bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust Resource Targeting in Continuous and Batch Process
Authors: Kumawat Piyush Kumar , Chaturvedi Nitin Dutt 
Published: March 2021

Abstract
Water is considered a significant resource in process industries. It is essential for planners to target and optimize the use of water as an external resource for industrial operations. Such optimization problems account for uncertainties related to internal resources and must be handled to provide solutions for real plants of industrial relevance. In this paper, these parametric uncertainties are addressed, while targeting resources for continuous and flexible schedule batch process. The proposed robust counterpart formulations include resource minimization constraints for continuous and batch processes to satisfy the demand. Three different robust optimization methodologies are adapted and extended to handle parametric uncertainties associated with internal resources. Assuming bounded and known uncertainty, the resultant formulations are then implemented to literature examples and the results are compared with the deterministic formulation. The results show that the formulation proposed by Bertsimas and Sim is the most appropriate model for the defined problem because it preserves the linearity and provides a mechanism to control the degree of conservatism, guaranteeing feasibility. This model will assist the planner to decide the resource requirement under uncertain conditions. Thus, immune the process against uncertainties to satisfy demands.
Identifying the Presence and Severity of Dementia by Applying Interpretable Machine Learning Techniques on Structured Clinical Records
Authors: Vyas Akhilesh , Aisopos Fotis , Vidal Maria-Esther , Garrard Peter , Paliouras George 
Published: March 2021

Abstract
Background: Dementia occurs through declining of cognitive abilities, and its early detection stands essential for effective preventive measures. However, mainstream diagnostic tests and screening tools, such as Cambridge Cognition Examination (CAMCOG) and Mini-Mental State Examination (MMSE), often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ electronic medical records, they represent promising options to identify the presence and severity of dementia more precisely. Methods: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation method where a dementia expert is in the loop to ensure meaningful curation decisions. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the detection of dementia. Results: Our experiment results prove that clinical record features (such as age or comorbidities), along with baseline arithmetic or memory tests, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.85 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various clinical features and cognitive tests from the past episodes of the elderly population. Moreover, a set of rules represent building blocks in the efficiently patient classification. Relevant clinical and screening test features (e.g., simple arithmetic or animal fluency task) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. They identify not only meaningful features but also justifications of classifications. As a result, the predictive power of machine learning models over curated clinical data is demonstrated, paving the way for accurate diagnosis of dementia.
A large-scale population-based epidemiological study on the prevalence of central sensitization syndromes in Japan
Authors: Haruyama Yasuo , Sairenchi Toshimi , Uchiyama Koji , Suzuki Keisuke , Hirata Koichi , Kobashi Gen 
Published: March 2021

Abstract
To investigate the prevalence of CSS in a general population. A large-scale cross-sectional study on 21,661 residents (7,531 men, 14,130 women) in a community in Japan was performed. CSS were assessed using Central Sensitization Inventory (CSI-A, B). Participants with a CSI-A score ≥40 was defined as having CSS. Age, sex, district, CSI-B (10 CSS-related diseases), lifestyle, and mental health factors were rated in a self-reported survey. The prevalence of CSS and its relationship with potential factors were examined by sex using descriptive and logistic regression models. The prevalence of CSS was 5.4% (95% Confidence Interval (CI) 5.1 to 5.7) in all participants. It significantly differed between men (3.6%, 95% CI 3.2 to 4.1) and women (6.3%, 95% CI 5.6 to 6.7). A CSI-A score ≥40 correlated with more than one CSI-B disease, exercise habits, sleeping hours per day, perceived stress, and resilience in both men and women. In women, age, ex-smokers, coffee intake (≥1 day per week), and alcohol intake correlated with CSS. The prevalence of CSS was 5.4% in the community examined and was higher in women than in men. CSS-related diseases and many factors correlated with CSS. The present results provide important information for future research on CSS.
Connexin32 promotes autophagy of cervical cancer to resistance to chemotherapy-induced apoptosis via AMPK pathway
Authors: fan lixia , Tao Liang , Lai Yongchang , Cai Shaoyi , Zhao Ziyu , Yang Feng , Su Riya , Wang Qin 
Published: March 2021

