bluuebunny/crossref_metadata_2025_split huggingface dataset:
The acquisition of demonstratives in a complex noun class system
Authors: Sagna Serge , Vihman Virve‑Anneli , Vihman Marilyn , Brown Dunstan 
Published: December 2022

We present an exploratory study of 2- to 3-year-old children’s acquisition of the demonstrative system of Eegimaa (ISO 369–3 bqj), an endangered language belonging to the Jóola cluster of the Atlantic family of the Niger-Congo phylum, spoken by about 13,000 speakers in southwestern Senegal. Eegimaa demonstratives express distance from speaker (proximal, medial and distal) and the agreement categories of number and gender, as well as having four morphological types that create an additional dimension of complexity for children to learn. These demonstrative types are each associated with a range of syntactic functions with partial overlaps.From nearly seven hours of recordings, including children at three age points (2;0, 2;6 and 3;0), we extracted 218 demonstrative tokens from the children’s speech, matched with 205 tokens from a sub-sample of caregiver speech. The youngest children can be described as restricting their use of demonstratives to a small set of learned items, with evidence of generalisation and productivity arising over the course of development, alongside an increase in frequency and development in distribution patterns of the various demonstrative forms to more target-like usage in the 3;0 sample. At age three we observe more variation by syntactic function. As has been found in other languages, children acquiring Eegimaa seem to make use of the diverse forms of demonstratives early, but they do not yet make use of the full range of forms even at age three, when they are beginning to produce more systematic forms of the demonstratives across syntactic contexts and with a variety of genders.
A review of swine heat production: 2003 to 2020
Authors: Ramirez Brett C. , Hoff Steven J. , Hayes Morgan D. , Brown-Brandl Tami , Harmon Jay D. , Rohrer Gary A. 
Published: December 2022

Swine heat production (HP) data are an essential element of numerous aspects affecting swine production sustainability, such as, housing environmental control design, energetics and thermoregulation modeling, as well as understanding of feed energy partitioning. Accurate HP values that reflect the continuous advances in growth, nutrition, health, and reproduction are needed to update outdated models and data; hence, this review of swine HP values is a critical contribution. This review updates the last previous review conducted in 2004, by reviewing literature from growing and breeding pigs from 2003 to 2020. In total, 33 references were identified that provided relevant HP data and from these references, 192 records were identified for pigs ranging in weight from 12.5 to 283 kg and exposed to temperatures between 12.0°C and 35.5°C. For growing pigs at thermoneutral conditions, a 4.7% average increase in HP was observed compared to HP data summarized from 1988 to 2004. Only five records were identified for gestating sows and the 43 records for lactating sows plus litter. This sow data shows high variability and inconsistent trends with temperature, most likely attributed to variation in experimental protocols, management, and limited reported information. There is still a lack of data on growing pigs greater than 105 kg, gilts and gestating sows housed in different systems (stall, pen, mixed, etc.), and latent HP values that reflect different housing systems. Further, there is a need to standardize reporting of HP values (with an example provided) across different disciplines to drive documentation of increased swine production efficiency, environmental control design, and energetics modeling.
Application of yeast in plant-derived aroma formation from cigar filler leaves
Authors: Yao Lan , Huang Chenyi , Ding Jingyi , Zhang Tongtong , Yu Jun , Yang Chunlei , Chen Xiong 
Published: December 2022

Introduction: There are various degrees of defects of cigar filler leaves after air drying.Methods: In order to improve the quality and plant-derived aroma content of cigar filler leaves, nine aroma-producing yeasts were applied in artificially solid-state fermentation of cigar filler leaves in this study. The differences with various yeasts application were compared by chemical composition and GC-MS analysis.Results and discussion: The results showed that 120 volatile components were identified and quantified in cigar filler leaves after fermentation, including aldehydes (25 types), alcohols (24 types), ketones (20 types), esters (11 types), hydrocarbons (12 types), acids (4 types) and other substances (23 types). Based on the analysis of odor activity value (OAV), the OVA of fruity and floral aroma components were higher. It was found that floral aroma are the representative aroma types of cigar filler leaves treated with Clavispora lusitaniae, Cyberlindera fabianii, Saccharomycosis fibuligera and Zygosaccharomyces bailii R6. After being inoculated with Hanseniaspora uvarum J1, Hanseniaspora uvarum J4 and Pichia pastoris P3, the OAV of fruity aroma in cigar filler leaves was the highest, followed by tobacco aroma and woody aroma. The correlation between volatile components of cigar filler leaves with different yeasts was revealed after PCA analysis. It was concluded that the quality of cigar filler leaves was improved, and cigar filler leaves fermented with different yeasts showed different flavor.
Human parvovirus B19 infection in hospitalized patients suspected of infection with pathogenic microorganism
Authors: Guo Junshuang , Wang Yating , Zhang Mian , Zheng Hongxiang , Zang Qiuling , Huang Peipei , Wen Lijun , Song Dandan , Yang Fan , Dong Ruirui , Miao Wang 
Published: December 2022

