bluuebunny/crossref_metadata_2025 huggingface dataset:
Case report: Successful thromboprophylaxis with enoxaparin in a pregnant woman with internal jugular vein agenesis
Authors: Di Micco Pierpaolo , Orlando Luana , Cataldo Donato , Imbalzano Egidio
Published: November 2022

Internal jugular agenesis is a vascular malformation that is often associated with a history of recurrent headache. Due to the resulting abnormalities in intracranial venous drainage, it may be complicated by neurological dysfunction, such as intracranial hypertension, intracranial micro-thromboses, and neurodegenerative diseases such as multiple sclerosis. The simultaneous presence of jugular vein agenesis and thrombosis is possible in cases of acute illness, hormonal treatment, pregnancy, hypomobility, or venous drainage abnormalities (VDA) (e.g., May-Thurner syndrome). In particular, the literature still lacks data on thromboprophylaxis in pregnant women with jugular vein agenesis. Here, we report a positive experience with prophylaxis using enoxaparin during pregnancy in a patient with internal jugular agenesis.
Shock index, modified shock index, age shock index score, and reverse shock index multiplied by Glasgow Coma Scale predicting clinical outcomes in traumatic brain injury: Evidence from a 10-year analysis in a single center
Authors: Lin Po-Chen , Liu Chi-Yuan , Tzeng I-Shiang , Hsieh Tsung-Han , Chang Chun-Yu , Hou Yueh-Tseng , Chen Yu-Long , Chien Da-Sen , Yiang Giou-Teng , Wu Meng-Yu
Published: November 2022

Objectives

Early identification of traumatic brain injury (TBI) patients at a high risk of mortality is very important. This study aimed to compare the predictive accuracy of four scoring systems in TBI, including shock index (SI), modified shock index (MSI), age-adjusted shock index (ASI), and reverse shock index multiplied by the Glasgow Coma Scale (rSIG).

Patients and methods

This is a retrospective analysis of a registry from the Taipei Tzu Chi trauma database. Totally, 1,791 patients with TBI were included. We investigated the accuracy of four major shock indices for TBI mortality. In the subgroup analysis, we also analyzed the effects of age, injury mechanism, underlying diseases, TBI severity, and injury severity.

Results

The predictive accuracy of rSIG was significantly higher than those of SI, MSI, and ASI in all the patients [area under the receiver operating characteristic curve (AUROC), 0.710 vs. 0.495 vs. 0.527 vs. 0.598], especially in the moderate/severe TBI (AUROC, 0.625 vs. 0.450 vs. 0.476 vs. 0.529) and isolated head injury populations (AUROC 0.689 vs. 0.472 vs. 0.504 vs. 0.587). In the subgroup analysis, the prediction accuracy of mortality of rSIG was better in TBI with major trauma [Injury Severity Score (ISS) ≥ 16], motor vehicle collisions, fall injury, and healthy and cardiovascular disease population. rSIG also had a better prediction effect, as compared to SI, MSI, and ASI, both in the non-geriatric (age < 65 years) and geriatric (age ≥ 65 years).

Conclusion

rSIG had a better prediction accuracy for mortality in the overall TBI population than SI, MSI, and ASI. Although rSIG have better accuracy than other indices (ROC values indicate poor to moderate accuracy), the further clinical studies are necessary to validate our results.
A taxonomic assessment of novel and remarkable fungal species in Didymosphaeriaceae (Pleosporales, Dothideomycetes) from plant litter
Authors: Tennakoon Danushka S. , Thambugala Kasun M. , Silva Nimali I. de , Suwannarach Nakarin , Lumyong Saisamorn
Published: November 2022

Fungal taxonomy has a long history and changed significantly in the last few decades. Most recent studies have witnessed morphology combined with DNA-based molecular analyses as the main research tool for fungal species identification. During field surveys, some interesting Didymosphaeriaceae species were found from plant litter in China and Thailand. Morphology combined with phylogenetic analyses (Bayesian and maximum likelihood) of ITS, LSU, SSU, tef1-α, and tub2 loci was used to identify fungal taxa. In this article, three new species and six new host records are described. The new species, Montagnula acaciae, Paraconiothyrium zingiberacearum, and Paraphaeosphaeria brachiariae, can be distinguished from other species of the respective genera based on their distinct size differences (ascomata, asci, and ascospores) and DNA sequence data. The new host records, Montagnula jonesii, Paraconiothyrium fuckelii, Spegazzinia deightonii, and S. tessarthra are reported from Ficus benjamina, Dimocarpus longan, Hedychium coronarium, and Acacia auriculiformis respectively, for the first time. Also, Paraconiothyrium archidendri and P. brasiliense are reported for the first time from Magnolia sp. in China. Moreover, Paraconiothyrium rosae is synonymized under P. fuckelii based on close phylogeny affinities and morphological characteristics. In-depth morphological descriptions, micrographs, and phylogenetic trees are provided to show the placement of new taxa.
Principles and functions of condensate modifying drugs
Authors: Patel Avinash , Mitrea Diana , Namasivayam Vigneshwaran , Murcko Mark A. , Wagner Michael , Klein Isaac A.
Published: November 2022

Biomolecular condensates are compartmentalized communities of biomolecules, which unlike traditional organelles, are not enclosed by membranes. Condensates play roles in diverse cellular processes, are dysfunctional in many disease states, and are often enriched in classically “undruggable” targets. In this review, we provide an overview for how drugs can modulate condensate structure and function by phenotypically classifying them as dissolvers (dissolve condensates), inducers (induce condensates), localizers (alter localization of the specific condensate community members) or morphers (alter the physiochemical properties). We discuss the growing list of bioactive molecules that function as condensate modifiers (c-mods), including small molecules, oligonucleotides, and peptides. We propose that understanding mechanisms of condensate perturbation of known c-mods will accelerate the discovery of a new class of therapies for difficult-to-treat diseases.
Self Assessment Memory Scale (SAMS), a new simple method for evaluating memory function
Authors: Kowa Hisatomo , Uchimura Maki , Ohashi Asuka , Hiroe Mamoru , Ono Rei
Published: November 2022

We have developed a new method for easy self-assessment of changes in memory recall impairment, which can be used during the very early stages of dementia. An 8-picture recall and a 16-word regression were assessed, respectively, and the index was calculated by adding up the ratio of correct responses to both tests. A total of 85 subjects including 12 MCI, 8 AD, and 65 older persons with normal cognitive function were evaluated, and the correlation with the WMS-R Logical Memory II score was examined. The results showed that there was a statistically significant correlation between the 8-picture recall (R = 0.872, p < 0.0001) and the index (R = 0.857, p < 0.0001), respectively, with the Logical Memory score. We have named this index as Self Assessment Memory Scale (SAMS), and are now developing a digital tool to enable easy and self-administered evaluation of recall.
Using deep learning to study emotional behavior in rodent models
Authors: Kuo Jessica Y. , Denman Alexander J. , Beacher Nicholas J. , Glanzberg Joseph T. , Zhang Yan , Li Yun , Lin Da-Ting
Published: November 2022

Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.
Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients
Authors: de Seta Valeria , Toppi Jlenia , Colamarino Emma , Molle Rita , Castellani Filippo , Cincotti Febo , Mattia Donatella , Pichiorri Floriana
Published: November 2022

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.
GLIM in diagnosing malnutrition and predicting outcome in ambulatory patients with head and neck cancer
Authors: Orell Helena Kristiina , Pohju Anne Katariina , Osterlund Pia , Schwab Ursula Sonja , Ravasco Paula , Mäkitie Antti
Published: November 2022

Aim

This study aimed to determine the prevalence of malnutrition in a head and neck cancer (HNC) population according to the Global Leadership Initiative on Malnutrition (GLIM) criteria and to assess its relation to survival. The secondary aim was to compare GLIM criteria to Patient–Generated Subjective Global Assessment (PG–SGA) and Nutritional Risk Screening 2002 (NRS 2002) methods.

Methods

The assessment was performed in a series of 65 curative patients with newly diagnosed HNC in a nutrition intervention study. Malnutrition was defined as PG-SGA classes BC and nutritional risk as NRS 2002 score ≥3 and was retrospectively diagnosed with GLIM criteria in prospectively collected data at diagnosis. Sensitivity, specificity, and kappa (κ) were analyzed. Predictive accuracy was assessed by calculating the area under curve (AUC) b y receiver operating characteristic (ROC) analysis. Kaplan–Meier and Cox regression analyses were used to evaluate association between malnutrition and overall survival (OS), and disease-free survival (DFS).

Results

GLIM-defined malnutrition was present in 37% (24/65) of patients. The GLIM showed 77% sensitivity and 84% specificity with agreement of κ = 0.60 and accuracy of AUC = 0.80 (p < 0.001) with PG-SGA and slightly higher sensitivity (83%) with NRS 2002 (κ = 0.58). Patients with GLIM-defined malnutrition had shorter OS (56 vs. 72 months, HR 2.26, 95% CI 1.07–4.77, p = 0.034) and DFS (37 vs. 66 months, HR 2.01, 95% CI 0.99–4.09, p = 0.054), than well-nourished patients. The adjusted HR was 2.53 (95% CI 1.14–5.47, p = 0.023) for OS and 2.10 (95% CI 0.98–4.48, p = 0.056) for DFS in patients with GLIM-defined malnutrition.

Conclusion

A substantial proportion of HNC patients were diagnosed with malnutrition according to the GLIM criteria and this showed a moderate agreement with NRS 2002- and PG–SGA-defined malnutrition. Even though the GLIM criteria had strong association with OS, its diagnostic value was poor. Therefore, the GLIM criteria seem potential for malnutrition diagnostics and outcome prediction in the HNC patient population. Furthermore, NRS 2002 score ≥3 indicates high nutritional risk in this patient group.
Case report: Double L611S/V617L JAK2 mutation in a patient with polycythemia vera originally diagnosed with essential thrombocythemia
Authors: Li Xiao-qing , Liu Jia-jun , Liu Cheng-cheng
Published: November 2022

Double JAK2 mutations have rarely been described in myeloproliferative neoplasms (MPNs) and are demonstrated to be associated with the polycythemia vera (PV) phenotype. Here, we first report a case of a PV patient with a de novo double L611S/V617L in cis mutation of JAK2. A 40-year-old woman was admitted to the hospital with massive splenomegaly, multiple splenic infarcts, and abdominal pain. She had a 4-year history of erythrocytosis with an antecedent 10-year history of thrombocytosis before coming to our hospital. She was diagnosed with JAK2 L611S/V617L double-mutant PV after a detailed medical examination in 2017. According to the literature, IFNα therapy can induce clinical, hematological, histopathological, and occasionally molecular remission in individuals with MPNs. Our report demonstrates that combination therapy with ruxolitinib and IFNα can lead to a substantial reduction in JAK2 L611S/V617L double-mutant allele burden.
Expectant mothers and fathers' knowledge of nurturing care in a developing country
Authors: Soysal Cimen Hafize , Karaaslan Bedriye Tugba
Published: November 2022

Background

The Nurturing Care Framework (NCF), which has been emphasized by the World Health Organization (WHO) in recent years and is among the United Nations Sustainable Development Goals (SDGs), expresses the conditions created to promote early childhood development (ECD). These conditions aim to provide opportunities for children in the Good Health, Adequate Nutrition, Responsive Caregiving, Security and Safety, and Opportunities for Early Learning (5 Components) by caregivers, primarily from low and middle-income countries (LMICs). The aim of this study is to examine the knowledge and awareness of expectant mothers and fathers in Turkey, about the NCF.

Methods

In this study, a cross-sectional, analytical research design was used. Nurturing Care (NC) knowledge and awareness levels of expectant mothers and fathers in the 2nd and 3rd trimesters of pregnancy were determined via the online form of “The Knowledge of Nurturing Care Inventory (KNCI)” prepared by the researchers. This form consists of 60 questions describing the 5 components mentioned above. The sample contains a total of 103 people, 91 (88.3%) women, and 12 (11.7%) men.

Results

Among the findings, the highest mean of knowledge level (13.76 ± 1.22) was found in Responsive Caregiving, and the lowest mean of knowledge level (4.35 ± 1.83) was found in Opportunities for Early Learning. The general level of knowledge on NC was found to vary statistically significantly depending on gender in favor of the women [t(101) = 3.27, p < .05], depending on educational status in favor of those with university and graduate education [F(2, 100) = 3.481, p < .05], depending on participation in pregnancy school training in favor of those who attended [t(101) = 2.349, p < .05], and depending on knowledge on how to support development in favor of those who know how to support [t(101) = 2.370, p < .05].

Conclusions

As a result, expectant parents need information and support about the period when children can acquire the basic developmental milestones and about the developmental risk indicators. More research is needed to implement the NCF starting from the preconception period, especially in LMICs, so that the opportunities in early childhood (which is the time period when the brain architecture is shaped, and development is the fastest) are not missed.
Gypenosides ameliorate ductular reaction and liver fibrosis via inhibition of hedgehog signaling
Authors: Hu Yonghong , He Xiaoli , Zhou Xiaoxi , Liang Yue , Fu Yadong , Zhang Linzhang , Fang Jing , Liu Wei , Chen Gaofeng , Mu Yongping , Zhang Hua , Cai Hong , Liu Chenghai , Liu Ping , Chen Jiamei
Published: November 2022

Backgroud and aims: Ductular reaction (DR) is a common pathological change and thought to have a key role in the pathogenesis and progression of liver fibrosis. Our previous study reported Gypenosides (GPs) ameliorated liver fibrosis, however, the anti-fibrotic mechanisms of GPs are still unclear.

Methods: Liver fibrosis was induced in rats by carbon tetrachloride combining with 2-acerylaminofluorene (CCl4/2-AAF), and Mdr2 knockout (Mdr2−/−) mice to evaluate the anti-fibrotic role of GPs. In vitro, WB-F344 cells, a hepatic progenitor cells (HPCs) line, with or without Gli1 overexpressing lentiviral vectors, were induced by sodium butyrate (SB) to validate the mechanism of GPs and NPLC0393, the main ingredient of GPs.

Results: Both in CCl4/2-AAF-treated rats and Mdr2−/− mice, GPs obviously reduced the deposition of collagen and hydroxyproline content, inhibited the activation of hepatic stellate cells and inflammatory cell infiltration. Notably, GPs reduced the expressions of Epcam, CK19, CK7, Dhh, Smo, Ptch2, Gli1 and Gli2. Furthermore, CK19+ cells co-expressed Gli1, while the number of CK19+/Gli1+ cells was decreased by GPs. In vitro, GPs and NPLC0393 inhibited the differentiation of WB-F344 cells toward a biliary phenotype. Mechanistically, GPs and NPLC0393 protected against DR by inhibiting hedgehog signaling, which was supported by the results that DR, triggered directly by Gli1 overexpressing lentiviral vector was blocked by administration with GPs or NPLC0393.

Conclusion: GPs attenuated DR and liver fibrosis by inhibiting hedgehog signaling, which provided more evidences and a novel mechanism of anti-fibrotic effect of GPs.
UV-B induces the expression of flavonoid biosynthetic pathways in blueberry (Vaccinium corymbosum) calli
Authors: Song Yan , Ma Bin , Guo Qingxun , Zhou Lianxia , Lv Changyi , Liu Xiaoming , Wang Jianlei , Zhou Xintong , Zhang Chunyu
Published: November 2022

Ultraviolet-B (UV-B) radiation is an environmental signal that affects the accumulation of secondary metabolites in plants. In particular, UV-B promotes flavonoid biosynthesis, leading to improved fruit quality. To explore the underlying molecular mechanism, we exposed blueberry (Vaccinium corymbosum) calli to UV-B radiation and performed a transcriptome deep sequencing (RNA-seq) analysis to identify differentially expressed genes (DEGs). We detected 16,899 DEGs among different treatments, with the largest number seen after 24 h of UV-B exposure relative to controls. Functional annotation and enrichment analysis showed a significant enrichment for DEGs in pathways related to plant hormone signal transduction and phenylpropanoid and flavonoid biosynthesis. In agreement with the transcriptome data, flavonol, anthocyanin and proanthocyanidin accumulated upon UV-B radiation, and most DEGs mapping to the phenylpropanoid and flavonoid biosynthetic pathways using the KEGG mapper tool were upregulated under UV-B radiation. We also performed a weighted gene co-expression network analysis (WGCNA) to explore the relationship among genes involved in plant hormone signal transduction, encoding transcription factors or participating in flavonoid biosynthesis. The transcription factors VcMYBPA1, MYBPA2.1, MYB114, MYBA2, MYBF, and MYB102 are likely activators, whereas MYB20, VcMYB14, MYB44, and VcMYB4a are inhibitors of the flavonoid biosynthetic pathway, as evidenced by the direction of correlation between the expression of these MYBs and flavonoid biosynthesis-related genes. The transcription factors bHLH74 and bHLH25 might interact with MYB repressors or directly inhibited the expression of flavonoid biosynthetic genes to control flavonoid accumulation. We also observed the downregulation of several genes belonging to the auxin, gibberellin and brassinosteroid biosynthetic pathways, suggesting that MYB inhibitors or activators are directly or indirectly regulated to promote flavonoid biosynthesis under UV-B radiation.
Analysis of influencing factors of subjective career unsuccessfulness of vocational college graduates from the Department of Navigation in China
Authors: Wang Li , Ye Jian-Hong , Lee Yi-Sang , Miao Cong-Jin
Published: November 2022

The marine talent cultivation and output in higher vocational colleges is an important support to build a strong maritime country and ensure the steady development of the shipping business industry. Vocational colleges should ensure effective career preparation and career guidance education for their students, and train them to acquire the professional abilities to work and adapt effectively and quickly in the future. Some studies show that many crew members experience a decline in job satisfaction, a low sense of achievement, and a series of negative subjective career feelings. Even more, some crew members have poor work performance or unsuccessful career development behaviors such as career change or resignation. This study examined the causes and influencing factors of these circumstances and might be a reference for schools to strengthen the quality of their training programs in the future. In-depth interviews with 12 vocational marine navigation college graduates were conducted, and grounded theory was used to code and analyze the collected interview information. Four types of influencing factors were identified: adaptation preparation, career adaptability, adaptation action, and the situational factor. The adaptation preparation factor could be categorized into psychology, determination, lack of certificates, and simple work; the career adaptability factor could be categorized into boring work, busyness, a big gap between expectations and reality, danger, promotion difficulty, poor interpersonal relationships, high pressure, and maladaptation to the surroundings; the adaptation action factor could be categorized into passive fatigue, lack of training, competition, confidence, and planning; while the situational factor could be categorized into the impact of the epidemic, low social status, lack of support from family, and advantages of living on land. To help the marine navigation students better adapt to their jobs and avoid subjective career unsuccessfulness, higher vocational colleges need to strengthen students’ mental health education, consolidate knowledge and skills training, encourage students to obtain more vocational competency certificates, enhance their interpersonal communication skills, do effective career planning, pay attention to physical exercise and safety awareness training, and adopt other career management and counseling suggestions.
Spatial metaphors and the design of everyday things
Authors: Pitt Benjamin , Casasanto Daniel
Published: November 2022

People use space (e.g., left–right, up-down) to think about a variety of non-spatial concepts like time, number, similarity, and emotional valence. These spatial metaphors can be used to inform the design of user interfaces, which visualize many of these concepts in space. Traditionally, researchers have relied on patterns in language to discover habits of metaphorical thinking. However, advances in cognitive science have revealed that many spatial metaphors remain unspoken, shaping people’s preferences, memories, and actions independent of language – and even in contradiction to language. Here we argue that cognitive science can impact our everyday lives by informing the design of physical and digital objectsviathe spatial metaphors in people’s minds. We propose a simple principle for predicting which spatial metaphors organize people’s non-spatial concepts based on the structure of their linguistic, cultural, and bodily experiences. By leveraging the latent metaphorical structure of people’s minds, we can design objects and interfaces that help people think.
Social support criteria in vascularized composite allotransplantation versus solid organ transplantation: Should the same ethical considerations apply?
Authors: Kimberly Laura L. , Onuh Ogechukwu C. , Thys Erika , Rodriguez Eduardo D.
Published: November 2022

The field of vascularized composite allotransplantation (VCA) is evolving, with some procedures poised to transition from highly experimental research toward standard of care. At present, the use of social support as an eligibility criterion for VCA candidacy is at the discretion of individual VCA programs, allowing VCA teams to consider the unique needs of each potential candidate. Yet this flexibility also creates potential for bias during the evaluation process which may disproportionately impact members of certain communities where social configurations may not resemble the model considered “optimal.” We examine the extent to which ethical considerations for social support in solid organ transplantation (SOT) may be applied to or adapted for VCA, and the ethically meaningful ways in which VCA procedures differ from SOT. We conclude that VCA programs must retain some flexibility in determining criteria for candidacy at present; however, considerations of equity will become more pressing as VCA procedures evolve toward standard of care, and further empirical evidence will be needed to demonstrate the association between social support and post-operative success. The field of VCA has an opportunity to proactively address considerations of equity and justice and incorporate fair, inclusive practices into this innovative area of transplantation.
Bibliometric assessment of world scholars’ international publications related to conceptual metaphor
Authors: Han Ying , Peng Zhibin , Chen Hong
Published: November 2022

Research on metaphor has gained increasing attention of world’s scholars since the publication of Lakoff and Johnson’s collaborated bookMetaphors We Live Byin 1980. The present study comprises a pioneering review of publications on Conceptual Metaphor Theory (CMT). It aimed to use the CiteSpace software to provide a clear overview of international research in relation to CMT. In total, 4,458 bibliometric recordings ranging from 1980 to 2022 were collected from the Web of Science (WOS) Core Collection. The descriptive analysis presents the trend of annual publications, the top 10 most prolific journals and the top 10 most productive authors. A document co-citation analysis was conducted via CiteSpace to navigate the key documents in this field. A visualization of keywords and its cluster analysis were conducted to show the research fields and dominant topics. The top 5 keywords with high frequency were language, comprehension, conceptual metaphor, discourse, and figurative language. The most prominent 5 clusters are labeled as right hemisphere, self, time, teacher education, and corpus linguistics. The present review through CiteSpace flags the need for more investigations of CMT from more aspects or interdisciplinary studies, such as metaphor translation, metaphor in literature, metaphor and corpus linguistics, etc.
Organizational democracy and meaningful work: The mediating role of employees corporate social responsibility perceptions
Authors: Svendsen Mari , Jønsson Thomas Faurholt
Published: November 2022

Meaningful work is an important field of research, relating to both organizational outcomes and employee welfare. Organizational democracy has been theoretically proposed as an important antecedent to meaningful work. Nevertheless, this relationship is yet to be empirically explored. Thus, the objective of the current research is to explore the relationship between organizational democracy and meaningful work. We used structural equation modeling with self-reported, cross-sectional data from different nations and industries to test a mediation model in which corporate social responsibility (CSR) perceptions mediate the positive relationship between organizational democracy and meaningful work. Our findings confirmed that CSR perceptions partially mediate in the relationship between organizational democracy and meaningful work. Thus, based on our findings we can conclude that organizational democracy can play a direct role in increasing the experience of meaningful work, but also an indirect role trough the employees experience of CSR. Our findings have theoretical implications by adding to the classical theoretical literature that connect organizational democracy and meaningful work, and by disentangling the role of CSR perceptions in this relationship. Moreover, our findings have practical implications as our results give important knowledge to managers and organizational stakeholders that wish to increase the experience of meaningful work in organizations.
Association between type 1 diabetes and neurodevelopmental disorders in children and adolescents: A systematic review and meta-analysis
Authors: Xie Xue-Ni , Lei Xue , Xiao Chun-Ye , Li Ya-Min , Lei Xian-Yang
Published: November 2022

Type 1 diabetes and neurodevelopmental disorders are common chronic conditions in childhood and adolescence, and having one may lead to an increased chance of developing the other. Type 1 diabetes mellitus is mainly manifested by elevated blood glucose, while neurodevelopmental diseases are composed of a variety of diseases, which are relatively complex. The purpose of this meta-analysis was to find out the prevalence of type 1 diabetes-related neurodevelopmental disorders in children and adolescents and to explore the potential association between neurodevelopmental disorders and type 1 diabetes. PubMed, Embase and Web of science databases were searched from the inception to May 22, 2022 to identify relevant studies, Finally, 24 original studies were included in the meta-analysis. Prevalence estimates for neurodevelopmental disorders in the type 1 diabetes adolescent and their 95% confidence intervals were pooled using random effects models. The pooled estimates for autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD) in the type 1 diabetes population were 1.2 and 5.3%, respectively, both of which are higher than the 2019 global prevalence of ASD and ADHD in the general population. The results of the subgroup analysis showed that the prevalence of ASD and ADHD in the T1DM population tended to increase with age. In conclusion, there may be a potential link between the occurrence of type 1 diabetes mellitus and the development of neurodevelopmental disorders in children and adolescents, but more relevant studies are needed to understand the link between the underlying pathogenesis of type 1 diabetes and neurodevelopmental disorders.

Systematic review registration

[https://www.crd.york.ac.uk/PROSPERO/], identifier [CDR42022333443].
Empirical evidence on structural racism as a driver of racial inequities in COVID-19 mortality
Authors: Brown Tyson H. , Kamis Christina , Homan Patricia
Published: November 2022

Objective

This study contributes to the literature by empirically testing the extent to which place-based structural racism is a driver of state-level racial inequalities in COVID-19 mortality using theoretically-informed, innovative approaches.

Methods

CDC data are used to measure cumulative COVID-19 death rates between January 2020 and August 2022. The outcome measure is a state-level Black-White (B/W) ratio of age-adjusted death rates. We use state-level 2019 administrative data on previously validated indicators of structural racism spanning educational, economic, political, criminal-legal and housing to identify a novel, multi-sectoral latent measure of structural racism (CFI = 0.982, TLI = 0.968, and RMSEA = 0.044). We map B/W inequalities in COVID-19 mortality as well as the latent measure of structural racism in order to understand their geographic distribution across U.S. states. Finally, we use regression analyses to estimate the extent to which structural racism contributes to Black-White inequalities in COVID-19 mortality, net of potential confounders.

Results

Results reveal substantial state-level variation in the B/W ratio of COVID-19 death rates and structural racism. Notably, regression estimates indicate that the relationship between the structural racism and B/W inequality in COVID-19 mortality is positive and statistically significant (p < 0.001), both in the bivariate model (adjusted R2 = 0.37) and net of the covariates (adjusted R2 = 0.54). For example, whereas states with a structural racism value 2 standard deviation below the mean have a B/W ratio of approximately 1.12, states with a structural racism value 2 standard deviation above the mean have a ratio of just above 2.0.