Abstract
BackgroundThe roles of gap junction and its components, connexins in autophagy of cervical cancer cells are rarely investigated. Our previous study demonstrated that connexin32 (Cx32) exerted an anti-apoptotic effect on cervical cancer (CaCx). However, as an important regulator of apoptosis, whether the autophagy is involved in the function of Cx32 on cervical cancer cells are not well defined.MethodsThe expression of Connexin32 and the autophagy-associated protein LC3-Ⅱ in paracancerous cervical tissues (n = 30) and cervical cancer (n = 50) tissues were determined by Western blotting. Forty-five cervical cancer specimens were used to evaluate the clinical relevance of Connexin32 and LC3-Ⅱ. The effect of Connexin32 on autophagy was examined by detecting the change of LC3-Ⅱ using Western blotting, transfection with enhanced green fluorescent protein-LC3 plasmid and transmission electron microscopy analysis. The mechanism of Connexin32-mediated autophagy was assessed by Western blotting and flow cytometry.ResultsBoth Connexin32 and LC3-Ⅱ are upregulated in cervical cancer tissues compared to those in paracancerous cervical tissues. Overexpression of Connexin32 significantly enhanced autophagy in HeLa-Connexin32 cells, whereas knockdown of Connexin32 suppressed autophagy in C-33A cells. Connexin32 inhibited apoptosis of cervical cancer cells by promoting autophagy. Moreover, Connexin32 triggered autophagy by activation of the AMP-activated protein kinase signalling, which was gap junction-independent.ConclusionConnexin32 exerts its anti-apoptotic effect by activating autophagy through the AMPK pathway in cervical cancer. This finding suggests that Connexin32 is a potential biomarker and a new therapeutic target for cervical cancer.Trial registrationThe study was approved by the Research Committee of Ethics of the Affiliated Cancer Hospital of Xinjiang Medical University (K-201354).
CPXM1: a novel biomarker for gastric cancer prognosis
Authors: Du Jun , Zhu Mengxiang , Yan Wenwu , Yao Changsheng , Li Qingyi , Wang Jinguo 
Published: March 2021

Abstract
Background

The molecular role of carboxypeptidase X, M14 family member (CPXM1) in oncogenesis or tumor progression remains unclear. The aim of this study was to determine whether CPXM1 can be used as a potential prognostic biomarker for gastric cancer (GC).
Methods

We first demonstrated the relationship between CPXM1 expression and GC in various public databases. Secondly, the expression of CPXM1 in GC tissues was further verified by immunohistochemical staining using tissue microarray containing 96 cases of GC patients. Kaplan–Meier analysis and a Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CPXM1 and the survival of GC patients. Finally, we used the expression data of CPXM1 in The Cancer Genome Atlas database to predict CPXM1-related signaling pathways through bioinformatics analysis.
Results

The expression level of CPXM1 in GC tissues was significantly correlated with tumor size (p = 0.041) and lymph node metastasis (p = 0.014). In addition, Kaplan–Meier analysis showed that the expression of CPXM1 in GC tissues was significantly associated with poor prognosis (p = 0.011). Multivariate analysis indicated that CPXM1 is a potential predictor of poor prognosis in GC patients (p = 0.026). The results of biosynthesis analysis demonstrated that the data set of CPXM1 high expression was mainly enriched in cancer-related signal pathways.
Conclusion

CPXM1 is an effective biomarker for the prognosis of GC patients and may play a key role in the occurrence and progression of GC.
Risk Factors for Plasmodium Falciparum Infection in Pregnant Women in Burkina Faso: A Community-Based Malaria Cross-Sectional Survey
Authors: Yaro Jean Baptiste , Ouedraogo Alphonse , Diarra Amidou , Sombié Salif , Ouedraogo Z Amidou , Nébié Issa , Bousema Teun , Drakeley Chris , Sirima Sodiomon B , Tiono Alfred , Lindsay Steve W , Wilson Anne L 
Published: March 2021