BackgroundHuman parvovirus B19 (HPV B19) is a single-stranded DNA virus. The detection rate of HPV B19 in the blood of healthy blood donors using PCR technology was reported to be 6.323/100000. However, that among hospitalized patients suspected of being infected with a pathogenic microorganism is unknown.MethodsA retrospective analysis was conducted on 2,182 high-throughput NGS results for 1,484 inpatients admitted to the First Affiliated Hospital of Zhengzhou University from January 2020 to October 2021 who were suspected of being infected with a pathogenic microorganism, as well as on clinical data of some HPV B19-positive patients.ResultsHuman parvovirus B19 was detected in 39 samples from 33 patients. The positivity rate was 2.22% among patients and 1.78% among samples. HPV B19 was detected in 20 cerebrospinal fluid samples, 13 blood samples, 3 alveolar lavage fluid samples, 2 tissue samples, and 1 throat swab. Based on clinical symptoms and NGS results, 16 patients were diagnosed with HPV B19 infection. The number of HPV B19 sequences in these patients was greater than 6, and the patients showed common symptoms such as fever (14 cases), anemia (11 cases), and severe nervous system symptoms such as meningoencephalitis (9 cases) and Guillain–Barré syndrome with peripheral motor and sensory nerve axon damage (4 cases). All 16 patients had experienced events likely to lead to decreased immunity (11 had a history of trauma/surgery/major disease, 4 had a history of precursor infection, and 3 had used immunosuppressants) and 7 had a history of blood transfusion during hospitalization. After treatment with antiviral drugs (12 cases) and intravenous human immunoglobulin (3 cases), of the 16 patients, 14 patients improved.ConclusionThe HPV B19 infection rate in hospitalized patients suspected of microbial infection was 2.22%. Most patients with HPV B19 infection had a history of low immunity and blood transfusion. HPV B19 could be detected in various bodily fluids and tissues (especially cerebrospinal fluid) using NGS. Patients with severe HPV B19 infection may have nervous system damage such as Guillain–Barré syndrome and meningoencephalitis. Early diagnosis using NGS and treatment with antiviral drugs and immunoglobulin can improve prognosis.
Cardiac sequelae after COVID-19: Results of a 1-year follow-up study with echocardiography and biomarkers
Authors: Matejova Gabriela , Radvan Martin , Bartecku Elis , Kamenik Martin , Koc Lumir , Horinkova Jana , Sykorova Lubica , Stepanova Radka , Kala Petr 
Published: December 2022

ObjectiveTo evaluate the need for cardiac monitoring in unselected patients recovered from COVID-19 and to estimate the risk of heart complications after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Materials and methodsDuring March 2020 and January 2021, 106 patients who had recovered from SARS-CoV-2 (alpha and beta variants) were enrolled in prospective observational cohort study CoSuBr (Covid Survivals in Brno). The diagnosis was based on a reverse transcription-polymerase chain reaction swab test of the upper respiratory tract. Demographic parameters, patient history, clinical evaluation, cardiac biomarkers, ECG and echocardiography were recorded during three visits (Visit 1 at least 6 weeks after infection, Visit 2 three months later, and Visit 3 one year after Visit 1).Results58.5% of the study group (n = 106) were female, while the mean age was 46 years (range 18–77 years). The mean time interval between the onset of infection and the follow-up visit was 107 days. One quarter (24.5%) of the patients required hospitalization during the acute phase of the disease; the rest recovered at home. 74% suffered a mild form of the disease, with 4.8, 18.1, and 2.9% suffering moderate, severe, and critical forms, respectively. At the time of enrolment, 64.2% of the patients reported persistent symptoms, while more than half of the whole group (50.9%) mentioned at least one symptom of possible cardiac origin (breathing problems, palpitations, exercise intolerance, fatigue). In the 1-year follow-up after COVID-19 infection, left ventricle ejection fraction showed no significant decrease [median (IQR) change was −1.0 (−6.0; 4.0)%, p = 0.150], and there were no changes of troponin (mean change −0.1 ± 1.72 ng/L; p = 0.380) or NT-proBNP [median (IQR) change 2.0 (−20.0; 29.0) pg/mL; p = 0.315]. There was a mild decrease in right ventricle end diastolic diameter (-mean change 2.3 ± 5.61 mm, p &lt; 0.001), while no right ventricle dysfunction was detected. There was very mild progress in left ventricle diastolic diameter [median (IQR) change 1.0 (−1.0; 4.0) mm; p = 0.001] between V1 and V3, mild enlargement of the left atrium (mean change 1.2 ± 4.17 mm; p = 0.021) and a non-significant trend to impairment of left ventricle diastolic dysfunction. There was a mild change in pulmonary artery systolic pressure [median (IQR) change 3.0 (−2.0; 8.0) mmHg; p = 0.038].ConclusionDespite a lot of information regarding cardiac impairment due to SARS-CoV2, our study does not suggest an increased risk for developing clinically significant heart changes during the 1-year follow-up. Based on our results, routine echocardiography and biomarkers collection is currently not recommended after COVID-19 recovery.
Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation
Authors: Li Xiaojun , Yan Lamei , Wang Xuehong , Ouyang Chunhui , Wang Chunlian , Chao Jun , Zhang Jie , Lian Guanghui 
Published: December 2022

BackgroundEndoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs.MethodsThe patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models.ResultsA total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values.ConclusionThis proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.
Exercise blood-drop metabolic profiling links metabolism with perceived exertion
Authors: Opialla Tobias , Gollasch Benjamin , Kuich Peter H. J. L. , Klug Lars , Rahn Gabriele , Busjahn Andreas , Spuler Simone , Boschmann Michael , Kirwan Jennifer A. , Luft Friedrich C. , Kempa Stefan 
Published: December 2022

Background: Assessing detailed metabolism in exercising persons minute-to-minute has not been possible. We developed a “drop-of-blood” platform to fulfill that need. Our study aimed not only to demonstrate the utility of our methodology, but also to give insights into unknown mechanisms and new directions.Methods: We developed a platform, based on gas chromatography and mass spectrometry, to assess metabolism from a blood-drop. We first observed a single volunteer who ran 13 km in 61 min. We particularly monitored relative perceived exertion (RPE). We observed that 2,3-bisphosphoglycerate peaked at RPE in this subject. We next expanded these findings to women and men volunteers who performed an RPE-based exercise protocol to RPE at Fi O 2 20.9% or Fi O 2 14.5% in random order.Results: At 6 km, our subject reached his maximum relative perceived exertion (RPE); however, he continued running, felt better, and finished his run. Lactate levels had stably increased by 2 km, ketoacids increased gradually until the run’s end, while the hypoxia marker, 2,3 bisphosphoglycerate, peaked at maximum relative perceived exertion. In our normal volunteers, the changes in lactate, pyruvate, ß hydroxybutyrate and a hydroxybutyrate were not identical, but similar to our model proband runner.Conclusion: Glucose availability was not the limiting factor, as glucose availability increased towards exercise end in highly exerted subjects. Instead, the tricarboxylic acid→oxphos pathway, lactate clearance, and thus and the oxidative capacity appeared to be the defining elements in confronting maximal exertion. These ideas must be tested further in more definitive studies. Our preliminary work suggests that our single-drop methodology could be of great utility in studying exercise physiology.
Salivary metabolic signatures of carotid atherosclerosis in patients with type 2 diabetes hospitalized for treatment
Authors: Sakanaka Akito , Katakami Naoto , Furuno Masahiro , Nishizawa Hitoshi , Omori Kazuo , Taya Naohiro , Ishikawa Asuka , Mayumi Shota , Inoue Moe , Tanaka Isomura Emiko , Amano Atsuo , Shimomura Iichiro , Fukusaki Eiichiro , Kuboniwa Masae 
Published: December 2022

Atherosclerosis is a life-threatening disease associated with morbidity and mortality in patients with type 2 diabetes (T2D). This study aimed to characterize a salivary signature of atherosclerosis based on evaluation of carotid intima-media thickness (IMT) to develop a non-invasive predictive tool for diagnosis and disease follow-up. Metabolites in saliva and plasma samples collected at admission and after treatment from 25 T2D patients hospitalized for 2 weeks to undergo medical treatment for diabetes were comprehensively profiled using metabolomic profiling with gas chromatography-mass spectrometry. Orthogonal partial least squares analysis, used to explore the relationships of IMT with clinical markers and plasma and salivary metabolites, showed that the top predictors for IMT included salivary allantoin and 1,5-anhydroglucitol (1,5-AG) at both the baseline examination at admission and after treatment. Furthermore, though treatment induced alterations in salivary levels of allantoin and 1,5-AG, it did not modify the association between IMT and these metabolites (pinteraction &gt; 0.05), and models with these metabolites combined yielded satisfactory diagnostic accuracy for the high IMT group even after treatment (area under curve = 0.819). Collectively, this salivary metabolite combination may be useful for non-invasive identification of T2D patients with a higher atherosclerotic burden in clinical settings.
Aberrant brain functional network strength related to cognitive impairment in age-related hearing loss
Authors: Zhu Shaoyun , Song Jiajie , Xia Wenqing , Xue Yuan 
Published: December 2022

