations are discussed. Finally, the paper includes two examples to substantiate the validity of the main results.