Abstract
Background: Malaria in pregnancy remains a public health problem in sub-Saharan Africa. Identifying risk factors for malaria in pregnancy could assist in developing interventions to reduce the risk of malaria in Burkina Faso and other countries in the region.Methodology: Two cross-sectional surveys were carried out to measure Plasmodium falciparum infection using microscopy in pregnant women in Saponé Health District, central Burkina Faso. Data were collected on individual, household and environmental variables and their association with P. falciparum infection assessed using multivariate analysis. Results: A total of 356 pregnant women were enrolled in the surveys, 174 during the dry season and 182 during the wet season. The mean number of doses of sulphadoxine pyrimethamine for Intermittent Preventive Treatment in pregnancy (IPTp-SP) was 0.4 doses during the first trimester, 1.1 doses at the second and 2.3 doses at the third. Overall prevalence of P. falciparum infection by microscopy was 15.7%; 17.8% in the dry season and 13.7% in the wet season. 88.2% of pregnant women reported sleeping under an insecticide-treated net on the previous night. P. falciparum infection risk in pregnancy was reduced in those women who reported using an ITN (Odds ratio, OR=0.31, 95% CI 0.12-0.79, p=0.02) and an increasing number of IPTp-SP doses during pregnancy, with each additional dose reducing the odds by 40% (OR=0.59, 95% CI 0.43–0.81, p<0.001). ConclusionThe prevalence of P. falciparum infection among pregnant women remains high in Burkina Faso although use of IPTp-SP and ITNs were found to reduce the odds of infection. Despite this, compliance with IPTp remains far from that recommended by the National Malaria Control Programme and World Health Organization. Behaviour change communication should be improved to encourage compliance with protective malaria control tools during pregnancy.
Traditional Medicines Used as Adjuvant Therapy for COVID-19 Symptoms in Syria: An Ethno-medicine Survey
Authors: Khatib Chadi , Nattouf Abdulhakim , Agha Mohamad Isam Hasan 
Published: March 2021

Abstract
Context: The use of traditional Arabic medicine (TAM) has spread to treat various diseases in Syria since ancient time. They are cost-effective with fewer side effects and are more suitable for long-term use compared with chemically synthesized medicines.Objective: We conducted ethnobotanical and ethno-medicine research on plants traditionally used as adjuvant therapy for COVID-19 symptoms in Syria. Methods: Information was collected in the period of (September 1st, 2020 to December 21st, 2020), from Southern Region (Damascus, As Suwayda, Dar'a), Northern region (especially Aleppo), Central region (Himis, Hamah), Western coastal region (Latakia, Tartus) Eastern Region (Dayr az Zawr, Al Hasakah) in Syria. 150 informants were divided into two group one of them is pharmacists who interested in herbal remedies 73.34% (63.64% female and 36.36% male of them), and the other is herbalists 26.66% who are called "attarin" traditional healers and bee therapists. Medicinal plants being mentioned by the Informants were recorded with local names and photographed. Each reported medicinal plant species was gathered, compressed, dehydrated, and identified.Results: In this research we listed a total of 26 medicinal species relating to 15 botanical families were generally utilized by pharmacists and herbalists in the prevention and treatment of COVID 19. The calculated results of medicinal use-value MUV showed that Pimpinella anisum L. was ranked first (MUV=0.933) followed by Zingiber officinale Roscoe. (MUV=0.920), then Thymus syriacus Boiss. (MUV=0.9).Conclusion: There may be some effective Syrian traditional herbal remedies in preventing and treating COVID-19 symptoms for some people, but the lack of information on the mechanism of effect, the appropriate dosage, side effects, toxicity and drug interactions makes them questionable, as they need more research and study.
Multi-plane echocardiographic assessment of right ventricular function in adults with repaired Tetralogy of Fallot
Authors: Bowen Daniel , Berendoncks An van , McGhie Jackie , Roos-Hesselink Jolien , Bosch Annemien van den 
Published: March 2021

Abstract
Background In patients with repaired Tetralogy of Fallot (ToF), detailed assessment of right ventricular (RV) function is important for management and timing of possible pulmonary valve re-intervention. The aim of this study was to evaluate RV function using two-dimensional multi-plane echocardiography (2D MPE), a novel four-wall imaging method obtained from one apical acoustic window utilising electronic plane rotation. Methods and Results In sixty-two ToF patients (aged - 28 [22, 39] years, 65 % male), systolic function of four different RV walls (lateral, anterior, inferior and inferior coronal) were evaluated using MPE. Tricuspid annular plane systolic excursion (TAPSE), tricuspid annular peak systolic velocity (RV-S’) and RV wall longitudinal strain (RV-LS) measurements were compared with those of matched healthy individuals. 2D MPE measurements were highly feasible across the four RV walls (93.5-100% for TAPSE/S’; 66.1-95.1% for RV-LS) and could be performed more reliably than 3D RV ejection fraction (RVEF - 56.5%). All functional values were significantly reduced when compared to the control group (p<0.001). Higher RV-LS values were seen in the lateral (-17.8±4.5%) and inferior (-17.8±4.2%) walls compared to the anterior (-15.9±3.8%) and inferior coronal (-15.1±3.9%) walls. 3D RVEF correlated strongest with RV-LS values from the lateral (r -0.50; p=0.002) and anterior walls (r -0.74; p<0.001) and furthermore the four-wall average (r -0.57; p=0.001). Conclusion 2D MPE evaluation of the RV is highly feasible in ToF patients. This novel method provides new insights into regional RV wall function, enabling a more comprehensive and quantitative approach to RV assessment in daily clinical practice.
Identification of Novel Classes for Patients With Lupus Nephritis Using Two-step Cluster Model
Authors: Huang Ting , Xie Shasha , Ding Liqing , Luo Hui 
Published: March 2021

Abstract
Objectives: To identify and reclassify the patients in the LN cohort, and to further analyze the prominent clinical features and clinical significance of each cluster of patients.Methods: This is a cross-sectional study of a cohort of 635 LN patients from the Rheumatology Department of Xiangya Hospital of Central South University. Demographic data, laboratory findings and clinical evaluation system include physician’s global assessment and the SLICC/ACR Damage Index were collected. Using two-step cluster analysis, patients with similar clinical property were identified and compared.Results: Among the 635 LN patients, 599 patients (94.3%) were female. The mean age of the patients were 33.8 ± 10.4 years. Three subgroups were identified by two-step cluster analysis. Cluster 1 included 130 (20.5%) patients, Cluster 2 included 132(20.8%) patients and Cluster 3 included 373 (58.7%) patients. Cluster 3 was the largest group of mild disease activity, patients in this cluster had lower white blood cells, neutrophils, lymphocytes and mean SDI scores compared to those in the other two clusters. Cluster 1 was the smallest group of severe damage, patients in this cluster had multiple positive auto-antibodies, higher SDI scores and lower complement level. Patients of cluster 2 had the highest levels of granulocytes, but the results of other laboratory tests were roughly between the cluster 1 and cluster 3.Conclusions: This study reclassified three groups of LN patients in a large cohort. Our research shows that the multiple positive ANA antibody may be related to the high SDI score of LN patients. Clinicians can identify patients at different stages through cluster analysis to better implement prognosis.
Clinical characteristics, gestational weight gain and pregnancy outcomes in women with a history of gestational diabetes mellitus
Authors: Liang Xin , Zheng Wei , Liu Cheng , Zhang Li Rui , Zhang Li , Tian Zhi Hong , Li Guang Hui 
Published: March 2021

Abstract
Background Pregnant women with a history of gestational diabetes mellitus (GDM) are at high risk of GDM. It is unclear whether this population has pregnancy characteristics different from the general population. Whether these features affect the perinatal outcome has not yet been elucidated. Methods A retrospective study was conducted, including baseline characteristics, laboratory data, gestational weight gain (GWG), and pregnancy outcomes of 441 pregnant women with prior GDM. Besides, 1637 women without a history of GDM treated in the same period were randomly selected as the control group. The above indicators of the two groups were compared. Multivariable logistic regression analysis was performed to investigate how GWG was associated with perinatal outcomes for previous GDM women. Results Among women with GDM history, triglycerides (TG) and fasting plasma glucose (FPG) in the 1 st trimester were higher than those without GDM history. GWG was lower in women with prior GDM relative to the control group at various pregnancy stages. However, women with GDM history had a higher risk of developing GDM (OR 3.25, 95% CI 2.26–4.68) and pregnancy-induced hypertension (OR 1.50, 95% CI 1.05–2.45). In women with previous GDM, excessive GWG before OGTT exhibited a positive correlation with pregnancy-induced hypertension (OR 1.47, 95% CI 1.05–3.32), while inadequate GWG was not a protective factor for GDM and pregnancy-induced hypertension. Conclusion Women with prior GDM have glucose and lipid metabolism disorders in the 1st trimester. Limited reduction of GWG before OGTT was insufficient to offset the adverse effects of glucose and lipid metabolism disorders in women with previous GDM. Relevant interventions may be required at early stage or even before pregnancy.
Ecological niche complexity of invasive and native cryptic species of the Bemisia tabaci species complex in China
Authors: Xue Yantao , Lin Congtian , Wang Yaozhuo , Zhang Yibo , Ji Liqiang 
Published: March 2021

Abstract
Bemisia tabaci is an important pest of agricultural and horticultural crops worldwide and comprises a complex of cryptic species. In China, the introduction of the two invasive cryptic species, Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED), has considerably affected the ecological niche of the native cryptic species. Based on occurrence records obtained through field surveys and high-resolution environmental data, ecological niche models were established to predict the distribution of invasive and native cryptic species of B. tabaci in China using the MaxEnt model. The models showed that the suitable range, the important environmental variables affecting the distribution of the cryptic species, and the adaptation range of the main environmental variables related to them were different. The findings of this study showed that the B. tabaci species complex possesses a complex ecological niche. The distribution range and niche breadth of the invasive cryptic species exceeds that of the native cryptic species in the order of MED > MEAM1 > China1 > Asia1. There are different degrees of niche overlap and range overlap among the different cryptic species. This study, therefore, revealed the differences in the distribution and ecological niche of the invasive and native cryptic species of B. tabaci in China. The findings of this study contribute to the ecological knowledge of B. tabaci, which will be useful in the development of prevention and control strategies for this pest in China.
The Puzzle of Urban Density And Energy Consumption
Authors: Waśniewski Krzysztof 
Published: March 2021

Abstract
This article studies the broad socio-economic context of the otherwise known, yet intriguing a phenomenon of growing difference, in terms of density of population, between cities and the countryside. Empirical data collected in the here-presented research captures national idiosyncrasies covered by that global trend. A relatively novel empirical methodology is used to study that data, combining classical multiple regression with a heuristic transformation of data, as a Markov chain of states, with the use of an artificial neural network. That complex method is used for exploring and verifying the hypothesis that collective intelligence of human societies optimizes social interactions in the view of maximizing the absorption of energy from the environment. Both regression and artificial neural network substantiate the hypothesis, with probably more explanatory power to be found in the latter. Growing urban density seems to be instrumental, as type of social change, rather than a desired social outcome in itself.JEL codes: R11; R12; R14; R15
Two-axis scanning LIDAR using a liquid crystal control
Authors: Nishiwaki Seiji 
Published: March 2021

Abstract
Sophisticated non-mechanical technology for LIDARs is needed to realize safe autonomous cars. We have confirmed the operating principle of a non-mechanical LIDAR by combining concentric circular-grating couplers (CGCs) with a coaxially aligned rod lens. Laser light incident vertically on the center of the inner CGC along the center axis of the lens is radiated from the outer CGC and passes through the side surface of the lens. It is converted to a parallel beam that scans in two axes by applying voltages to two area-segmented electrode layers sandwiching the CGCs and a liquid crystal layer formed on the CGCs. We have demonstrated scanning whose motion ranges were 360 degrees horizontally and 10 degrees vertically. A beam with a spread angle of 0.3 ⨯ 0.8 degrees at a minimum swept vertically up to a frequency of 100 Hz and ten equally spaced beams scanned rotationally with a 6-degree cycle variation of spread of between 0.8 and 3.5 degrees.