Discussion

Findings suggest that efficacious health equity solutions will require bold policies that dismantle structural racism across numerous societal domains.
Modeling the occupational health risk of workers caused by environmental release during the production of PC components
Authors: Cui Peng , Zhao Haifeng , Dong Zhiyu , Ju Xuan , Zou Ping , Zhou Siyu
Published: November 2022

Prefabricated construction is one of the solutions to the problem of balancing environmental improvements with the new buildings in the construction industry. Some work originally done on site is transferred to the front end, and the occupational health risks to industrial workers during the production of prefabricate concrete components are thus aggravated. This study aims to propose a framework to simulate the occupational health risks of workers in prefabricate concrete component plants from the perspective of risk identification, risk assessment, and risk control. Through the following 4 steps, including environmental release monitoring, diffusion and human inhalation mechanism analysis, occupational health risk evaluation, and full-path health risk simulation, this study maps physical entities to virtual reality. The proposed method tends to address the root causes behind occupational health risks, such as the lack of measurement, assessment and prevention criteria, and providing new ideas for theoretical research and innovative practice of HSE management and risk management in the construction industry.
A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19
Authors: Li Zhuo , Xu Ruiqing , Shen Yifei , Cao Jiannong , Wang Ben , Zhang Ying , Li Shikang
Published: November 2022

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.
Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes
Authors: van der Sande Kiera , Flyer Natasha , Berger Thomas E. , Gagnon Riana
Published: November 2022

Supervised Machine Learning (ML) models for solar flare prediction rely on accurate labels for a given input data set, commonly obtained from the GOES/XRS X-ray flare catalog. With increasing interest in utilizing ultraviolet (UV) and extreme ultraviolet (EUV) image data as input to these models, we seek to understand if flaring activity can be defined and quantified using EUV data alone. This would allow us to move away from the GOES single pixel measurement definition of flares and use the same data we use for flare prediction for label creation. In this work, we present a Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA)-based flare catalog covering flare of GOES X-ray magnitudes C, M and X from 2010 to 2017. We use active region (AR) cutouts of full disk AIA images to match the corresponding SDO/Helioseismic and Magnetic Imager (HMI) SHARPS (Space weather HMI Active Region Patches) that have been extensively used in ML flare prediction studies, thus allowing for labeling of AR number as well as flare magnitude and timing. Flare start, peak, and end times are defined using a peak-finding algorithm on AIA time series data obtained by summing the intensity across the AIA cutouts. An extremely randomized trees (ERT) regression model is used to map SDO/AIA flare magnitudes to GOES X-ray magnitude, achieving a low-variance regression. We find an accurate overlap on 85% of M/X flares between our resulting AIA catalog and the GOES flare catalog. However, we also discover a number of large flares unrecorded or mislabeled in the GOES catalog.
Taxonomy of Scheduling Problems with Learning and Deterioration Effects
Authors: Paredes-Astudillo Yenny Alexandra , Montoya-Torres Jairo R. , Botta-Genoulaz Valérie
Published: November 2022

In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.
Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
Authors: Sulemane Shazia , Matos-Carvalho João P. , Pedro Dário , Moutinho Filipe , Correia Sérgio D.
Published: November 2022

This paper focuses on the gaps that occur inside plantations; these gaps, although not having anything growing in them, still happen to be watered. This action ends up wasting tons of liters of water every year, which translates into financial and environmental losses. To avoid these losses, we suggest early detection. To this end, we analyzed the different available neural networks available with multispectral images. This entailed training each regional and regression-based network five times with five different datasets. Networks based on two possible solutions were chosen: unmanned aerial vehicle (UAV) depletion or post-processing with external software. The results show that the best network for UAV depletion is the Tiny-YOLO (You Only Look Once) version 4-type network, and the best starting weights for Mask-RCNN were from the Tiny-YOLO network version. Although no mean average precision (mAP) of over 70% was achieved, the final trained networks managed to detect mostly gaps, including low-vegetation areas and very small gaps, which had a tendency to be overlooked during the labeling stage.
LIFRNet: A Novel Lightweight Individual Fish Recognition Method Based on Deformable Convolution and Edge Feature Learning
Authors: Yin Jianhao , Wu Junfeng , Gao Chunqi , Jiang Zhongai
Published: November 2022

With the continuous development of industrial aquaculture and artificial intelligence technology, the trend of the use of automation and intelligence in aquaculture is becoming more and more obvious, and the speed of the related technical development is becoming faster and faster. Individual fish recognition could provide key technical support for fish growth monitoring, bait feeding and density estimation, and also provide strong data support for fish precision farming. However, individual fish recognition faces significant hurdles due to the underwater environment complexity, high visual similarity of individual fish and the real-time aspect of the process. In particular, the complex and changeable underwater environment makes it extremely difficult to detect individual fish and extract biological features extraction. In view of the above problems, this paper proposes an individual fish recognition method based on lightweight convolutional neural network (LIFRNet). This proposed method could extract the visual features of underwater moving fish accurately and efficiently and give each fish unique identity recognition information. The method proposed in this paper consists of three parts: the underwater fish detection module, underwater individual fish recognition module and result visualization module. In order to improve the accuracy and real-time availability of recognition, this paper proposes a lightweight backbone network for fish visual feature extraction. This research constructed a dataset for individual fish recognition (DlouFish), and the fish in dataset were manually sorted and labeled. The dataset contains 6950 picture information instances of 384 individual fish. In this research, simulation experiments were carried out on the DlouFish dataset. Compared with YOLOV4-Tiny and YOLOV4, the accuracy of the proposed method in fish detection was increased by 5.12% and 3.65%, respectively. Additionally, the accuracy of individual fish recognition reached 97.8%.
Essential Role of Forest Fires in Humic Acids Structure and Composition Alteration
Authors: Chebykina Ekaterina , Abakumov Evgeny
Published: November 2022

Pyrogenic components of soil organic matter are practically not taken into account when assessing the implications of global climate changes on the SOM sequestration rate and its stability. In this regard, both the amount and structural stability of SOM were investigated for postpyrogenic forest–steppe environments. Mature unaffected by fire Psamment Entisols were investigated in comparison with same soil strongly affected by surface and crown fires in 2010 in Tolyatti city. This article discusses postpyrogenic succession when comparing the results of field works in 2010 with 2020. The elemental composition of humic acids as well as 13C NMR spectra were investigated for the studied humic acids. Moreover, for the first time ever, integrated indicators of HAs’ molecular composition were presented for postpyrogenic soils. The data obtained showed that there are significant changes in the structural organization of organic matter as a result of forest fires. A simultaneous decrease in the H/C and O/C atomic ratios was identified as a result of fire, which indicates a slight loss of oxygen-containing functional groups, while the aromaticity degree of molecules increases. An increase in the content of oxygen-containing groups in the HA molecules was shown for 10 years after the fires, which was accompanied by the oxidation of the HA molecule with the same increase in the aromaticity degree of the HA molecule. The HA molecules of 2020 turned out to be more resistant to environmental factors compared to analogues in 2010. As a result of fires, the aromaticity degree of HAs molecules significantly increases.
The Usefulness of Serum Brain Damage Biomarkers in Detection and Evaluation of Hypoxic Ischemic Encephalopathy in Calves with Perinatal Asphyxia
Authors: Ok Mahmut , Naseri Amir , Ates Mehmet Burak , Ider Merve , Uney Kamil , Sevinc Mutlu , Hatipoglu Fatih , Yildiz Ramazan , Erturk Alper , Baspinar Nuri , Iyigun Suleyman Serhat
Published: November 2022

The purpose of the present study was to determine hypoxic brain damage in calves with perinatal asphyxia using brain-specific damage biomarkers. Ten healthy and 25 calves with perinatal asphyxia were enrolled in the study. Clinical examination, neurological status score, and laboratory analysis were performed at admission, 24, 48, and 72 h. Serum concentrations of ubiquitin carboxy-terminal hydrolysis 1 (UCHL1), calcium-binding protein B (S100B), adrenomodullin (ADM), activitin A (ACTA), neuron-specific enolase (NSE), glial fibrillary acidic protein (GFAP) and creatine kinase-brain (CK-B) were measured. Histopathological and immunohistochemical examinations of the brain tissue were performed in 13 nonsurvivor calves. The neurological status score of the calves with asphyxia was significantly (p < 0.05) lower. Mix metabolic-respiratory acidosis and hypoxemia were detected in calves with asphyxia. Serum UCHL1 and S100B were significantly (p < 0.05) increased, and NSE, ACTA, ADM, and CK-B were decreased (p < 0.05) in calves with asphyxia. Histopathological and immunohistochemical examinations confirmed the development of mild to severe hypoxic-ischemic encephalopathy. In conclusion, asphyxia and hypoxemia caused hypoxic-ischemic encephalopathy in perinatal calves. UCHL1 and S100B concentrations were found to be useful markers for the determination of hypoxic-ischemic encephalopathy in calves with perinatal asphyxia. Neurological status scores and some blood gas parameters were helpful in mortality prediction.