PurposeAge-related hearing loss (ARHL) is a major public issue that affects elderly adults. However, the neural substrates for the cognitive deficits in patients with ARHL need to be elucidated. This study aimed to explore the brain regions that show aberrant brain functional network strength related to cognitive impairment in patients with ARHL.MethodsA total of 27 patients with ARHL and 23 well-matched healthy controls were recruited for the present study. Each subject underwent pure-tone audiometry (PTA), MRI scanning, and cognition evaluation. We analyzed the functional network strength by using degree centrality (DC) characteristics and tried to recognize key nodes that contribute significantly. Subsequent functional connectivity (FC) was analyzed using significant DC nodes as seeds.ResultsCompared with controls, patients with ARHL showed a deceased DC in the bilateral supramarginal gyrus (SMG). In addition, patients with ARHL showed enhanced DC in the left fusiform gyrus (FG) and right parahippocampal gyrus (PHG). Then, the bilateral SMGs were used as seeds for FC analysis. With the seed set at the left SMG, patients with ARHL showed decreased connectivity with the right superior temporal gyrus (STG). Moreover, the right SMG showed reduced connectivity with the right middle temporal gyrus (MTG) and increased connection with the left middle frontal gyrus (MFG) in patients with ARHL. The reduced DC in the left and right SMGs showed significant negative correlations with poorer TMT-B scores (r = −0.596, p = 0.002; r = −0.503, p = 0.012, respectively).ConclusionThese findings enriched our understanding of the neural mechanisms underlying cognitive impairment associated with ARHL and may serve as a potential brain network biomarker for investigating and predicting cognitive difficulties.
Acidosis in arterial blood gas testing is associated with clinical outcomes after endovascular thrombectomy
Authors: Shao Rui , Liu Lei , Xu Juan , Lan Pengpeng , Wu Guiping , Shi Hongfeng , Li Ruili , Zhuang Yingle , Han Shanshan , Li Yan , Zhao Ping , Xu Min , Tang Ziren 
Published: December 2022

BackgroundDespite recanalization, some of the patients undergoing endovascular thrombectomy (EVT) still suffer from unfavorable outcomes. Patients with poor prognoses are often accompanied by acidosis in arterial blood gas (ABG) testing. We, therefore, explored the ABG testing results in the early phase of recanalization and analyzed their association with poor prognosis.Patients and methodsWe identified all patients with ischemic stroke and successful endovascular recanalization for anterior circulation vessel occlusion between June 2019 and May 2022. ABG testing was performed in all patients within 0–30 min and 8 h after endovascular therapy. We investigated the relationship between the ABG testing results with symptomatic intracerebral hemorrhage (sICH), hemicraniectomy, and mortality.ResultsA total of 123 patients with stroke after endovascular thrombectomy were analyzed. Of those, eight (6.5%) patients had postinterventional sICH. Acidosis was associated with sICH. Decreased HCO3- levels and HCO3- levels at 8 h after EVT were independently related to a higher risk of sICH. Twelve (9.8%) patients underwent hemicraniectomy for postischemic malignant edema and similar results were found for hemicraniectomy. Increased lactate at 8 h after EVT and decreased HCO3- levels at 8 h after EVT were closely associated with hemicraniectomy. Twenty-two (17.9%) patients died within 3 months. Decreased HCO3- levels were independently related to mortality, as were decreased pH levels at 8 h after EVT and decreased HCO3- levels at 8 h after EVT.ConclusionAcidosis is associated with clinical outcomes after endovascular therapy and may help to select patients with poor prognosis in the acute early phase of recanalization.
Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
Authors: Feng Jin , Li Yunde , Jiang Chengliang , Liu Yu , Li Mingxin , Hu Qinghui 
Published: December 2022

IntroductionElectroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor.MethodsTo solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model.ResultsIn order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%.DiscussionCompared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation.